The Curious Engineer

All blogs / AI / ML

The Practical Quant

practicalquant.blogspot.com

AI / MLTech news / industry

’10’15’20’25
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1,296 from 18 Sep 2008 to 29 Oct 2026
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weekly (115 in the last year)
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personal blog
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2026 95

  1. When AI Writes the Code, Who Proves It Works?
  2. I changed my mind about world models
  3. Let Domain Experts Build the Agents
  4. What I learned from companies training their own models
  5. AI Is Coming for Tasks Before It Comes for Jobs
  6. The local AI question I had backwards
  7. Why Build Your Own AI Model When Frontier Models Already Exist?
  8. I keep thinking about these 17,600 attempts
  9. Why AI Makes Games Worse—and How to Make Them Better
  10. Something changed in how AI gets fed
  11. What Comes After Transformers?
  12. why your data stack is about to get less forgiving
  13. Inside OpenAI’s First Chip
  14. so Google just paid $10 million for an airline's Teams messages
  15. Navier-Stokes and AI generated work: If You Can't Explain It, Did You Really Do It?
  16. The Missing Data for Spatial Intelligence
  17. Your AI model is a rental (but the loop is an asset)
  18. Your AI Agent Is Costing You More Than You Think
  19. if everyone rents the same intelligence, what's left?
  20. Reasoning Doesn’t Start With Language
  21. I keep hearing the same advice about agents
  22. An Agent Is Just an LLM in a For-Loop
  23. The model may not be your biggest risk
  24. The Bloomberg Terminal for AI Compute
  25. Why do our AI models stop learning the second we deploy them?
  26. Can China Do to Robotaxis What It Did to Solar?
  27. Why Video Is AI's Next Great Frontier
  28. What Workday, OpenAI, and a German court have in common
  29. Your AI Safety Tests Are Lying to You
  30. The Big AI Labs Are Suddenly Competing with Your Own Data
  31. Why Enterprise AI Is Moving Slower Than You Think
  32. we need to talk about where AI spend is actually going
  33. Why Observability May Be AI’s Next Frontier
  34. 25+ startups all solving the same missing piece
  35. AI Is Producing More Code, but Is It Producing More Value?
  36. Stop Renting Generic Intelligence for Your Business
  37. What happens when your agent can touch money
  38. The Data Layer Enterprise AI Has Been Missing
  39. I talked to Google's former AI head about messy data
  40. The Hidden Failure Modes of AI Agents
  41. What your base model doesn't protect you from
  42. The Data Stack Wasn’t Built for AI — Here’s What Comes Next
  43. Your AI bill is a tax on scale
  44. The SaaSpocalypse Is Coming — But Don't Count Out the Incumbents
  45. AI Agents Are Implemented, Not Adopted
  46. 12 GW announced. 5 GW under construction. What happens next?
  47. Why Your AI Agent Isn't Ready to Ship (And How to Know When It Is)
  48. The smartest AI teams are moving past chatbots
  49. Why Foundation Models Haven’t Replaced Classical Machine Learning
  50. What Upwork, DoorDash, Meta, EY, and Fundrise reveal about agents
  51. When "Garbage In, Garbage Out" Gets It Wrong
  52. Stop upgrading your LLM. Start fixing your data.
  53. As Code Generation Speeds Up, Who Tests the Output?
  54. Why your AI bills are going up (even as tokens get cheaper) 📉💸
  55. The Gap Between AI Hype and Enterprise Reality
  56. Your AI agent looks capable. But can it actually finish the job?
  57. Reading the Tea Leaves: What the World’s Top AI Researchers Are Really Working On
  58. Generation is cheap. Evaluation is everything.
  59. From Web Video to Real-World Robots
  60. Stop tweaking your AI models. Do this instead.
  61. Why Your AI Committee Might Be Your Biggest AI Problem
  62. Building Mathematical Superintelligence
  63. Shadow IT is back, and this time it has admin access
  64. Your First AI Employee Is Already Clocking In
  65. AI is describing your competitors better than you. Here's why.
  66. Are Multi-Agent Systems More Complex Than They Need to Be?
  67. Why the heaviest AI users actually produce worse results 🤯
  68. Coding Agents Meet Data Science
  69. Why smarter agent architecture does not always improve results
  70. World Models Are Here—But It’s Still the GPT-2 Phase
  71. When AI does the junior work, how do we train seniors?
  72. The Hidden Challenges of Running AI at Scale in Production
  73. 8 domains where AI agents are actually working
  74. What No One Tells You About Staying Employable in the AI Era
  75. Adaptation: The Missing Layer Between Apps and Foundation Models
  76. The warning signs your AI vendor is becoming your cage
  77. Securing the “YOLO” Era of AI Agents
  78. AI agents just made your data pipeline obsolete
  79. Building the Open Source Alternative to AWS
  80. The margin paradox threatening every AI company
  81. Breaking the Memory Wall in the Age of Inference
  82. Your agents need runbooks, not bigger context windows
  83. Is Waymo Actually Profitable? The Real Cost of the Robotaxi Revolution
  84. Beyond Vibe Coding: Building Your Entire Business with AI
  85. The "Data Center Rebellion" is here
  86. The Rise of the Machine Identity: Securing the AI Workforce and AI Agents
  87. The 6 security shifts AI teams can't ignore in 2026
  88. Why Traditional Observability Falls Short for AI Agents
  89. Your AI passed benchmarks. Why is it failing in production?
  90. Teaching AI How to Forget
  91. Emerging AI patterns in finance (what to watch in 2026)
  92. The Humanoid Hype Cycle: Separating “Shiny Objects” from Real Utility
  93. The Junior Data Engineer is Now an AI Agent
  94. Inside the Agent Optimization Toolkit
  95. Two Quick Recommendations

2025 143

  1. The Truth About Agents in Production
  2. Data Engineering in 2026: What Changes?
  3. The best books we read this year 📚
  4. Your holiday reading list: 12 books we loved this year
  5. The Developer’s Guide to LLM Security
  6. "World Model" is a mess. Here’s how to make sense of it.
  7. Is AI a Utility? Defining Usability and Public Trust
  8. How to Build AI Copilots That Teach Rather Than Automate
  9. Beyond Black Boxes: A Guide to Observability for Agentic AI
  10. The AI Revolution Finally Comes to Structured Data
  11. The Rise of the Multimodal Lakehouse
  12. Building the Knowledge Layer Your Agents Need
  13. Legal AI Unpacked: What Works, What Fails, What’s Next
  14. How Language Models Actually Think
  15. Time Series Foundation Models: What You Need To Know
  16. How AI Is Reshaping Jobs, Budgets, and Data Centers
  17. Making Data Engineering Safe for Automation and Agents
  18. The PARK Stack Is Becoming the Standard for Production AI
  19. Is Your Database Ready for an Army of AI Agents?
  20. How to build an AI business that survives the bubble
  21. Beyond the Dashboard: Collaborative Analytics in Slack
  22. Inside the race to build agent-native databases
  23. Stop Piloting, Start Shipping: A Playbook for Measurable AI
  24. Databases for Machines, Not People
  25. Agentic AI Applications: A Field Guide
  26. When AI Agents Need to Talk: Inside the A2A Protocol
  27. The Convergence of Data, AI, and Agents: Are You Prepared?
  28. The Infrastructure for Production AI
  29. Where AI's Future Takes Shape
  30. Escaping Prototype Purgatory: A Playbook for AI Teams
  31. Escaping Prototype Purgatory: A Playbook for AI Teams
  32. How to Make Your Data Truly AI-Ready
  33. Beyond RL: A New Paradigm for Agent Optimization
  34. Beyond the Agent Hype
  35. How to Build and Optimize AI Research Agents
  36. The Specialist's Edge: When a Small, Fine-Tuned Model Beats a Giant
  37. PyTorch Summit 2025
  38. Why Digital Work is the Perfect Training Ground for AI Agents
  39. 🆕 AI Agents Are Pushing Databases to Their Limits. Here's What's Next
  40. Beyond the Chatbot: What Actually Works in Enterprise AI
  41. A pragmatic guide to enterprise search that works
  42. Why China's Engineering Culture Gives Them an AI Advantage
  43. Predictability Beats Accuracy in Enterprise AI
  44. Robotics Is Becoming AI’s Ultimate Testing Ground
  45. 2025 AI Governance Survey
  46. The "AI Doom Loop" is here
  47. Foundation Model vs. Specialized Small Models
  48. AI Consumer Insights from the Big Three Models
  49. The Fenic Approach to Production-Ready Data Processing
  50. The GraphRAG Hype is Over ⠿⠭⠿ But Graph-Based Reasoning is Just Getting Started
  51. When AI Eats the Bottom Rung of the Career Ladder
  52. From Lab to Enterprise: How Reinforcement Learning is Finally Going Mainstream in Business AI
  53. From NotebookLM to Audio Companions: Why Google’s AI Team Went Startup
  54. 🆕 The AI Builder’s Cheat Sheet 🎯 12 Lessons You Can’t Ignore
  55. 🆕 AI Data Centers Unpacked: The $5.2 Trillion Infrastructure Race
  56. The AI-Native Notebook That Thinks Like a Spreadsheet
  57. 🆕 From Hype to Reality: The True State of AI-Assisted Development
  58. New Report: The Architectural Patterns of Financial AI
  59. How Agentic AI is Transforming Wall Street
  60. Large Action Models Unpacked
  61. Compound Interest: AI's Invisible Impact on Productivity and Jobs
  62. The Quantum Advantage Is Real—But Where's the Infrastructure?
  63. 8 Game-Changing Strategies for Scaling AI Profitably
  64. A DeepMind veteran on the future of AI and quantum
  65. From Human-Readable to Machine-Usable: The New API Stack
  66. The Complete Guide to AI Evals
  67. Beyond Automation: Meet 3 AI Agents Transforming Research and Knowledge Work
  68. Why Voice Security Is Your Next Big Problem
  69. Your AI playbook for the rest of 2025
  70. The AI Governance Revolution: From Principles to Practice
  71. Unlocking Unstructured Data with LLMs
  72. Why Smaller, Specialized AI Agents Are Beating Mega-Models
  73. How Wall Street Giants and Digital Banks Are Converging on the Same AI Playbook
  74. Building Production-Grade RAG at Scale
  75. The Enterprise Guide to Voice AI Threat Modeling and Defense
  76. Speak at Ray Summit 2025
  77. Unlocking AI Superpowers in Your Terminal
  78. Unlocking AI Superpowers in Your Terminal
  79. Messy by Design: Why China's AI Chaos Might Be Its Strength
  80. 🆕 2025 AI Governance Survey Results
  81. The "boring" truth about successful AI
  82. RAG Reimagined: 5 Breakthroughs You Should Know
  83. From Vibe Coding to Autonomous Agents
  84. Time Bought, Advantage Lost? The Limits of Semiconductor Sanctions
  85. 🚨 New Data Reveals Why Most Gen-AI Pilots Fail
  86. How a Public-Benefit Startup Plans to Make Open Source the Default for Serious AI
  87. Can a single agent automate 90% of your code fixes? Block thinks so
  88. The Protocol Foundation: Building Enterprise-Ready AI Agent Systems
  89. The Highly Uncertain Future of OpenAI's Dominance
  90. Beyond Open Weights: The Path to Unconditionally Open AI
  91. The Real Apple AI Story
  92. Beyond Guardrails: Defending LLMs Against Sophisticated Attacks
  93. The Human Blueprint for Smarter AI Agents
  94. The Rise of Voice as AI’s Interface Layer: Why AI Security Must Come First
  95. Navigating the Generative AI Maze in Business
  96. OpenAI’s $125B Claim—Can It Really Happen?
  97. Chinese Open-Weights AI: Separating Security Myths from Reality
  98. The Practical Realities of AI Development
  99. Generative AI in the Real World: Lessons From Early Enterprise Winners
  100. The Model Reliability Paradox: When Smarter AI Becomes Less Trustworthy
  101. Beyond the Demo: Building AI Systems That Actually Work
  102. Is Your Data Stack Ready for Multimodal AI?
  103. Is Your AI Secure? New Threats & How to Mitigate Them
  104. Vibe Coding and the Rise of AI Agents: The Future of Software Development is Here
  105. US vs. China: Who Wins the Critical AI Diffusion Battle?
  106. Real-World Lessons from Agentic AI Deployments
  107. 2025 Artificial Intelligence Index
  108. Why Your Multi-Agent AI Keeps Failing
  109. Agent Conference
  110. How AI is Transforming Talent Development
  111. An In-Depth Look at the Stanford AI Index Report
  112. AI Deep Research Tools: Landscape, Future, and Comparison
  113. Prompts as Functions: The BAML Revolution in AI Engineering
  114. Building the Operating System for AI Agents
  115. Infrastructure Imperatives That Will Make or Break Your AI Future
  116. AI Governance at the Crossroads
  117. Recent Articles
  118. Faster Iteration, Lower Costs: BAML's Impact on AI Projects
  119. Bridging the AI Agent Prototype-to-Production Chasm
  120. [FREE cheat sheets] AI This Week: New Agents, Open Models, and the Race for Productivity
  121. The Evolution of Reinforcement Fine-Tuning in AI
  122. Recent Articles
  123. Escaping Prototype Purgatory: Bridge the gap from prototypes to scalable, enterprise-grade AI agent solutions
  124. Beyond GPUs: Cerebras’ Wafer-Scale Engine for Lightning-Fast AI Inference
  125. Recent Articles
  126. The Future of AI: Regulation, Foundation Models & User Experience
  127. Boost AI Performance: Understanding Inference Scaling
  128. The AI Agent Rundown: 10 Things to Know Now
  129. Why ‘Structure’ Is All You Need: A Deep Dive into Next-Gen AI Retrieval
  130. Real World Lessons from DeepSeek
  131. 10 Things to Know About the State of AI Agents
  132. Why Legal Hurdles Are the Biggest Barrier to AI Adoption
  133. Foundation Models: What’s Next for 2025 and Beyond
  134. Unlocking Spreadsheet Intelligence with AI
  135. AI's Spreadsheet Blind Spot: Bridging the Logic Gap
  136. Monthly Roundup: Deregulation, Hardware, and Inference Scaling
  137. Red Teaming AI: What You Need To Know
  138. What AI Teams Need to Know for 2025
  139. DeepSeek: What You Need to Know
  140. Content Moderation in Social Media: What You Need to Know
  141. AI in 2025: Don't Miss These Key Trends
  142. AI Unlocked: The Data Bottleneck
  143. The Data-Centric Shift in AI: Challenges, Opportunities, and Tools

2024 97

  1. [newsletter] The Real Story of AI in Code
  2. Monthly Roundup: Semiconductors, Frontier Models, and Practical Innovations
  3. Breaking the Cloud Barrier: How DBOS Transforms Application Development
  4. [newsletter] Empowering AI Agents with Real-Time Data
  5. The Essential Guide to AI Guardrails
  6. Recent Articles
  7. Beyond ETL: How Snow Leopard Connects AI, Agents, and Live Data
  8. 2024 Generative AI in Healthcare Survey Results
  9. [newsletter] Paradigm Shifts in Data Processing for the Generative AI Era
  10. Monthly Roundup: BAML, Tencent’s Hunyuan Model, AI & Kubernetes, and the Future of Voice AI
  11. [newsletter] Seven Features That Make BAML Ideal for AI Developers
  12. Building the Future of Finance: Inside AI Valuation Bots
  13. Analysis: What Companies Actually Want When Hiring AI Engineers in 2024
  14. Unleashing the Power of BAML in LLM Applications
  15. [newsletter] Protecting User Privacy in the Age of Generative AI
  16. Cracking the Code: How Enterprises Are Adopting Generative AI
  17. Monthly Roundup: Ray Compiled Graphs, Llama 3.2 and Multimodal AI, and Structured Data for RAG
  18. [newsletter] Unlocking Business Value by Optimizing AI Workflows
  19. Reimagining Code: The AI-Driven Transformation of Programming and Data Analytics
  20. The Security Debate: How Safe is Open-Source Software?
  21. [newsletter] Structure Is All You Need
  22. Generative AI in Voice Technology
  23. Building An Experiment Tracker for Foundation Model Training
  24. [newsletter] How Tech-Forward Organizations Build Custom AI Platforms: A Feature Breakdown
  25. Monthly Roundup: AI Regulations, GenAI for Analysts, Inference Services, and Military Applications
  26. Ray Summit 2024: Advancing AI Platforms and Applications
  27. Unlocking the Power of LLMs with Data Prep Kit
  28. [newsletter] Rethinking Analyst Roles in the Age of Generative AI
  29. Advancing AI: Scaling, Data, Agents, Testing, and Ethical Considerations
  30. The Case Against SB 1047
  31. Recent Articles
  32. Bridging the Hardware-Software Divide in AI
  33. [newsletter] Unpacking Model Collaboration: Ensembles, Routers, and Merging
  34. Monthly Roundup: The Economic Realities of Large Language Models
  35. Recent Articles
  36. From Hype to Reality: The Current State of Enterprise Generative AI Adoption
  37. [newsletter] Why Digital-First Companies Are Building Their Own AI Platforms
  38. Automating Unstructured Data Extraction with LLMs
  39. Recent Articles
  40. Generative AI in Context: Hybrid Intelligence and Responsible Development
  41. Recent Articles
  42. Monthly Roundup: Navigating the Peaks and Valleys of Generative AI Technology
  43. Recent Articles
  44. Recent Articles
  45. From Preparation to Recovery: Mastering AI Incident Response
  46. [newsletter] What is an AI Alignment Platform?
  47. Unlocking the Power of Unstructured Data
  48. Recent Articles
  49. Postgres: The Swiss Army Knife of Databases
  50. [newsletter] Why Your Generative AI Projects Are Failing
  51. Supercharging AI with Graphs
  52. Recent Articles
  53. Recent Articles
  54. Monthly Roundup: SB 1047, GraphRAG, and AI Avatars in the Workplace
  55. [newsletter] The Future of Vector Search
  56. Fine-tuning and Preference Alignment in a Single Streamlined Process
  57. Recent Articles
  58. Recent Articles
  59. TinyML, Sensor-Driven AI, and Advances in Large Language Models
  60. [newsletter] GraphRAG: Design Patterns, Challenges, Recommendations
  61. Machine Unlearning: Techniques, Challenges, and Future Directions
  62. Unleashing the Power of AI Agents
  63. Recent Articles
  64. [newsletter] Agentic AI: Challenges and Opportunities
  65. Monthly Roundup: Llama 3, Agents, Evaluation Metrics, Cyc, TikTok, and more
  66. Recent Articles
  67. LLMs for Data Access: Unlocking Insights with Text-to-SQL
  68. [newsletter] Learning from the Past: Comparing the Hype Cycles of Big Data and GenAI
  69. 2024 Artificial Intelligence Index
  70. DBRX and the Future of Open LLMs
  71. Enhancing the reliability and robustness of Retrieval Augmented Generation (RAG) systems
  72. [newsletter] GenAI and LLMs: Insights from TikTok and KPMG
  73. Monthly Roundup: New LLMs, GTC 2024, Constraint-Driven Innovation, Model Safety, and GraphRAG
  74. Automating Software Upgrades: How to Combine AI and Expert Developers
  75. [newsletter] Is Your Data Strategy Ready for Generative AI?
  76. Generative AI in the Industrial Sphere
  77. The Intersection of LLMs, Knowledge Graphs, and Query Generation
  78. DOJ Antitrust Division 🤜🤛 Apple : a cheat sheet
  79. [Newsletter] The Efficient Frontier of LLMs + Boosting RAG Systems with Knowledge Graphs
  80. Unlocking the Potential of Private Data Collaboration
  81. Frontiers of AI: From Text-to-Video Models to Knowledge Graphs
  82. Adaptive, Specialized, and Accessible: Where AI Systems Are Heading Next
  83. 2024 Themes and Trends in AI
  84. FREE Report: Generative AI in Healthcare
  85. The AI Infrastructure Revolution: From Cloud Computing to Data Center Design
  86. AI in Depth: Transforming Transportation, Enterprise, and Policy
  87. We want to hear your experiences with generative AI
  88. [newsletter] Favorable Winds for AMD in the GenAI Chip Market
  89. Software Meets Hardware: Enabling AMD for Large Language Models
  90. Incentives are Superpowers: Mastering Motivation in the AI Era
  91. [newsletter] The Rise of Function Calling in LLMs
  92. Synthetic Futures: The Convergence of Biology and AI
  93. AI Co-Pilots in Action: Transforming Function Calling in Cybersecurity
  94. [newsletter] LLM Inference Hardware: Emerging from Nvidia's Shadow
  95. Leveling Up: Tools and Techniques to Make AI Development More Accessible
  96. LLMs on CPUs, Period
  97. Mimicry or Transformation? Fair Use and Copyright Clash Over AI Training Methods

2023 85

  1. [newsletter] AI Topics That Shaped 2023 and our 2023 Book of the Year
  2. Democratizing Wealth Management With AI
  3. Apple’s AI Leap: Bridging the Gap in On-Device Intelligence
  4. Knowledge Graphs: Contextualizing Enterprise Data for More Accurate LLMs
  5. [newsletter] From Rails to AI: Lessons in Open Source for the AI Era
  6. TimeGPT: Machine Learning for Time Series, Made Accessible
  7. 🆕 🔥 FREE Report Applications of Generative AI
  8. Best Practices for Building LLM-Backed Applications
  9. [newsletter] Securing AI: Addressing the Emerging Threat of Prompt Injection
  10. The Evolution of Crypto, Blockchain, and Web3
  11. Open Source Data and AI: Past, Present, Future
  12. [newsletter] Generative AI 2023: Why This Year Marks a Major Turning Point
  13. Orchestration for LLM and RAG applications
  14. Book Review: The Fund
  15. Reflections from the First AI Conference in San Francisco
  16. OpenAI Developer Conference: Customizable AI Sparks Excitement and Concern
  17. [newsletter] Charting the Graphical Roadmap to Smarter AI
  18. Kùzu: A simple, extremely fast, and embeddable graph database
  19. Navigating the Nuances of Retrieval Augmented Generation
  20. Review: Waymo Robotaxis in San Francisco
  21. [newsletter] Best Practices in Retrieval Augmented Generation
  22. The Rise of Generative AI-Powered Social Media Manipulation
  23. Versioning and MLOps for Generative AI
  24. [newsletter] A Comprehensive Approach to Using LLMs
  25. Navigating the Generative AI Landscape
  26. Trends in Data Management: From Source to BI and Generative AI
  27. [newsletter] 7 Must-Have Features for Crafting Custom LLMs
  28. AI and the Future of Speech Technologies
  29. The Future of Cybersecurity: Generative AI and its Implications
  30. [newsletter] Unlocking the Future of Efficient AI Model Deployment
  31. Ivy: The One-Stop Interface for AI Model Deployment and Development
  32. The Future of Creativity: The Intersection of AI and Copyright
  33. Navigating the Risk Landscape: A Deep Dive into Generative AI
  34. [newsletter] Generative AI in Finance: Opportunities & Challenges
  35. Software Development with AI and LLMs
  36. A Lightweight SDK for Integrating AI Models and Plugins
  37. [newsletter] Lessons from the FTC's Probe into OpenAI
  38. Using LLMs to Build AI Co-pilots for Knowledge Workers
  39. ETL for LLMs
  40. [newsletter] Enterprise Generative AI Unfolded
  41. The Future of Graph Databases
  42. Delivering Safe and Effective LLM and NLP Applications
  43. [newsletter] The New Era of Efficient LLM Deployment
  44. Using Data and AI to Democratize Entity Resolution and Master Data Management
  45. An Open Source Data Framework for LLMs
  46. [newsletter] Decoding Apple's AI Ambitions
  47. Redefining AI Infrastructure: Deploying and Developing with a Next-Generation Developer Platform
  48. The Rise of Custom Foundation Models
  49. [newsletter] Get the Most Out of Your Custom LLMs
  50. The Future of Vector Databases and the Rise of Instant Updates
  51. LLMs Are the Key to Unlocking the Next Generation of Search
  52. [newsletter] What You Need to Know About GPT-4 and PaLM 2
  53. Building and Deploying Foundation Models for Enterprises
  54. Building Robust AI Infrastructure for Critical Solutions
  55. The AI Conference: Bridge the gap between research and practice
  56. Machine Learning for High-Risk Applications
  57. Boosting Perception With Synthetic Data
  58. [newsletter] The Future of Prompt Engineering
  59. Revolutionizing B2B: Unleashing the Power of AI and Data
  60. AI Metadata
  61. [newsletter] Unleashing LLMs in Cybersecurity: A Playbook for All Industries
  62. The 2023 AI Index
  63. Custom Foundation Models
  64. [newsletter] Building LLM-powered Apps: What You Need to Know
  65. Uncovering and Highlighting AI Trends
  66. How Data and AI Happened
  67. [newsletter] Alignment in AI: Key to Safe and Beneficial Systems
  68. Blazing fast bulk data transfers between any cloud
  69. Exhaustion of High-Quality Data Could Slow Down AI Progress in Coming Decades
  70. [newsletter] Maximizing the Potential of Large Language Models - the LLM Trifecta (Tune, Prompt, Reward)
  71. Generating high-fidelity and privacy-preserving synthetic data
  72. How technology is disrupting the venture capital industry
  73. [newsletter] The Future of Search and How You Can Shape It
  74. Running Machine Learning Workloads On Any Cloud
  75. 2023 Trends in Data Engineering and Infrastructure
  76. [newsletter] Unveiling the Future of AI: Insights on AI Chips, Knowledge Graphs, and AI Regulations
  77. Preparing for the Implementation of the EU AI Act and Other AI Regulations
  78. Specialized Hardware for AI: Rethinking Assumptions and Implications for the Future
  79. The Open Source Stack Unleashing a Game-Changing AI Hardware Shift
  80. [newsletter] Latest Trends in Automation, Experimentation, and Language Model Evaluation
  81. Data Science and AI in Context
  82. Evaluating Language Models
  83. FREE Report: Trends in Data, Machine Learning, and AI
  84. 2023 Opportunities and Trends: Data, Machine Learning, and AI
  85. Exploring DALL·E 2

2022 67

  1. Data Science at Shopify and Stitch Fix
  2. Building a data management system for unstructured data
  3. [newsletter] Our Top Book Pick for 2022
  4. A Cloud Native Vector Database Management System
  5. What's Next for Machine Learning in Time Series
  6. [newsletter] We Need Efficient and Transparent Language Models
  7. Efficient Methods for Natural Language Processing
  8. Responsible and Trustworthy AI
  9. Building a premier industrial AI research and product group
  10. [newsletter] Taking time series modeling and stream processing mainstream
  11. An open source, production grade vector search engine
  12. The Stream Processing Index
  13. A comprehensive suite of open source tools for time series modeling
  14. Trends and Opportunities in Time Series
  15. Building Safe and Reliable AI applications
  16. [newsletter] Data and AI job markets are slowing down
  17. A new storage engine for vectors
  18. Here’s what we need to do to fix AutoML
  19. Project Lightspeed: Next-generation Spark Streaming
  20. The Unreasonable Effectiveness of Speech Data
  21. Embed Retrieve Win
  22. Machine Learning Integrity
  23. Synthetic data technologies can enable more capable and ethical AI
  24. The Vector Database Index
  25. Confidential Computing for Machine Learning
  26. New open source tools to unlock speech and audio data
  27. Applied NLP Research at Primer
  28. Exposing APIs and services through SQL
  29. Machine Learning for Time Series Intelligence
  30. Unleashing the power of large language models
  31. Building production-ready machine learning pipelines
  32. Ray Summit 2022
  33. Machine Learning at Gong
  34. Data Infrastructure for Computer Vision
  35. How DALL·E works
  36. Scalable, end-to-end machine learning, for everyone
  37. Orchestration and Pipelines for Data Scientists
  38. Dataframes at scale
  39. FREE Reports: Orchestration and Identity Management
  40. Software-Defined Assets
  41. Adversarial Machine Learning
  42. The Business Intelligence Index
  43. Orchestrating Machine Learning Applications
  44. Narrative AI
  45. Machine Learning Model Observability
  46. Dataflow Automation
  47. Practical Machine Learning and Deep learning
  48. Machine Learning for Optimization
  49. Efficient Scaling of Language Models
  50. Data Science at Stitch Fix
  51. The 2022 AI Index
  52. Why You Need A Time-Series Database
  53. Data Science at Shopify
  54. An AI Risk Management Framework
  55. An open source and end-to-end library for causal inference
  56. The Graph Intelligence Stack
  57. NLP and Language Models in Healthcare and the Life Sciences
  58. Ratio of Data Scientists to Data Engineers (Gradient Flow Snapshot #90)
  59. Delivering Continuous Intelligence at Scale
  60. Where Do Machine Learning Engineers Work? (Gradient Flow Snapshot #89)
  61. Imperceptible NLP Attacks
  62. Evolving Data Science Training Programs
  63. Building Machine Learning Infrastructure at Netflix and beyond
  64. Democratizing NLP
  65. Machine Learning at Discord
  66. Applications of Knowledge Graphs
  67. Key AI and Data Trends for 2022

2021 105

  1. Large Language Models
  2. Data and Machine Learning Platforms at Shopify
  3. Gradient Flow Snapshot #84: GitHub Copilot, Coding Assistants, and the Rise of Low-Code and No-Code
  4. What is AI Engineering?
  5. NLP and AI in Financial Services
  6. Gradient Flow Snapshot #82: Chinese Data Governance
  7. Modern Experimentation Platforms
  8. Reinforcement Learning in Real-World Applications
  9. Gradient Flow Snapshot #81: Top Places to Work for Data Engineers
  10. MLOps Anti-Patterns
  11. Gradient Flow Snapshot #80: Homomorphic Encryption and Hardware Enclaves
  12. Why You Need a Modern Metadata Platform
  13. Gradient Flow Snapshot #79: Data Engineering Survey Report
  14. Making Large Language Models Smarter
  15. Gradient Flow Snapshot #78: Resurgence of Conversational AI
  16. AI Begins With Data Quality
  17. Gradient Flow Snapshot #77: Cleanlab, machine learning with noisy labels
  18. Modernizing Data Integration
  19. Gradient Flow Snapshot #76: Scalable Entity Resolution; Human-Assisted AI
  20. Deploying Machine Learning Models Safely and Systematically
  21. Gradient Flow Snapshot #75: Top Places to Work for Data Scientists
  22. Large-scale machine learning and AI on multi-modal data
  23. Gradient Flow Snapshot #74: 2021 NLP Industry Survey
  24. Machine Learning in Astronomy and Physics
  25. Gradient Flow Snapshot #73: Low-Code and No-Code Unpacked
  26. Julia and the unreasonable effectiveness of multiple dispatch
  27. Gradient Flow Snapshot #72: Data Lakehouse FAQ; Demystifying graph deep learning
  28. How To Lead In Data Science
  29. Gradient Flow Snapshot #71: Scaling Deep Learning Model Training to the Trillions; Prettymaps
  30. Why interest in graph databases and graph analytics are growing
  31. Gradient Flow Snapshot #70: Cloud Strategy Survey; Feature Stores Unpacked; FTC on Algorithmic Harms
  32. The State of Data Journalism
  33. Gradient Flow Snapshot #69: Data Quality Unpacked
  34. Auditing machine learning models for discrimination, bias, and other risks
  35. Gradient Flow Snapshot #68: Enterprise Software and Sky Computing; Stack Overflow Developer Survey
  36. An oscilloscope for deep learning
  37. Gradient Flow Snapshot #67: Silicon Valley Software Engineering Talent Report
  38. What’s new in data engineering
  39. Gradient Flow Snapshot #66: Data Orchestration; Storage for a Multi-cloud World; AI in China
  40. The evolution of the data science role and of data science tools
  41. Gradient Flow Snapshot #65: Data Portability
  42. Data Augmentation in Natural Language Processing
  43. Gradient Flow Snapshot #64: Applications of AI in Smart Buildings & Energy Efficiency
  44. Storage Technologies for a Multi-cloud World
  45. Gradient Flow Snapshot #63: Data For Better Lives
  46. Building a next-generation dataflow orchestration and automation system
  47. Gradient Flow Snapshot #62: Edge Observability → Intelligence; Datanova; Global Commodity Traders
  48. Building a flexible, intuitive, and fast forecasting library
  49. Gradient Flow Snapshot #61: Working with data in GitHub using Flat Data, Data Engineering Survey
  50. Neural Models for Tabular Data
  51. 2021 Data engineering Survey
  52. Gradient Flow Snapshot #60: Self-supervised Learning; SaaS CTO Security Checklist; 2021 NLP Survey
  53. Training and Sharing Large Language Models
  54. Gradient Flow Snapshot #59: From Cloud → Sky computing; Automation in DataOps; Top Technology Trends
  55. Questioning the Efficacy of Neural Recommendation Systems
  56. Gradient Flow Snapshot #58: Delta Live Tables; Knowledge Graphs; Data Portability
  57. Automation in Data Management and Data Labeling
  58. Gradient Flow Snapshot #57: Monitoring Machine Learning Models; Greykite for Time-series Forecasting
  59. Reinforcement Learning For the Win
  60. Gradient Flow Snapshot #56: Airflow + Ray; Data Warehouse → Lakehouse; CSV file → Knowledge Graph
  61. How Companies Are Investing in AI Risk and Liability Minimization
  62. Gradient Flow Snapshot #55: Reinforcement Learning in the Enterprise, Knowledge Graphs in Finance
  63. The Future of Machine Learning Lies in Better Abstractions
  64. Gradient Flow Snapshot #54: NLP Index and Getting Read for New AI Regulations
  65. Why You Should Optimize Your Deep Learning Inference Platform
  66. Gradient Flow Snapshot #53: Data Validation for Machine Learning; Modernizing Data Governance
  67. AI Beyond Automation
  68. Gradient Flow Snapshot #52: Data Integration; Language Benchmarks; Online Resource Allocation with Ray
  69. Injecting Software Engineering Practices and Rigor into Data Governance
  70. Gradient Flow Snapshot #51: What is DataOps; 2021 Technology Radar
  71. Building a data store for unstructured data and deep learning applications
  72. FREE Report: 2021 Business at the Speed of AI Report
  73. Gradient Flow Snapshot #50: Data Engineering jobs in the U.S; Algorithms That Make Instacart Roll
  74. How Technology Companies Are Using Ray
  75. FREE Report: 2020 NLP Industry Survey Report
  76. Gradient Flow Snapshot #49: Data Cascades; Exploiting ML Models; Prisma Migrate
  77. Data quality is key to great AI products and services
  78. FREE Report: 2021 Trends in Data and AI
  79. Gradient Flow Snapshot #48: Privacy and Computer Vision; AI in Healthcare Survey Results
  80. Machine Learning in Healthcare
  81. FREE Report: AI in Healthcare Survey Results
  82. Gradient Flow Snapshot #47: Tips for New Engineering Managers; Implementing a New Data Management Paradigm
  83. Measuring Corporate and University Research Impact in AI and Machine Learning
  84. Gradient Flow Snapshot #46: Resource-intensive applications; Shipping Data Products; Bellingcat
  85. The Mathematics of Data Integration and Data Quality
  86. Gradient Flow Snapshot #45: U.S. National AI Strategy
  87. Pricing Data Products
  88. Gradient Flow Snapshot #44: Graph Computing Platform; State-of-the-art image recognition; Infosec
  89. Challenges, Opportunities, and Trends in EdTech
  90. Gradient Flow Snapshot #43: Business at the Speed of AI; Towards Interpretable and Trustworthy AI
  91. Towards Simple, Interpretable, and Trustworthy AI
  92. Gradient Flow Snapshot #42: State of Machine Learning & Data Science; Speech Synthesis; Apache Arrow
  93. The Rise of Metadata Management Systems
  94. Gradient Flow Snapshot #41: Metadata Management; Data Transparency; Surveillance Technology
  95. Tools for building robust, state-of-the-art machine learning models
  96. Gradient Flow Snapshot #40: Security Predictions for 2021; Trends in Data and AI; Apache Pulsar
  97. Creating Master Data at Scale with AI
  98. Gradient Flow Snapshot #39: Top data science & data engineering tools; Jumpstart your Data Strategy
  99. Bringing AI and computing closer to data sources
  100. Gradient Flow Snapshot #38: Data & AI Executive Survey; Top Geopolitical Risks for 2021; Voices of Tech Workers
  101. Deep Learning in the Sciences
  102. Gradient Flow Snapshot #37: Top 10 AI Advances in 2020; What Silicon Valley "Gets" about Developers
  103. Taking business intelligence and analyst tools to the next level
  104. Data exchanges and their applications in healthcare and the life sciences
  105. Gradient Flow Snapshot #36: Technology in Banking; Skills Gap in Data Science; RL jobs

2020 96

  1. Key AI and Data Trends for 2021
  2. Gradient Flow Snapshot #35: AI and Automation meet Business Intelligence
  3. A Unified Management Model for Successful Data-Focused Teams
  4. Gradient Flow Snapshot #34: Book recommendations - China ⋂ ASEAN, Cybersecurity, Experimentation
  5. Gradient Flow Snapshot #33: Feature Stores, Time-series Databases, Top Cities for Expats
  6. Security and privacy for the disoriented
  7. Gradient Flow Snapshot #32: Billion Dollar Dataset, Driving Assistance Systems, Data Pipelines
  8. The State of Responsible AI
  9. Improving the robustness of natural language applications
  10. Gradient Flow Snapshot #30: Incubating Startups ; Data & AI Learning Maps ; Adaptive Learning
  11. End-to-end deep learning models for speech applications
  12. Gradient Flow Snapshot #29: Bias in Computer Vision, Time-series databases, Tuning Language Models
  13. Securing machine learning applications
  14. Gradient Flow Snapshot #28: Technology job market; Training data & reinforcement learning; AI Safety
  15. Testing Natural Language Models
  16. Gradient Flow Snapshot #27: Feature Stores, Detecting Fake News, Building Machine Learning apps
  17. Detecting Fake News
  18. Gradient Flow Snapshot #26: Transparency in AI, Data Science Jobs, Advanced Hyperparameter Tuning
  19. The Computational Limits of Deep Learning
  20. Gradient Flow Snapshot #25: Attacking autonomous driving, Becoming a Data Engineer, Bias in NLP
  21. Making deep learning accessible
  22. Gradient Flow Snapshot #24: Deep learning for financial time series, Data Catalogs, the Hype Machine
  23. Building and deploying knowledge graphs
  24. Gradient Flow Snapshot #23: Infinite Laptops, Automation & Physical Safety, Trending Topics
  25. Financial Time Series Forecasting with Deep Learning
  26. Gradient Flow Snapshot #22: 2020 NLP Survey, Accelerators for Deep Learning, & Navigating the Future
  27. A programming language for scientific machine learning and differentiable programming
  28. Gradient Flow Snapshot #21: NLP in the legal profession, Technology in Banking, Blands
  29. Using machine learning to modernize medical triage and monitoring systems
  30. 2020 NLP Survey Report
  31. Gradient Flow Snapshot #20: Deep Learning for navigation, Accelerating NLP model tuning, Legacy IT
  32. Connecting Reinforcement Learning to Simulation Software
  33. Scalable machine learning, scalable Python, for everyone
  34. Gradient Flow Snapshot #19: NLP for public policy, Differential privacy, Technology job market
  35. Using machine learning to detect shifts in government policy
  36. Gradient Flow Snapshot #18: Levels of Conversational AI, AI Assurance, Voter Rolls
  37. What is AI Assurance?
  38. Gradient Flow Snapshot #17: Efficient Reinforcement Learning, Compressing Language Models, Semiconductors
  39. Best practices for building conversational AI applications
  40. Gradient Flow Snapshot #16: Bias in speech technologies; Industry sectors that use Reinforcement Learning
  41. Tools for scaling machine learning
  42. Gradient Flow Snapshot #15: Testing of NLP models, Technology Adoption in the US, Data Skepticism
  43. From Python beginner to seasoned software engineer
  44. Which industry sectors are using reinforcement learning?
  45. Gradient Flow Snapshot #14: State-of-the-art chatbots, Models that rely on Wastewater, Serverless job market
  46. Assessing Models and Simulations of Epidemic Infectious Diseases
  47. Gradient Flow Snapshot #13: Computational Limits of Deep Learning, Water, Survival of the Friendliest
  48. Improving the hiring pipeline for software engineers
  49. Mail-in Voting in the U.S.
  50. Gradient Flow Snapshot #12
  51. How to build state-of-the-art chatbots
  52. FREE pass to the upcoming NLP Summit (virtual conference)
  53. Gradient Flow Snapshot #11
  54. Democratizing Machine Learning
  55. Gradient Flow Snapshot #10
  56. How graph technologies are being used to solve complex business problems
  57. Natural Language Processing: An Industry Survey
  58. Gradient Flow Snapshot #9
  59. Machines for unlocking the deluge of COVID-19 papers, articles, and conversations
  60. Gradient Flow Snapshot #8
  61. Designing machine learning models for both consumer and industrial applications
  62. Gradient Flow Snapshot #7
  63. Building open source developer tools for language applications
  64. Demystifying AI Infrastructure
  65. Gradient Flow Snapshot #6
  66. Viewing machine learning and data science applications as sociotechnical systems
  67. Gradient Flow Snapshot #5
  68. Identifying and mitigating liabilities and risks associated with AI
  69. Data+AI Snapshot #4
  70. How machine learning is being used in quantitative finance
  71. How machine learning is being used in quantitative finance
  72. One Simple Chart
  73. Data+AI Snapshot #3
  74. Understanding machine learning model governance
  75. Data+AI Snapshot #2
  76. Improving performance and scalability of data science libraries
  77. Why TinyML will be huge
  78. Understand the Ray ecosystem in ... less than seven minutes
  79. An open source platform for training deep learning models
  80. Algorithms that continually invent both problems and solutions
  81. Computational Models and Simulations of Epidemic Infectious Diseases
  82. Human-in-the-loop machine learning
  83. Next-generation simulation software will incorporate deep reinforcement learning
  84. Business at the speed of AI: Lessons from Shopify
  85. How deep learning is being used in search and information retrieval
  86. The responsible development, deployment and operation of machine learning systems
  87. Hyperscaling natural language processing
  88. Please subscribe to The Data Exchange
  89. What businesses need to know about model explainability
  90. Scalable Machine Learning, Scalable Python, For Everyone
  91. Computational humanness, analogy and innovation, and soft concepts
  92. Building domain specific natural language applications
  93. The state of privacy-preserving machine learning
  94. Taking messaging and data ingestion systems to the next level
  95. Business at the speed of AI: Lessons from Rakuten
  96. The combination of the right software and commodity hardware will prove capable of handling most machine learning tasks

2019 51

  1. Books I enjoyed in 2019
  2. Key AI and Data Trends for 2020
  3. The Road to Software 2.0
  4. The evolution of TensorFlow and of machine learning infrastructure
  5. Building large-scale, real-time computer vision applications
  6. Introducing the Data Exchange
  7. A world of deep fakes
  8. Machine learning for operational analytics and business intelligence
  9. Machine learning and analytics for time series data
  10. Understanding deep neural networks
  11. How New Tools In Data And AI Are Being Used In Healthcare And Medicine
  12. Becoming a machine learning practitioner
  13. One simple chart: Who is interested in Apache Pulsar?
  14. How organizations are sharpening their skills to better understand and use AI
  15. Labeling, transforming, and structuring training data sets for machine learning
  16. Got speech? These guidelines will help you get started building voice applications
  17. Make data science more useful
  18. One simple graphic: Researchers love PyTorch and TensorFlow
  19. Acquiring and sharing high-quality data
  20. You'll want Nexar's newly released Live Map for your city
  21. Managing machine learning in the enterprise: Lessons from banking and health care
  22. Tools for machine learning development
  23. RISELab’s AutoPandas hints at automation tech that will change the nature of software development
  24. One simple chart: Who is interested in Spark NLP?
  25. AI and machine learning will require retraining your entire organization
  26. Enabling end-to-end machine learning pipelines in real-world applications
  27. What are model governance and model operations?
  28. The quest for high-quality data
  29. AI adoption is being fueled by an improved tool ecosystem
  30. Bringing scalable real-time analytics to the enterprise
  31. Applications of data science and machine learning in financial services
  32. Becoming a machine learning company means investing in foundational technologies
  33. How AI and machine learning are improving customer experience
  34. Real-time entity resolution made accessible
  35. Why companies are in need of data lineage solutions
  36. What data scientists and data engineers can do with current generation serverless technologies
  37. Specialized tools for machine learning development and model governance are becoming essential
  38. It's time for data scientists to collaborate with researchers in other disciplines
  39. Gliding down the world's longest zipline
  40. Algorithms are shaping our lives - here's how we wrest back control
  41. You created a machine learning application. Now make sure it's secure.
  42. Why your attention is like a piece of contested territory
  43. How AI can help to prevent the spread of disinformation
  44. The evolution and expanding utility of Ray
  45. The technical, societal, and cultural challenges that come with the rise of fake media
  46. Using machine learning and analytics to attract and retain employees
  47. How machine learning impacts information security
  48. 9 AI trends on our radar
  49. 7 data trends on our radar
  50. In the age of AI, fundamental value resides in data
  51. Books I enjoyed in 2018

2018 40

  1. Deep automation in machine learning
  2. Assessing progress in automation technologies
  3. Tools for generating deep neural networks with efficient network architectures
  4. Building tools for enterprise data science
  5. Managing risk in machine learning
  6. Lessons learned while helping enterprises adopt machine learning
  7. Machine learning on encrypted data
  8. How social science research can inform the design of AI systems
  9. Why it's hard to design fair machine learning models
  10. Using machine learning to improve dialog flow in conversational applications
  11. Building accessible tools for large-scale computation and machine learning
  12. Simplifying machine learning lifecycle management
  13. Notes from the first Ray meetup
  14. How privacy-preserving techniques can lead to more robust machine learning models
  15. Specialized hardware for deep learning will unleash innovation
  16. Data collection and data markets in the age of privacy and machine learning
  17. What machine learning means for software development
  18. Data regulations and privacy discussions are still in the early stages
  19. Managing risk in machine learning models
  20. Understanding automation
  21. The real value of data requires a holistic view of the end-to-end data pipeline
  22. The evolution of data science, data engineering, and AI
  23. Companies in China are moving quickly to embrace AI technologies
  24. How to build analytic products in an age when data privacy has become critical
  25. Teaching and implementing data science and AI in the enterprise
  26. Building tools for the AI applications of tomorrow
  27. The importance of transparency and user control in machine learning
  28. What machine learning engineers need to know
  29. How to train and deploy deep learning at scale
  30. Using machine learning to monitor and optimize chatbots
  31. Unleashing the potential of reinforcement learning
  32. Graphs as the front end for machine learning
  33. Machine learning needs machine teaching
  34. Introducing RLlib: A composable and scalable reinforcement learning library
  35. How machine learning can be used to write more secure computer programs
  36. Responsible deployment of machine learning
  37. What lies ahead for data in 2018
  38. 5 AI trends to watch in 2018
  39. 8 fintech trends for 2018
  40. Bringing AI into the enterprise

2017 36

  1. How machine learning will accelerate data management systems
  2. The state of AI adoption
  3. Practical applications of reinforcement learning in industry
  4. Machine learning at Spotify: You are what you stream
  5. The current state of Apache Kafka
  6. Building a natural language processing library for Apache Spark
  7. Machine intelligence for content distribution, logistics, smarter cities, and more
  8. How companies can navigate the age of machine learning
  9. Vehicle-to-vehicle communication networks can help fuel smart cities
  10. Transforming organizations through analytics centers of excellence
  11. The state of machine learning in Apache Spark
  12. Effective mechanisms for searching the space of machine learning algorithms
  13. The current state of applied data science
  14. How Ray makes continuous learning accessible and easy to scale
  15. Why continuous learning is key to AI
  16. Why AI and machine learning researchers are beginning to embrace PyTorch
  17. How big data and AI will reshape the automotive industry
  18. A framework for building and evaluating data products
  19. Building a next-generation platform for deep learning
  20. A scalable time-series database that supports SQL
  21. Programming collective intelligence for financial trading
  22. Creating large training data sets quickly
  23. What are machine learning engineers?
  24. Data science and deep learning in retail
  25. Language understanding remains one of AI’s grand challenges
  26. Data preparation in the age of deep learning
  27. Scaling machine learning
  28. Architecting and building end-to-end streaming applications
  29. Becoming a machine learning engineer
  30. Natural language analysis using Hierarchical Temporal Memory
  31. Deep learning that's easy to implement and easy to scale
  32. Building machine learning solutions that can withstand adversarial attacks
  33. Deep learning for Apache Spark
  34. The key to building deep learning solutions for large enterprises
  35. Use deep learning on data you already have
  36. How big compute is powering the deep learning rocket ship

2016 34

  1. 2017 will be the year the data science and big data community engage with AI technologies
  2. Data is only as valuable as the decisions it enables
  3. Introducing model-based thinking into AI systems
  4. Building the next-generation big data analytics stack
  5. Visual tools for overcoming information overload
  6. Why businesses should pay attention to deep learning
  7. The technology behind self-driving vehicles
  8. Data architectures for streaming applications
  9. Strata NYC 2016 is next week
  10. Data science for humans and data science for machines
  11. The importance of emotion in AI systems
  12. Building human-assisted AI applications
  13. Enabling enterprise adoption of AI technologies
  14. Using Agile development techniques for data science projects
  15. 3 ideas to add to your data science toolkit
  16. Commercial speech recognition systems in the age of big data and deep learning
  17. What is Artificial Intelligence?
  18. Building intelligent applications with deep learning and TensorFlow
  19. Hybrid transactional/analytic systems and the quest for database nirvana
  20. Using AI to build a comprehensive database of knowledge
  21. Hardcore Data Science, London 2016
  22. Structured streaming comes to Apache Spark 2.0
  23. Don't overlook simpler techniques and algorithms
  24. Recent trends in recommender systems
  25. Semi-supervised, unsupervised, and adaptive algorithms for large-scale time series
  26. Practical machine learning techniques for building intelligent applications
  27. Hardcore Data Science, California 2016
  28. Democratizing business analytics
  29. Stream processing and messaging systems for the IoT age
  30. Using Apache Spark to predict attack vectors among billions of users and trillions of events
  31. Metadata services can lead to performance and organizational improvements
  32. Building a business that combines human experts and data science
  33. Compressed representations in the age of big data
  34. Is 2016 the year you let robots manage your money?

2015 51

  1. Investing in big data technologies
  2. Building a scalable platform for streaming updates and analytics
  3. Graph databases are powering mission-critical applications
  4. Singapore Eats
  5. Architecting big data applications in the cloud
  6. Building systems for massive scale data applications
  7. Turning big data into actionable insights
  8. How intelligent data platforms are powering smart cities
  9. We need open and vendor-neutral metadata services
  10. Resolving transactional access and analytic performance trade-offs
  11. Specialized and hybrid data management and processing engines
  12. Hardcore Data Science, NYC 2015
  13. Building enterprise data applications with open source components
  14. From search to distributed computing to large-scale information extraction
  15. Introduction to Tachyon and a deep dive into Baidu's production use case
  16. Celebrating the real-time processing revival
  17. Bringing Apache Spark closer to bare metal
  18. Bridging the divide: Business users and machine learning experts
  19. Pattern recognition and sports data
  20. Understanding neural function and virtual reality
  21. 6 reasons why I like KeystoneML
  22. Apache Spark in the Enterprise and in China
  23. Why data preparation frameworks rely on human-in-the-loop systems
  24. Fireside chat with Ben Horowitz
  25. Large-scale Data Science and Machine Learning with Spark
  26. Building self-service tools to monitor high-volume time-series data
  27. Apache Spark: Powering applications on-premise and in the cloud
  28. Data science makes an impact on Wall Street
  29. Israel conference on Big Data, Analytics and Machine Learning
  30. The tensor renaissance in data science
  31. More tools for managing and reproducing complex data projects
  32. The Spark Spot at Spark Summit East 2015 - NYC
  33. Coming full circle with Bigtable and HBase
  34. Spark Summit East panel
  35. Building big data systems in academia and industry
  36. A real-time processing revival
  37. Scikit-Learn 0.16
  38. Redefining power distribution using big data
  39. Apache Spark 1.3, the new Dataframe API, and Spark performance
  40. Let's build open source tensor libraries for data science
  41. Turning Ph.D.s into industrial data scientists and data engineers
  42. Topic Models: Past, Present, Future
  43. Hardcore Data Science: 2015 California
  44. Forecasting events, from disease outbreaks to sales to cancer research
  45. Network structure and dynamics in online social systems
  46. The evolution of GraphLab
  47. Building and deploying large-scale machine learning pipelines
  48. A brief look at data science’s past and future
  49. "Humans-in-the-loop" machine learning systems
  50. Lessons from next-generation data wrangling tools
  51. Spark 1.2 and Beyond

2014 43

  1. Apache Spark’s journey from academia to industry
  2. Clustering bitcoin accounts using heuristics
  3. Regulation and decentralization: Defending the blockchain
  4. Bitcoin and the Future of Money
  5. Hardcore Data Science day: Strata+Hadoop World 2015
  6. Building Apache Kafka from scratch
  7. Decoding bitcoin and the blockchain
  8. The Future of Bitcoin
  9. The science of moving dots: the O'Reilly Data Show Podcast
  10. Spark + Cassandra: Technical Integration Details
  11. Anomaly Detection with ElasticSearch
  12. Unboxing Apache Spark 1.1
  13. Announcing Spark Certification
  14. Real-world Active Learning
  15. Scaling up Data Frames
  16. What's New in Scikit-learn 0.15
  17. Best Practices for Optimizing Infrastructure Performance and Budget
  18. Deep Learning for Hackers
  19. Databricks Cloud makes it easier to build Data Products
  20. There are many use cases for graph databases and analytics
  21. Super Simple Real-Time Big Data Backend
  22. Don't miss the keynotes at the 2014 Spark Summit
  23. Scalable Data Science on a Laptop
  24. Feature Engineering
  25. Data Analysis on Streams
  26. A growing number of companies are building applications with Apache Spark
  27. Welcome to Intelligence Matters
  28. Network Science Dashboards
  29. Verticalized Big Data applications
  30. Advanced Analytics on Relational Data with Spark SQL
  31. 5 Fun Facts about HBase that you didn't know
  32. Crowdsourcing Feature discovery
  33. Instrumenting collaboration tools used in data projects
  34. 2013 Revenue of some startup companies
  35. Interface Languages and Feature Discovery
  36. Extending GraphLab to tables
  37. Bridging the gap between research and implementation
  38. Big Data solutions through the combination of tools
  39. Business analysts want access to advanced analytics
  40. What I use for data visualization
  41. IPython: A unified environment for interactive data analysis
  42. Big Data systems are making a difference in the fight against cancer
  43. A compelling family of DSLs for Data Science

2013 52

  1. Six reasons why I recommend scikit-learn
  2. Financial analytics as a service
  3. Expanding options for mining streaming data
  4. Reproducing Data Projects
  5. Data Scientists and Data Engineers like Python and Scala
  6. The Seven Things You're Not Supposed to Talk About
  7. Data Wrangling gets a fresh look
  8. Day-Long Immersions and Deep Dives at Strata Santa Clara 2014
  9. How companies are using Spark
  10. Simplifying interactive, realtime, and advanced analytics
  11. The emergence of Crowdsourcing specialists
  12. Deep Learning oral traditions
  13. Stream Mining essentials
  14. Semi-automatic method for grading a million homework assignments
  15. Gaining access to the best machine-learning methods
  16. Databricks aims to build next-generation analytic tools for Big Data
  17. Stream Processing and Mining just got more interesting
  18. How Twitter monitors millions of time-series
  19. Data Analysis: Just one component of the Data Science workflow
  20. Running batch and long-running, highly available service jobs on the same cluster
  21. Data analysis tools target non-experts
  22. Interactive Big Data analysis using approximate answers
  23. Surfacing anomalies and patterns in Machine Data
  24. Big Data and Advertising: In the trenches
  25. Near realtime, streaming, and perpetual analytics
  26. Tightly integrated engines streamline Big Data analysis
  27. Data scientists tackle the analytic lifecycle
  28. Pattern-detection and Twitter's Streaming API
  29. Moving from Batch to Continuous Computing at Yahoo!
  30. Analytic engines that factor in security labels
  31. HBase looks more appealing to data scientists
  32. It's getting easier to build Big Data Applications
  33. How signals, geometry, and topology are influencing data science
  34. Improving options for unlocking your graph data
  35. 11 Essential Features that Visual Analysis Tools Should Have
  36. Scalable streaming analytics using a single-server
  37. Tachyon: An open source, distributed, fault-tolerant, in-memory file system
  38. Simpler workflow tools enable the rapid deployment of models
  39. Single server systems can tackle Big Data
  40. The re-emergence of Time-series
  41. Data Science tools: Are you "all in" or do you "mix and match"?
  42. Python data tools just keep getting better
  43. Saying Goodbye to our (Cat) Lily
  44. No single DBMS will meet all your needs
  45. Data Science Tools: Fast, easy to use, and scalable
  46. An update on in-memory data management
  47. MLbase: Scalable Machine-learning made accessible
  48. 2012 Revenue of some Big Data companies
  49. Contest: Social Computing, Behavioral-Cultural Modeling, & Prediction
  50. 2012 Hedge Fund Performance
  51. Need speed for big data? Think in-memory data management
  52. My Year In Data: How my Toolset evolved in 2012

2012 21

  1. GraphChi: Graph analytics over billions of edges using your laptop
  2. Shark: Real-time queries and analytics for big data
  3. All In on Real-time: Hokusai adds a temporal component to Count-Min Sketch
  4. Beyond bag-of-words: Using markup to Understand How Science is Written
  5. Reconstruction Error: A promising fresh take on Automatic Text Summarization
  6. Mining Time-series with Trillions of Points: Dynamic Time Warping at scale
  7. Spark 0.6 Improves Performance and Accessibility
  8. Statwing Simplifies Data Analysis
  9. How ZeroVM changes analytics in the cloud
  10. Seven Reasons I like Spark
  11. Hedge Fund Performance: First half of 2012
  12. Estimating Word Count - Graham Greene edition
  13. Which Code Review Tool do you prefer?
  14. Offbeat encouters at Foo Camp 2012
  15. Frugal Innovation in India: Aravind + Women in Delhi
  16. Much to like about #HBasecon
  17. Private Equity 30-year performance: Buyout and Venture Capital
  18. Hedge Fund profits flow mainly to Industry Insiders
  19. When evaluating Investment Funds, use Dollar-weighted Returns
  20. Sarah Palin and machine-translation
  21. 2011 Hedge Fund Performance

2011 32

  1. Long-term performance of Venture Capital & Hedge Funds
  2. Data Mining with the Maximal Information Coefficient
  3. Hedge Fund Performance (Nov/2011) & Maximum Drawdown
  4. On Elite Schools, and Soft vs. Hard Skills
  5. 2011 Web 2.0 Summit: By the Numbers
  6. Today's Occupy SF March
  7. The Best of @BigData Sep/2011
  8. Twitter's Front-end Summit
  9. Proposed U.S. Tax Hikes in a few charts
  10. Hedge Fund Performance - August/2011
  11. Popular Links during Obama Jobs Speech & Republican Debate
  12. The Best of @BigData Aug/2011
  13. Bits from Scifoo 2011
  14. Large-scale Named Entity Recognition in the Cloud
  15. Rebranding Data Scientists as Data Alchemists
  16. Computer Vision & Compressed Sensing: Recovery of signals, images, & video from meager data
  17. Writing code that makes your data sing: Auditory Data Display Tools and Resources
  18. Factoids from Twitter's Engineering Open House
  19. Text Mining and Twitter III- LDA Code on Hadoop
  20. 2010 Census: Ethnic & Racial breakdown within the major Metro Areas
  21. Feeling right at home in the SF Bay Area
  22. Tweaking Press Releases
  23. Hedge Fund Performance - April/2011
  24. You know there's a Tech Bubble
  25. Hedge Fund Performance - Q1/2011
  26. Hedge Fund Performance - Feb/2011
  27. Social Networks evolve into two opposing camps, or into a single all-friendly group
  28. Hedge Fund Performance - Jan/2011
  29. A quick overview of the App Economy, using data from the iTunes app store
  30. James Simons reminisces about Math
  31. Supporting old-fashioned Journalism
  32. Hedge Fund Performance - 2010

2010 97

  1. John Sayles' Amigo
  2. Ranking U.S. Universities using Research Ouput and Impact
  3. Hedge Fund Performance - Nov/2010
  4. Recent data analytics & visualization contests
  5. Hedge Fund Performance - Oct/2010
  6. Bad Girls Book Club
  7. 2010 Web 2.0 Summit: By the Numbers
  8. Active Facebook users by region: November, 2010
  9. Hiring trends among the major platform players
  10. Inside Facebook's infrastructure: Hive
  11. Inside Google's Infrastructure: MapReduce Statistics Over Time
  12. Windows Mobile apps are more expensive than iPhone Apps
  13. Crowdsourcing Specific Microtasks
  14. Semi-regular Dimes
  15. Compressed Sensing and Big Data
  16. Apple's Q4-2010 Earnings: Strong revenue from the iPhone and iPad
  17. Hedge Fund Performance - Q3/2010
  18. Partha Niyogi, Computer Science Professor, 1967-2010: Manifold (Machine) Learning Pioneer
  19. Smartphone stats on the day Windows Phone 7 Launches
  20. Failcon 2010 Fails (at least temporarily)
  21. A Taste of Home in San Francisco
  22. Manufacturing in China: Two Recent Perspectives
  23. Text Mining and Twitter II - Python code for Analyzing Streaming Data Sets
  24. The most dangerous job in America
  25. Some thoughts on (natural language) Search
  26. Hedge Fund Performance - August/2010
  27. Power to the Peaceful 2010
  28. Too Smart
  29. Amazon's cloud platform still the largest, but others are closing the gap
  30. Don't Panic, It's Organic
  31. The number of Hadoop jobs continue to rise
  32. Which Social Gaming companies are Hiring
  33. Absolute Return
  34. Where Facebook's half a billion users reside
  35. Absolute Return
  36. A quick tour through next-gen Payments Systems
  37. Hedge Fund Performance - Q2/2010
  38. Absolute Return
  39. How to nurture Data Scientists
  40. Absolute Return
  41. Popular iPhone games stay highly-ranked only for a few weeks
  42. Absolute Return
  43. Absolute Return
  44. Hedge Fund Performance - May/2010
  45. Absolute Return
  46. Absolute Return
  47. What every aspiring Investment Banker should know about Wall Street
  48. Absolute Return
  49. The stock selection skills of Long/Short Equity Managers
  50. Absolute Return
  51. Absolute Return
  52. Conversations for a Better World
  53. Hedge Fund Performance - April/2010
  54. Absolute Return
  55. Hess Collection
  56. Actually half of all iPad Books are Fiction
  57. Absolute Return
  58. A tour of Experimental and Development Economics for Data Scientists
  59. Quants: The documentary
  60. A third of all iPad books are fiction
  61. Absolute Return
  62. Mobile Voice 2010: Big Data shakes up the Speech Industry
  63. ad:tech 2010
  64. Price of the Top 50 iPad Books
  65. Absolute Return
  66. Text Mining and Twitter
  67. Big Data Analytics for Data Scientists and Business Analysts
  68. Spring Cleaning
  69. Hedge Fund Performance - Q1/2010
  70. Chirp 2010: Twitter By The Numbers
  71. Absolute Return
  72. Absolute Return
  73. Where 2.0
  74. Absolute Return
  75. Semi-regular Dimes
  76. Absolute Return
  77. Acupuncture Works
  78. Semi-regular Dimes
  79. Absolute Return
  80. Hedge Fund Performance - February 2010
  81. Greg Boyle Stays the Course
  82. Absolute Return
  83. An Attack to De-Anonymize Social Network Users
  84. Repurposing a Traffic Sign
  85. RSA 2010
  86. Economic Conditions and Employment History
  87. The First Family of Surfing and Garbage Warrior
  88. Quantifying Losses with Maximum Drawdowns
  89. The Coming Restructuring in Venture Capital
  90. Hedge Fund Performance - January 2010
  91. World Music and Culture + the Death Penalty
  92. Convertible Arbitrage and Liquidity Risks
  93. When The Opposite is True
  94. Semi-regular Dimes
  95. Risk-adjusted Hedge Fund Performance
  96. Semi-regular Dimes
  97. 2009 Hedge Fund Performance Summary

2009 102

  1. Semi-regular Dimes
  2. Semi-regular Dimes
  3. Pushcart School Founder is CNN's 2009 Hero of the Year
  4. Investigative Journalists Take Aim At Bankers
  5. Semi-regular Dimes
  6. 2007 Government Taxation
  7. Semi-regular Dimes
  8. Semi-regular Dimes
  9. Semi-regular Dimes
  10. Repurposing a Billboard
  11. 2009 Web 2.0 Summit
  12. Two Documentaries Set In Africa
  13. Hedge Fund Performance Index: September 2009
  14. Semi-regular Dimes
  15. Oracle World 2009
  16. Flying with the Birdmen
  17. Scenes from Open Studio: October 2009
  18. Semi-regular Dimes
  19. Hundred Year Flood in the Philippines
  20. End of September 2009
  21. In Perspective: Long-term Returns from Real Estate
  22. Semi-regular Dimes
  23. Hedge Fund Performance Index: August 2009
  24. Semi-regular Dimes
  25. Power to the Peaceful 2009
  26. Semi-regular Dimes
  27. Labor Day Weekend 2009
  28. Top 30 US Universities: Washington Monthly Rankings
  29. Foo Camp 2009
  30. Semi-regular Dimes
  31. SF Zine Fest 2009
  32. Family and Education: Revisiting an American success story
  33. When it comes to Musical Chops, the World Is Flat
  34. Semi-regular Dimes
  35. Hedge Fund Performance Index: July 2009
  36. di Rosa Preserve
  37. Semi-regular Dimes
  38. Semi-regular Dimes
  39. Semi-regular Dimes
  40. Semi-regular Dimes
  41. Semi-regular Dimes
  42. Semi-regular Dimes
  43. OSCON 2009 (and more)
  44. Semi-regular Dimes
  45. Hedge Fund Performance Index: June 2009
  46. Scifoo 2009
  47. Scifoo 2009 Photos
  48. Semi-regular Dimes
  49. Semi-regular Dimes
  50. Hedge Fund Performance Index: May 2009
  51. Semi-regular Dimes
  52. Semi-regular Dimes
  53. Semi-regular Dimes
  54. Semi-regular Dimes
  55. Semi-regular Dimes
  56. Bull Market Feedback Loops Can Work in Reverse
  57. Semi-regular Dimes
  58. Semi-regular Dimes
  59. Semi-regular Dimes
  60. Semi-regular Dimes
  61. Hedge Fund Performance Index: April 2009
  62. Semi-regular Dimes
  63. Semi-regular Dimes
  64. Semi-regular Dimes
  65. The Case-Shiller Housing Index
  66. Semi-regular Dimes
  67. Semi-regular Dimes
  68. Hedge Fund Performance Index: March 2009
  69. Semi-regular Dimes
  70. Semi-regular Dimes
  71. The Importance of the Dollar
  72. Semi-regular Dimes
  73. Semi-regular Dimes
  74. Hedge Fund Performance Index: February 2009
  75. Unit Root or Trend Stationary
  76. Semi-regular Dimes
  77. Two Charts: Unemployment and the S&P 500
  78. Semi-regular Dimes
  79. Semi-regular Dimes
  80. Oh Europe: Citizens Turned Currency Traders
  81. Equity or Debt
  82. Semi-regular Dimes
  83. Alternative Investments: Be Curious
  84. Semi-regular Dimes
  85. Gallup Poll of U.S. States
  86. Hedge Fund Performance Index: January 2009
  87. Pew/Economist Profile of the Middle-Class
  88. Greg Boyle and East LA's Finest Join the Clean Tech Revolution
  89. Semi-regular Dimes
  90. Occasional Dimes
  91. Alternative Investments: Avoid Complexity
  92. Sonification and the Stock Market
  93. Clones and Hedge Fund Replication
  94. Michael Lewis: Financial Planning for the Masses
  95. Alternative Investments: Maximize Transparency
  96. Conjoint Analysis and Implicit Biases
  97. David Swensen on Charlie Rose
  98. Alternative Investments: Don't Over-Rely on Outsiders (or a Few Insiders)
  99. 2008 Hedge Fund Performance Summary
  100. Mortgage Reform Ideas From Denmark
  101. Persistence of Hedge Fun Returns
  102. Subprimes and Excess in the Housing Market

2008 49

  1. On How AIG Got Deeply Involved With Credit Derivatives
  2. Merry Christmas
  3. Hedge Fund Performance Index: November 2008
  4. Bill Miller of Legg Mason
  5. Home Bias and Mutual Funds
  6. Bill Gross on the Q Ratio and More
  7. How Much Is Socially-Responsible Investing Costing You?
  8. Citigroup and CDO's
  9. S&P 500: 1825 to 2008
  10. Value + Momentum and Tactical Asset Allocation
  11. Asia Looks To Fare Better This Time Around
  12. The Current Crash and Other Bear Markets
  13. Volatility and Put Options
  14. Stanley Druckenmiller feels 'lucky' he didn't buy the Steelers
  15. Hedge Fund Performance Index: October 2008
  16. Measuring Spam Marketing Conversion
  17. A conversation with Bill Ackman
  18. Income Inequality and Social Mobility
  19. Michael Lewis on Wall St and Subprimes
  20. Exchange Rates and Forecasting Commodity Prices
  21. Google Docs and Web Surveys
  22. Web-based Hiring And Minorities
  23. Predictability of Currency Excess and Carry Trade Returns
  24. 2008 Polls and Home Prices By State
  25. Keep Your Keys In Your Pocket
  26. China: Inflation, College Grads and Municipal Budgets
  27. Limitations Of AIG's Credit Models
  28. What the U.S. Can Learn From Japan
  29. Total Tax Revenue as a Share of GDP
  30. Obama and McCain Should Ditch Their Health Care Plans
  31. Currency Carry Trades, Liquidity and Crash Risks
  32. The Unemployment Rate Over The Next 2 Years
  33. University Endowments: 1993 to 2005
  34. The Looming Credit-Card Disaster
  35. Oil and Stock Prices
  36. YOY Hedge Fund Returns: Managed Futures and Short-Sellers Lead Other Styles
  37. Unemployment Rates Following Housing Busts
  38. The Subprime Crisis Reviewed
  39. The Subprime Crisis Reviewed: CDOs
  40. Lehman Auction Update
  41. The Subprime Crisis Reviewed: RMBS
  42. The Styles They Are A-Changin'
  43. The Subprime Crisis Reviewed: Mortgage Loans
  44. Power Laws in Finance
  45. A Volatile Environment
  46. Valuing Credit Derivatives With (Implied) Copulas
  47. Value + Momentum Outperforms Value or Momentum Alone
  48. The Spread Between CD's and T-Bills
  49. The U.S. Subprime Crisis and Previous Banking Crises