Sitemap - 2021 - TheSequence
🕋 8 Free ML courses – our favorites
🔮🤓 Some Non-Obvious ML Predictions for 2022
🚚 🚛 Edge#152: How DeepMind and Waymo Train Self-Driving Car Models?
👯♀️📦 Edge#151: Model Packaging
🥜 Meta’s Clever Idea to Handle Uncertainty in ML models
🏷 Data Labeling for ML, part 3
✨ Edge#150: Microsoft’s SynapseML is a New Framework for Large Scale Machine Learning
📺 3 tips to optimize your infrastructure for data-driven targeting*
🔎 Edge#149: Model Tracing and Lineage
🕹 A Massive Leap in ML for Gaming
📝 The Definitive ML Observability Checklist*
➗✖️ Edge#148: The OpenAI Model to Solve Text Math Problems
🎙 Doug Downey/Semantic Scholar: Applying Cutting Edge NLP at scale
🍮 Edge#147: MLOPs – Model Serving
📕 If only someone wrote the book on ML Observability*
🔺 Edge#146: A Deep Dive Into Arize AI ML Observability Platform
📝 Guest post: A Guide to Leveraging Active Learning for Data Labeling
🔬 Edge#145: MLOPs – model observability
👨🏼🎓👩🏽🎓 The Standard for Scalable Deep Learning Models
▪️▫️▪️▫️ Edge#144: How Many AI Neurons Does It Take to Simulate a Brain Neuron?
🎙 TheSequence Chat: a year of interesting conversations
🏗 Edge#143: Feature Stores in ML Pipelines: A Recap
♟♟ Chess Learning Explainability
🤖Edge#142: How Microsoft Built a 530 Billion Parameter Model
☝️ CoreWeave is allocating $50 million to scale the best compute infrastructure for ML*
🩺 Edge#141: MLOPs – Model Monitoring
🌟Share your opinion on MLOps and get rewarded*
☁️ Edge#140: cnvrg.io’s Metacloud aims to help AI developers to fight vendor lock-in
🎙 Brian Venturo/CoreWeave about GPU-first ML infrastructures
🔥 Edge#139: MLOps – one of the hottest topics in the ML space
➗✖️ OpenAI New NLP Challenge: Mathematical Reasoning
📝 Guest post: How to build SuperData for AI [Full Checklist]*
🏷 Edge#138: Toloka App Services Aims to Make Data Labeling Easier for AI Startups
📌 Event: MLOps Cocktails Done Right: How to Mix Data Science, ML Engineering, and DevOps*
🤓 Self-Supervised Learning Recap
🤔🤯 Addressing One of the Fundamental Questions in Machine Learning
🤩 Early access: try the world's most flexible AI cloud*
🔵⚪️Edge#136: Kili Technology and Its Automated Data-Centric Training Platform
🎙 Olga Megorskaya/Toloka: Practical Lessons About Data Labeling
👓 Edge#135: Self-Supervised Learning for Computer Vision
🎙 Rinat Gareev/Provectus About Data Quality and Enterprise ML Solutions
🔵🔴 Edge#134: Run:AI's 2021 AI Infrastructure Survey
📝 Guest post: 7 key considerations to develop a scalable annotation pipeline
🗣 Edge#133: Self-Supervised Learning for Speech
🗣🏎 The Race for Big Language Models Continues
🟠🟣 Edge#132: WhyLabs, AI Observability as a Service
🎙 Paroma Varma/Snorkel on programmatic approaches to data labeling
✍🏽 Edge#131: Self-Supervised Learning for Language
🧠🧠🧠 The Thousand Brains Theory, A New AI Book You Must Read
📝 Guest post: Data Aggregation is Unavoidable! (And Other Big Data Lies)
🧙🏻♂️ Edge#130: The ML Engineering Magic Behind OpenAI Codex
🐹 Edge#129: Self-Supervised Learning as Non-Contrastive Learning
🌋 The Biggest Problems in ML Safety
📌 Join us free for TransformX on Oct 6-7
🤖 Edge#128: Wu Dao – the Biggest Transformer Model in History
🎙 Judah Phillips / Squark about No-Code Predictive Analytics
🐼 Edge#127: How Contrastive Learning Influences Self-Supervised Learning methods
📌 Join us on Oct 6: Data Aggregation is Unavoidable! (And Other Big Data Lies)*
🔷🟥 Edge#126: Pachyderm 2 Brings New Data Capabilities to Accelerate your ML Lifecycle
📌 Event: Data Discovery and Observability for ML Engineers
⚡️Edge#125: Self-Supervised Learning as Energy Based Methods
👑 Big Tech and their Favorite Deep Learning Schools
🏷 Data Labeling for ML, part 2
🦾Transformer Architectures Recap
🎙 German Osin/Provectus About Data Discovery and Observability in ML Solutions
🌌 Edge#123: A New Series About Self-Supervised Learning
🏷🥊 The Fight Against Labeled Dataset Dependencies
🌟 Take part in the ML Insider Survey
🎭 Edge#122: Unified VLP is a Transformer Model for Visual Question Answering
🎙 Bryce Daines/CDS at Modulus Therapeutics: Using ML to Power Next Generation Cell Therapy
🕐🕚 Edge#121: Transformers and Time Series
🥗 Will Machine Learning Data Infrastructures Become Commoditized?
⚪️🟠️ Edge#120: How to Leverage Open-Source Data Labeling for your Business
⚒ Edge#119: Data Labeling – Build vs. Buy vs. Customize
🗄 ML to Power a New Generation of Databases
🔴 Cutting-Edge, No-Code Data Science: Powerful, Flexible, Nimble and Explainable AI Automation*
👯♀️ Edge#118: DeepMind Releases Two New Super Models to Handle Any Type of Dataset
☝️ The #1 Easy Way You Win Machine Learning*
👁 Edge#117: Transformers and Computer Vision
😱 Distributed ML Training is the Problem Everyone is Going to Have
📝 Guest post: Introducing Low-Latency Streaming Pipelines for Real-Time ML
💪🏻 Edge#116: AI2-Thor is an Open-Source Framework for Embodied AI Research
🎙 Greg Finak/ CTO of Ozette: using ML to extract intelligence from the immune system
🤩Edge#115: OpenAI GPT-3, OpenAI API for GPT-3; and how to make transformers more efficient
💻 OpenAI Codex, a Programming Challenge and one of the Most Impressive AI Demos Ever Created
👏 Edge#114: AI2’s Longformer is a Transformer Model for Long
🍢 Edge#113: Google BERT; TAPAS that Query Tabular Datasets; and AutoNLP
👾Transformers are Getting More Ambitious
🎙 Emad Elwany/CTO at Lexion on using deep learning to reimagine contract management systems
🤗 Edge#111: The concept of Attention; Google Switch Transformer; and Hugging Face
🔱 Triton: GPU Programming for Deep Neural Networks
🔷🟥 Edge#110: How The Pachyderm Platform Delivers the Data Foundation for Machine Learning
🤖 Edge#109: What are Transformers?
🛠 Introducing the Real World ML Section
⚪️⚫️ Edge#108: How to Improve Model Accuracy with Crowdsourced Data Labeling – Real World Use Cases
🎙Albert Azout/Level Ventures on the state of AI market and the areas to pay attention to
🗂 Edge#107: Crowdsourced vs. Automated vs. Hybrid Data Labeling
Edge#106: 💥The “What’s New in AI” recap#2️⃣
Edge#105: 💥The “What’s New in AI” recap#1️⃣
🔮 The Future of Deep Learning According to Three Legends 🧙🏻♂️ 🧙🏻♂️ 🧙🏻♂️
🔹🔸Edge#104: AllenNLP Makes Cutting-Edge NLP Models Look Easy
🏆📚 Reinforcement Learning Recap
👩🏻✈️The GitHub CoPilot Milestone
💥 Edge#102: DeepMind Redefines One of the Most Important Algorithms in ML as a Game
⛲️ The Importance of Open-Source ML Datasets
📌 Join us July 14th at MLCon – The AI & ML Developer Conference
Edge#💯: Will NetHack Challenge Become One of the Toughest RL Benchmarks in History?
🎙 Piero Molino on creating Ludwig and the Importance of Low-Code ML
🌐 Edge#99: What are Trust Region and Proximal Policy Optimization; PPO to master Dota2; and RLlib
🔥 A New Release of PyTorch is Here
🗿 Edge#98: OpenAI Built RL Agents that Mastered Montezuma’s Revenge by Going Backwards
⚽️ Edge#97: Policy Optimization in RL; how to master football with RL; and DeepMind’s bsuite
📲 Why Mobile Deep Learning is Tougher Than You Think
🎙Oren Etzioni/CEO of Allen Institute for AI (AI2) on advancing AI research for the common good
🚩 Edge#95: What is DQN; how DeepMind masters Quake III; and OpenAI Gym as a must-have tool
🖼 AI Incumbents and Their Favorite ML Frameworks
🔸◽️Edge#94: Determined AI Tackles the Monster Challenge of Distributed Training
🕵🏻♀️ Edge#93: Q-Learning, Google SEED RL architecture, and Facebook’s ReAgent
🔥 PyTorch is Getting Serious About the Enterprise
🤹♂️ Edge#92: Cogito Brings Human-in-the-Loop Data Annotation to Enterprises
🎙 Hyun Kim/CEO of Superb AI on true data labeling automation
🌊 Google’s New Wave of Machine Learning Capabilities
💥 Edge#90: OpenAI Safety Gym is an Environment to Improve Safety in Reinforcement Learning Models
⛑👑 Edge#89: Feature Learning – What Makes Some Features Better Than Others?
🧠 What is TheSequence? The guide to our content universe
🤓😡👹 Open Source ML from Large Tech Incumbents: The Good, The Bad and the Ugly
🎙H.O. Maycotte/CEO of Molecula on shifting from “data as fuel” to “features as fuel”
🧘♀️📚 Edge#87: Model-Based Reinforcement Learning, Google Dreamer, and Uber Fiber.
🤜🤛 AI/ML startups align to build a canonical stack and compete with the incumbents
🧠🤖 Edge#86: How DeepMind Prevents RL Agents from Getting "Too Clever"
🏆📚 Edge#85: Reinforcement Learning – very popular and yet misunderstood deep learning discipline
👀👀 Self-Supervised Learning is Making Inroads
⚪️⚪️🔵 Edge#84: Snorkel Flow – One of the Most Comprehensive ML Platforms on the Market
🎙 William Falcon: "We did our job right if the term MLOps disappears"
🥃 👯 Edge#83: One-Shot Learning, Siamese Networks, and ONNX standard
🛴🚲 The Race to Improve Reinforcement Learning
⚪️🔵 Edge#82: Fiddler is Bringing ML Monitoring to Enterprises
🎙 François Chollet: Keras, TensorFlow and New Ways to Measure Machine Intelligence
🥛 Edge#81: Zero-Shot Learning and How It Can Be Used
❇️ The Nvidia AI Network Effect Goes Beyond Hardware
📌 Event on April 21-22: apply() – the ML Data Engineering Conference
💻⚛️* Edge#80: Some Things You Should Know about TensorFlow Quantum
🥃🥃 Edge#79: Few-Shot Learning, Prototypical networks, and TorchMeta
Ⓜ️🌀 The MLOps Space is Getting Crowded and Confusing
🥗🥩 Edge#78: Feast is an Open Source, Lightweight Feature Store You Should Know About
🎙 Adam Wenchel/CEO of Arthur AI on ML explainability, interpretability, and fairness
🏗🏪 Edge#77: How Feature Stores Were Started
☝️⚖️ ML Fairness is Everybody’s Problem
🔎 👯♂️ Edge#76: Google’s Model Search is a New, Open-Source Framework for Finding Optimal ML Models
🥃📚 Edge#75: N-Shot Learning, how OpenAI Uses it; and learn2learn Meta-Learning Framework
🔆🔅 Go Big First, Then Compress
👯♀️👯 Edge#74: How Uber, Google, DeepMind and Microsoft Train Models at Scale
📌 Event: AI & Automation for Document Processing in Healthcare
🐍🦎 Edge#73: Meta-Learning and AutoML, OpenAI’s Reptile Model, and the Auto-Keras Framework
👉🕳👈 Closing the Gap Between Deep Learning Software and Hardware
🔴⚪️ Edge#72: Tecton – The Enterprise Feature Store Built by the Creators of Uber's ML Platform
🔎◼️ Edge#71: What is Differentiable Architecture Search?
🚜 🛵 Using Transformers in Mainstream Deep Learning Applications
//💨 Edge#70: Typed Features to Accelerate ML Experimentation at Scale
🎙 Manu Sharma/CEO of Labelbox about the future of data labeling automation
🔎🔍 Edge#69: Search Strategies in Neural Architecture Search
♨️ Making Sense of Microsoft’s Recent Machine Learning Announcements
📕📖📗 Natural Language Understanding Recap
🔵🔴 Edge#68: Run:AI Decouples Machine Learning Pipelines from the Underlying Hardware
🔪 Edge#67: Dissecting Neural Architecture Search in the context of AutoML
👩🏽🔧👨🏻🔧 Continuous Data Improvements and ML Performance
🃏😎 Edge#66: Pluribus – superhuman AI for multiplayer poker
🏎🏎 The AI Chip Race is Getting More Specialized
🛠 Edge#64: The Architectures Powering ML at Google, Facebook, Uber, LinkedIn
🙈🙉🙊 GPT-3 and Large Language Models can Get Out of Control
👩💻 Edge#62: Data Discovery and Management Architectures at LinkedIn, Uber, Lyft, Airbnb and Netflix
🎙 Jan Beitner, Creator of PyTorch Forecasting
🏋️♂️🤸♀️Edge#61: Understanding AutoML and its Different Disciplines
🏷 🔥Training Data Labeling is One of the Hottest Markets in Machine Learning
⏳⌛️Time-Series Forecasting Wrap-Up
🕹🤖 Edge#60: Google’s Switch Transformer
☝️⏰ Edge#59: NeuralProphet, the final chapter on time-series
💸🤷🏽♂️ Running AI Compute Infrastructures Without Breaking the Bank
🎙 Jim Dowling/CEO Logical Clocks: The future of feature stores
🤖 🕕 Edge#57: Transformer Architectures for Time Series
🤓☝️The Need for Open-Source Datasets and Benchmarks
⚪️ ⚫️ Edge#56: DeepMind’s MuZero that Mastered Go, Chess, Shogi and Atari Without Knowing the Rules
⏳ Edge#55: DeepAR, multi-dimensional time-series forecasting, and Sktime
🎈 Are Feature Stores the Next Bubble in AI?
🎙 Krishna Gade/CEO Fiddler AI: Challenges with model explainability
♣️ Edge#54: Facebook ReBeL That Can Master Poker
🚜 Transformers Continue Setting Records
🤖 Edge#52: Google Meena That Can Chat About Anything
⏱ Edge#51: Arima, GluonTS, and AutoML for Time Series Forecasting
1️⃣2️⃣3️⃣ Three Data Science Trends that are Hard to Live Without in 2021