Sitemap - 2022 - TheSequence

Edge 256: The Architecture and Methods Powering ChatGPT

Edge 255: Interpretability Methods: Accumulated Local Effects (ALE)

OpenAI Gets Into the Text-to-3D Game with Point-E

Edge 254: InstructGPT is the Model that Inspired the Famous ChatGPT

Edge 253: Interpretability Methods: Partial Dependence Plots

Security: The Most Ignored Area of MLOps

Edge 252: Another Foundation Super Model: Googleโ€™s DreamFusion Can Convert Text to 3D

Edge 251: Global Model-Agnostic Interpretability

Diplomacy: The AI Benchmark that Gets Us Closer to the Turing Test

๐Ÿš€๐Ÿš€ Edge#250: Meta AIโ€™s New Super Model: CICERO is Able to Negotiate and Cooperate with People

๐Ÿ”ฎ Edge#249: Model-Intrinsic vs. Post-Hoc Interpretability Methods

What a Week for Generative AI

๐Ÿš€๐Ÿš€ Edge#248: Foundation Models are Creating the Industrial Era of AI

๐Ÿ“ƒ Edge#247: Classifying ML Interpretability Methods

๐Ÿ“ Guest post: Burst Compute: Scaling Workloads Across Thousands of GPUs in the Cloud, Instantly*

๐Ÿค— Stable Diffusion v2

๐Ÿ“ Guest post: How to Succeed as an ML/AI Startup?

๐Ÿ‹๏ธโ€โ™‚๏ธ๐Ÿคผโ€โ™€๏ธ Edge#246: OpenAI Used These Best Practices to Mitigate Risks While Training DALL-E 2

๐Ÿ”ฎ Edge#245: A New Series About Machine Learning Interpretability

๐Ÿ“ Guest post: What is a Vector Database?*

๐ŸŒ… The Era of Foundation Models is Here

๐Ÿ“ Guest post: Using One Methodology to Solve Three Model Failure Modes

๐Ÿ—ฃ๐Ÿ‘ฅ Edge#244: This Google Model Combines Reasoning and Acting in a Single Language Model

๐Ÿ“Œ Event: apply(recsys)โ€”ML experts from Slack, ByteDance & more share their recommender system learnings

๐Ÿ”‚ Edge#243: Text-to-Image Synthesis Models โ€“ Recap

โ˜๏ธCoreWeave to Offer NVIDIA HGX H100 Supercomputers - Supporting Cutting Edge AI & ML Companies*

โœ‚๏ธโœ‚๏ธ ML Talent Layoffs and Priorities Reset

โœ–๏ธโž— Edge#242: Meta AI New Model can Solve International Math Olympiad Level Problems

๐Ÿช„๐Ÿ–ผ Edge#241: Emerging Capabilities of Text-to-Image Synthesis Models

๐Ÿคผ DALL-E API and the Open Source Model vs. API Debate

๐Ÿ“บ Edge#240: The AI Powering Imagen Video

๐ŸŒŠ Edge#239: What is Stable Diffusion?

๐Ÿ“Œ Galileo Demo Hour โ€“ Introducing Instant Data Debugging for NLP*

๐ŸŒ ๐ŸŽ‡ Generative AI for DevOps

๐Ÿšฉ Edge#238: The New ImageNet: DeepMindโ€™s New Perception Benchmark for Deep Learning Models

๐Ÿ“ Guest post: SuperData is the new oil โ€“ How to win the AI race in the 21st century*

๐ŸŽจ Edge#237: What is Midjourney?

๐Ÿ’ธ Generative AI Fundraising Momentum

๐Ÿ”ข Edge#236: Inside DeepMindโ€™s AlphaTensor

๐ŸŽ™Dmitrii Evstiukhin/Provectus: "Four Horsemen of AI Project Failure and How to Deal with Them"

๐Ÿฑ๐Ÿถ Edge#235: Understanding Meta AIโ€™s Make-A-Scene

๐ŸชŸ๐ŸชŸ DALL-E 2 is Coming to Azure! and Other Exciting Microsoftโ€™s ML Announcements

๐Ÿ“โžก๏ธ๐Ÿ“บ Edge#234: Inside Meta AIโ€™s Make-A-Video

โ›“ Check the first ML Value Chain Landscape shaped by you!

๐Ÿค– Edge#233: Understanding DALL-E 2

๐Ÿงฎ DeepMindโ€™s AlphaTensor can Discover New Math Algorithms

๐Ÿ“ Guest post: Key Challenges To Automated Data Labeling and How To Overcome Them*

๐Ÿ’๐Ÿปโ€โ™€๏ธ๐Ÿ’๐Ÿพ Edge#232: DeepMindโ€™s New Method for Discovering when an Agent is Present in a System

๐ŸŽ†๐ŸŒ† Edge#231: Text-to-Image Synthesis with GANs

๐Ÿ“Œ Event: Choosing the right feature store: Feast or Tecton?

๐Ÿ“ฝ๐ŸŽฅ Meta AIโ€™s Make-A-Video

๐Ÿ“ŒJoin industry leaders at the ML:Integrity conference / Oct 19

1๏ธโƒฃ0๏ธโƒฃ0๏ธโƒฃ0๏ธโƒฃ Edge#230: How Amazon Scaled Alexa to 1000 Languages

๐ŸŒ… Edge#229: VQGAN + CLIP

๐Ÿ“ Guest post: 4 Types of ML Data Errors You Can Fix Right Now*

โ‡๏ธ NVIDIA Continues Pushing AIโ€™s Boundaries

๐Ÿ‘พ Edge#228: How Amazon is Improving BERT-Based Models Used in Alexa

๐Ÿ“ Guest post: Unlock the Power of BLOOM With the Broadest Range of GPUs Served On-Demand*

๐Ÿ“ƒโžก๏ธ๐Ÿ–ผ Edge#227: Autoregressive Text-to-Image Models

๐Ÿฆ Follow us on Twitter

๐Ÿ”ฅ The PyTorch Foundation

๐Ÿ“Œ Event: Leverage your Snowflake, BigQuery, Redshift Data Warehouse with a Real-Time Feature Store / Sept 21

๐Ÿ—œ๐Ÿ—œEdge#226: DeepSpeed Compression, a new library for extreme compression of deep learning models

๐ŸŽ™Or Itzary/Superwise About Model Observability and Streamlining Large ML Projects

๐Ÿ˜ถโ€๐ŸŒซ๏ธ Edge#225: Understanding Latent Diffusion Models

โ†•๏ธโ†”๏ธTensorFlow 2.10 is Here

๐Ÿ“Œ Event: Learn strategies to scale your ML models using Kubernetes - SEP 14

๐Ÿค˜Edge#224: AlexaTM 20B is Amazonโ€™s New Language Super Model Also Capable of Few-Shot Learning

๐Ÿ—บ โ“What is the current ML value chain landscape? Help us shape it!

๐Ÿตโ˜•๏ธ Edge#223: Different Types of Diffusion

๐Ÿค–โž•๐Ÿ‘จโ€๐Ÿ’ปHuman-AI Collaborative Writing

๐Ÿ“ Guest post: How to Write Better Annotation Guidelines for Human Labelers: 4 Top Tips*

๐Ÿ“บ Edge#222: Inside Axion, The Feature Store Architecture Powering ML Pipelines at Netflix

๐ŸŽ™Fabio Buso about How Hopsworks Feature Store Became Fully Serverless

๐Ÿงช๐Ÿงช Edge#221: What are Diffusion Models

๐Ÿค–โž•๐Ÿ™Ž๐ŸฝAGI and Human Alignment

๐Ÿ“Œ Event: Data Validation for Enterprise ML using Great Expectations and Hopsworks Feature Store/ Aug 31

๐Ÿ™ Edge#220: Dive into Meta AIโ€™s Make-A-Scene, which pushes the boundaries of AI art synthesis

๐Ÿ“ Guest post: Your Fitbit for data and model health*

๐Ÿ“„โžก๏ธ๐Ÿ–ผ Edge#219: A New Series About Text-to-Image Models

๐Ÿฑ The Text-to-Image Synthesis Revolution

๐Ÿ“ Guest post: "ML Data": The past, present and future*

๐Ÿ—ฃ๐Ÿค– Edge#218: Meta AI's BlenderBot 3, A 175B Parameter Model that can Chat About Every Topic and Organically Improve Its Knowledge

๐Ÿ”‚ Edge#217: ML Testing Series โ€“ Recap

๐Ÿ“™ Free book: Meet the Data Science Innovators

๐Ÿ˜ด โŒ Donโ€™t Sleep on JAX

๐Ÿ“Œ Event: Last chance to register for conference on scalable AI โ€“ Aug 23-24 in San Francisco!

๐Ÿˆโ€โฌ› Edge#216: DeepMindโ€™s New Super Model can Generalize Across Multiple Tasks on Different Domains

๐Ÿ› Edge#215: Pre-Train Model Testing and the Pillars of Robust ML

๐Ÿท Data Labeling for ML: Survey

๐Ÿ—ฃ๐Ÿ—ฃ๐Ÿ—ฃAnother Amazing Week for Large Language Models

๐Ÿ“ Guest post: Auto Labeling to Power Insurance Automation: Quickly Label Quality Datasets*

๐Ÿ—บ Edge#214: NLLB-200, Meta AIโ€™s New Super Model that Achieved New Milestones in Machine Translations Across 200 Languages

๐Ÿฉบ Edge#213: Testing Trained Models

๐Ÿ“ Guest post: Using AI to Learn a Disentangled Gait Representation for Versatile Quadruped Locomotion*

๐Ÿงฌ DeepMindโ€™s AlphaFold Database

๐Ÿ“ Guest post: Getting ML data labeling right*

๐Ÿ‘ Edge#212: Inside the Masterful CLI Trainer, a low-code CV model development platform

๐ŸŽ™ Ran Romano/Qwak about bridging the gap between data science and ML engineering

๐Ÿคท๐Ÿป Edge#211: What to Test in ML Models

๐Ÿ—„ A Model Compression Library You Need to Know About

๐Ÿ“Œ Event: Join us for this live webinar to learn how Tide reduced model deployment time by 50%!

๐ŸŸขโšช๏ธ Edge#210: Hopsworks 3.0, Connecting Python to the Modern Data Stack

๐Ÿ“Œ Event: Join us at The Future of Data-Centric AI 2022 โ€” a free virtual event by Snorkel AI

๐Ÿ”‚ Edge#209: A New Series About ML Testing

๐Ÿ“Œ Event: A dive into continuous training automation โ€“ webinar by Superwise

๐Ÿค–๐Ÿ‘ฉ๐Ÿผโ€๐ŸŽจ Meta Steps Into Generative Art with Make-A-Scene

๐Ÿ’ฅEdge#208: Googleโ€™s Minerva Can Solve Mathematical and Scientific Problems Through Step-by-Step Reasoning

๐Ÿ“ Guest post: 5 Principles You Need To Know About Continuous ML Data Intelligence*

๐ŸŒ ๐Ÿ•ธ Graph Neural Networks Recap

๐Ÿ—ฃ๐Ÿ—ฃ๐Ÿ—ฃ No Language Left Behind

๐Ÿ“Œ Free 7-Day Trial of FeatureBase, the Real-Time Database for Continuously Changing Data

๐ŸŸฉโฌ›๏ธ Edge#206: OpenAIโ€™s New Transformer Model Mastered Minecraft by Using Unlabeled Videos

โ˜๏ธโš™๏ธ Edge#205: What is Graph Attention Network?

โ™ฆ๏ธโšก๏ธโ™ฆ๏ธ Databricks' New ML Announcements

๐Ÿ“ Guest post: How to Select Better ConvNet Architectures for Image Classification Tasks*

๐Ÿ“ƒ๐Ÿช„๐Ÿ–ผ Edge#204: Inside Imagen. Googleโ€™s Impressive Text-to-Image Alternative to OpenAIโ€™s DALLE-2

๐Ÿ”ด๐ŸŸจ Edge#203: What are Graph Recurrent Neural Networks?

๐Ÿ“ Guest post: Right-Sizing Training Workloads with NVIDIA A100 and A40 GPUs*

๐Ÿ“น ๐Ÿค– Transformers for Video

๐ŸŽ™ Orly Amsalem/ on building developer-first ML products

๐ŸŸขโšช๏ธ Edge#202: How to Ship ML-powered Apps with Baseten

๐ŸŽ™ Googleโ€™s Allen Day on Using ML in the Cryptocurrency Space

๐Ÿ’  Edge#201: Understanding Graph Convolutional Neural Networks

๐Ÿ“Œ Event: Explore the future of scalable AI & more at Ray Summit: August 23-24 in San Francisco!

๐Ÿ”ตโšช๏ธ The Alexa Factor

๐Ÿ“Œ Event: Discover What It Takes to Scale Innovation & Data Science

โบโžก๏ธโบ Edge#200: PyWhy is Microsoftโ€™s New Home for Causal Inference

๐Ÿ“Œ Event: Unstructured data summit to feature OpenAI, Hugging Face, the creator of UMAP & moreโ€ฆ

๐Ÿงฑ Edge#199: Building Blocks and Types of GNN Architectures

๐Ÿ“Œ Event: A guide to multi-tenancy architectures in ML โ€“ webinar by Superwise

๐Ÿ‘พ๐Ÿค– Simpler, More Efficient Transformers

ใ€ฐ๏ธ Edge#198:, Flexible and Expressive Tool for Experiment Tracking and Model Registry

๐ŸŒ Edge#197: Types of Graph Learning Tasks

โ“โ—๏ธMicrosoftโ€™s Causal Inference Just Got Better with PyWhy

๐Ÿ“ Guest post: How to Prioritize Data Quality for Computer Vision: An Expert Primer*

๐Ÿ‘คโš™๏ธ Edge#196: FLUTE is Microsoftโ€™s New Framework for Federated Learning

๐Ÿ“ Guest post: Prevent AI failure with data logging and ML monitoring*

๐Ÿ’  Edge#195: A New Series About Graph Neural Networks

๐ŸŸฅ๐ŸŸฉ๐ŸŸฆ๐ŸŸจ Microsoftโ€™s New ML Announcements

๐ŸŽ™ Mike Del Balso/CEO of Tecton about Operational ML and ML Flywheels

๐Ÿ‘ Edge#194: Masterful AI, the Training Platform for Automated Computer Vision

โœ… ML Training at Scale Recap

โ˜๏ธ๐Ÿ”๐Ÿ“ฑ The Most Important Federated Learning Framework

๐Ÿ“ Guest post: Fast Access to Feature Data for AI Applications with Hopsworks*

๐ŸŸง Edge#192: Inside Predibase, the Enterprise Declarative ML Platform

๐Ÿ“ Guest post: How to Measure Your GPU Cluster Utilization, and Why That Matters*

๐Ÿ“จ Edge#191: MPI โ€“ the Fundamental Enabler of Distributed Training

๐Ÿ“ŒEvent: Join the Largest Conference on MLOps: 3rd Annual MLOps World 2022! ๐ŸŽ‰

Googleโ€™s Big ML Week

๐Ÿ“Œ Last chance! Join us at apply() โ€“ the ML Data Engineering Conference

๐Ÿ”ฌ Edge#190: Continuous Model Observability With Superwise

๐Ÿ“ Guest post: It's Time to Use Semi-Supervised Learning for Your CV models*

๐Ÿšฐ Edge#189: What is Pipeline Parallelism?

๐Ÿ‘„ A New Open Source Massive Language Model

๐Ÿ“ Guest post: Active Learning 101: A Complete Guide to Higher Quality Data* (part 2)

๐Ÿง™๐Ÿปโ€โ™‚๏ธ Edge#188: Inside Merlin, the Platform Powering Machine Learning at Shopify

๐Ÿ“ Guest post: Testing feature logic, transformations, and feature pipelines with pytest*

๐Ÿฅข Edge#187: The Different Types of Data Parallelism

๐Ÿ“Œ Event: SuperAnnotateโ€™s Free Webinar Series on Automated CV Pipelines is Live

๐Ÿ”Ž๐Ÿง  Improving Language Models by Learning from the Human Brain

๐Ÿ“Œ Event: Understanding performance and availability for feature stores

๐Ÿ“ Guest post: 2022 State of Data Practice Report - Key Findings Revealed*

๐Ÿ— Edge#186: From Feature Stores to Feature Platforms

๐ŸŽ™ Hyun Kim/CEO of Superb AI About Challenges with Data Labeling in Computer Vision

๐Ÿ•ธ Edge#185: Centralized vs. Decentralized Distributed Training Architectures

๐Ÿฆพ Serverless ML Execution

๐Ÿ“Œ Event: Join us at apply() โ€“ the ML Data Engineering Community Conference

๐Ÿง‘โ€๐ŸŽจ Edge#184: Inside DALL-E 2: OpenAIโ€™s Upgraded Supermodel that can Generate Artistic Images from Text

๐Ÿ“ Guest post: How to setup MLOps at a reasonable scale: tips, tool stacks, and templates from companies that did

โš™๏ธ Edge#183: Data vs Model Parallelism in Distributed Training

๐Ÿ› Machine Learning at Shopify

๐Ÿท Data Labeling for ML, part 4

๐ŸŒฑ Edge#182: What Responsible AI starts with

Edge#181: ๐ŸŒ„ A New Series About High Scale ML Training

๐ŸŽฅ How to achieve 1M+ record/second Kafka ingest without sacrificing query latency

๐Ÿ’ƒ New Week, New AI Super Model

โœจ ML Performance Tracing: Your Key To Reducing MTTR*

๐Ÿ—‚ Edge#180: A Deep Dive Into SuperAnnotate, End-to-End Platform for Building and Managing SuperData, the Ground Truth of AI

๐Ÿ“Œ MLOps live: biweekly Q&A for people doing MLOps at a reasonable scale*

๐Ÿคผ GANs Recap

๐Ÿ“ŒJoin us at Rev 3, the #1 MLOps conference

๐Ÿ‘พ ML that Improves Code Writing

๐Ÿ“ Guest post: Active Learning 101: A Complete Guide to Higher Quality Data* (part 1)

๐ŸŸ ๐Ÿ‘€ Edge#178: Supporting the Fight Against Illegal Fishing with AI

๐ŸŽ™ Piotr Niedzwiedz, neptune's CEO on Ideas About Machine Learning Experimentation

๐ŸŒ…๐Ÿž Edge#177: An Overview of StackGANs

๐Ÿ’ป Another NVIDIA AI Week

๐Ÿ“ Guest post: How to Build an ML Platform from Scratch*

๐Ÿง  Edge#176: Metaโ€™s New Architecture for Build AI Agents that Can Reason Like Humans and Animals

๐Ÿ“ Guest post: You are probably doing MLOps at a reasonable scale. Embrace it*

๐Ÿ‘ฉ๐Ÿผโ€๐ŸŽจ Edge#175: Understanding StyleGANs

๐Ÿ“Œ Remember in-person events? This oneโ€™s worth the wait

๐ŸญThe ML Hardware Virtualization Layer

๐Ÿ“ Guest post: Beating the Challenges of AI Inference Workloads in the Cloud*

๐Ÿ“œ Edge#174: How DeepMind Uses Transformer Models to Help Restore Ancient Inscriptions

๐ŸŽ™Jeff Hawkins, author of A Thousand Brains, about the path to AGI

โž— Edge#173: Exploring Conditional GANs

๐Ÿ”ฆ PyTorchโ€™s New Release

๐Ÿ“Œ Time is running out to register for Arize:Observe

๐ŸŒจ Edge#172: DeepMind's Deep Generative Model of Rain (DGMR)

๐Ÿ“Œ SuperAnnotate Launches Free Webinar Series on Automated CV Pipelines | Register Now

๐Ÿš Edge#171: What are Deep Convolutional GANs?

๐Ÿ›ฐ Is Computer Vision About to Have its GPT-3 Moment?

๐Ÿ”ถโ—ฝ๏ธEdge#170: Superb AI, a DataOps Platform to Automate Data Preparation

๐Ÿ” Edge#169: Understanding CycleGANs

๐Ÿง  Meta AI Ideas for Autonomous Intelligence

๐Ÿ“Œ Register for the inaugural ML observability summit featuring Chime, DoorDash, Etsy, Kaggle & moreโ€ฆ

๐Ÿ† Edge#168: OpenAIโ€™s GPT-3 Inspired Model can Solve Problems from the Math Olympiads

๐Ÿคผ Edge#167: What are Generative Adversarial Networks?

โš›๏ธ DeepMindโ€™s Push Into Real World ML

๐Ÿ“Œ Last chance! โ€“ Join us at mlcon 2.0โ€“ the free AI and ML developers conference

โœณ๏ธ Edge#166: DeepMind's AlphaCode Can Generate Code at the Level of Programming Competitions

๐ŸŽ™Ronen Dar, Run:AI's CTO, on managing computation resources in ML pipelines

๐Ÿ•ธ Edge#165: AutoRegressive Networks

๐Ÿšฉ Machine Learningโ€™s Last Mile Problem

๐Ÿ“ Guest post: The Rise of Shadow AI*

๐Ÿค– ๐Ÿ‘ถ Edge#164: Metaโ€™s Data2vec is a New Self-Supervised Model that Works for Speech, Vision, and Text

๐Ÿ“Œ Tomorrow! Feb 10 โ€“ Join us at apply() โ€“ the ML Data Engineering Community Meetup

โšง Edge#163: Understanding Variational Autoencoders

โœจ DeepMind and OpenAI Magical Week in AI

๐Ÿ“Œ Webinar: Four Requirements to Efficiently Deliver Real-Time Data at Scale

๐Ÿ”นโ—ฝ๏ธEdge#162: EleutherAIโ€™s GPT-NeoX-20B, one of the largest open-source language models

๐ŸŽ™SuperAnnotate's CTO Vahan Petrosyan on the present and future of ML data labeling

๐ŸŒ†๐Ÿ™ Edge#161: A New Series About Deep Generative Models

๐Ÿ’ป Metaโ€™s AI SuperComputer

๐Ÿ“ Guest post: The Original Open Source Feature Store - Hopsworks*

๐ŸŸข โšช๏ธEdge#160: A Deep Dive Into Aporia, the ML Observability Platform

๐Ÿ“ Guest post: Data Labeling and Its Role in E-commerce Today โ€“ Recent Use Cases*

๐Ÿ”„๐Ÿ”„ MLOps Full Recap

๐Ÿ‘ทโ€โ™€๏ธ๐Ÿง‘๐Ÿปโ€๐ŸŽ“๐Ÿ‘ฉโ€๐Ÿ’ป๐Ÿ‘จ๐Ÿปโ€๐Ÿซ The MoE Momentum

๐Ÿ“Œ Learn from 40+ AI experts at mlcon 2.0 ML dev conf <Feb22-23>

๐Ÿฅธ Edge#158: Microsoft KEAR is a Deep Learning Model for Common Sense Reasoning

๐ŸŽ™Yinhan Liu/CTO of BirchAI about applying ML in the healthcare industry

โžฐโžฐ Edge#157: CI/CD in ML Solutions

๐Ÿš˜ Uber Continues its Open-Source ML Traction

๐Ÿ“ฅ Download your AI Infrastructure report from Forrester Research*

๐Ÿ“Š ๐Ÿ‘ฉโ€๐Ÿ’ป๐Ÿฅธ Edge#156: The ML Powering LinkedInโ€™s Recruiting Recommendation System

๐Ÿ“Œ Event: Join us at apply() โ€“ the ML Data Engineering Community Meetup

๐Ÿ…ฐ๏ธ/๐Ÿ…ฑ๏ธ Edge#155: A/B Testing for ML Models

๐Ÿ‘๐Ÿ‘‚๐ŸปMulti-Modal Learning is Becoming Real

โ†—๏ธ Data Scientists, Youโ€™re Invited: Make 2022 a Year of Continuous Improvement

๐Ÿ‘ฏโ€โ™€๏ธ๐ŸŽฒ Edge#154: DeepMindโ€™s New Super Model that can Master Perfect and Imperfect Information Games

๐Ÿ‘ฅ Edge#153: ML Model Versioning