🗺 ❓What is the current ML value chain landscape? Help us shape it!
Was this email forwarded to you? Sign up here Today we have a very special project for you – as we know you are really plugged into the ML world. We offer you to shape an objective landscape of the ML Value Chain. You've probably seen some AI/ML companies’ landscapes before. They are typically assembled by either analyst firms (e.g. CB Insights 100 AI Companies), or media (e.g. Forbes Top-50 AI Companies), or VC firms (e.g. FirstMark’s Machine Learning, AI and Data (MAD) Landscape). But we trust that TheSequence’s audience only can shape an accurate landscape of the ML Value Chain. Participate and be the first to receive this super useful research shaped by you! Some contextThe AI market is booming more now than ever, and the demand for environments that can support ML processes is on the rise. These processes tend to fall into six distinct stages of the ML value chain:
This is the beginning of the ML lifecycle. Several qualitatively different solutions that fulfill different needs are available on the market. Simply put, this stage is about obtaining raw and/or unstructured data.
This stage is about preparing raw data for labeling or testing. It may involve data cleaning, filtering, visualization, and searching for data insights through any number of methodologies.
Considering that 80% of all AI project time is spent managing data, this is one of the most decisive stages. Human-handled, automatic, and hybrid solutions exist.
Model training under supervised learning is about getting your model to learn a function by fitting an input to an output using an input-output example (this is where the labeled data comes in). The training model has to accurately predict the output. During evaluation, you assess the model’s correctness by using a separate dataset that wasn’t used in training.
Sadly, much research indicates that many if not most ML models never make it into production, because of using inferior data that renders training models and AI products inadequate.
This stage is about keeping a close eye on your ML model in order to avoid such things as model degradation, data drift or concept drift. It comes down to having your model perform at a satisfactory level in the long term. ProposalMany companies that offer ML services tend to offer them in some but not all stages of the chain; for example, some do only annotation, while others focus primarily on evaluation, deployment, and monitoring. Since most ML projects fail before deployment, we feel that there’s an unfulfilled need for a comprehensive, easy-to-navigate map that will showcase market solutions valued by the end user, making it easier for ML teams to reach their goals quickly and effectively. We’ve made the first version of the ML value chain landscape specifically for this project (it’s a compilation of existing landscapes), which is meant to serve as a starting point and help you shape your ideal landscape. It’s important to keep in mind that the landscape above is the compilation of existing research: we count on you to judge if it's accurate and help us shape a more precise picture. This version will change as more information and recommendations come from you. Share your insights and shape the best possible version of this landscape that reflects today’s ML value chain processes and needs fairly and accurately. Please do not share this compilation with anybody yet. With your help, we hope to make it more accurate. After analyzing all the responses and making adjustments, you will be the first to receive the accurate landscape of the ML Value Chain with a lot of useful insights. Thank you! 💜 You’re on the free list for TheSequence Scope and TheSequence Chat. For the full experience, become a paying subscriber to TheSequence Edge. Trusted by thousands of subscribers from the leading AI labs and universities. |
Key phrases
Older messages
🍵☕️ Edge#223: Different Types of Diffusion
Tuesday, September 6, 2022
+OpenAI's GLIDE; +the Hugging Face text-to-image catalog
🤖➕👨💻Human-AI Collaborative Writing
Sunday, September 4, 2022
Weekly news digest curated by the industry insiders
📝 Guest post: How to Write Better Annotation Guidelines for Human Labelers: 4 Top Tips*
Friday, September 2, 2022
In this guest post, Superb AI discusses the importance of manual annotation or the direct involvement of human labelers and shares 4 great tips on how to write better annotation guidelines for human
📺 Edge#222: Inside Axion, The Feature Store Architecture Powering ML Pipelines at Netflix
Thursday, September 1, 2022
The feature store is a key component of Netflix's ML Platform
🎙Fabio Buso about How Hopsworks Feature Store Became Fully Serverless
Wednesday, August 31, 2022
Getting to know the experience gained by researchers, engineers, and entrepreneurs doing real ML work is an excellent source of insight and inspiration. Share this interview if you like it. No
You Might Also Like
a16z’s Infrastructure team gets a new general partner
Friday, April 19, 2024
Post News is shutting down and Wall Street isn't feeling a Salesforce-Informatica pairing View this email online in your browser By Christine Hall Friday, April 19, 2024 Image Credits: Andreessen
New Roundtable! Additive for Mass Production Applications
Friday, April 19, 2024
The Outlook for the Future View this email in your browser engineering.com Roundtable - Additive for Mass Production Applications: The Outlook for the Future 6 Considerations for Choosing the Right
📷 What to Know About Macro Photography — Why You Should Buy a Budget Motherboard
Friday, April 19, 2024
Also: How to Automatically Highlight Values in Excel, and More! How-To Geek Logo April 19, 2024 📩 Get expert reviews, the hottest deals, how-to's, breaking news, and more delivered directly to your
Is the wind going out of the AI sails?
Friday, April 19, 2024
Rippling vacuums up venture capital and Ramp bags more millions View this email online in your browser By Haje Jan Kamps Friday, April 19, 2024 Image Credits: Getty Images / Carol Yepes Welcome to
Llama 3 is out - Weekly News Roundup - Issue #463
Friday, April 19, 2024
Plus: brand-new, all-electric Atlas; AI Index Report 2024; Microsoft pitched GenAI tools to US military; Humane AI Pin reviews are in; debunking Devin; and more! ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Daily Coding Problem: Problem #1417 [Easy]
Friday, April 19, 2024
Daily Coding Problem Good morning! Here's your coding interview problem for today. This problem was asked by Wayfair. You are given a 2 x N board, and instructed to completely cover the board with
Charted | How Hard Is It to Get Into an Ivy League School? 🎓
Friday, April 19, 2024
We detail the admission rates and average annual cost for Ivy League schools, as well as the median SAT scores required to be accepted. View Online | Subscribe Presented by: Discover the motivations
Dark Matter & Tortured Poets
Friday, April 19, 2024
New music releases aren't what they used to be -- for good and bad. Dark Matter & Tortured Poets By MG Siegler • 19 Apr 2024 View in browser View in browser New music releases in 2024 are a
Impact of AI on Product Management
Friday, April 19, 2024
Impact of AI on Product Management The rise of the AI Product Manager. Product managers have always championed customer's needs. However, with AI, the job requires new technical and ethical
⚙️ Zuck has entered the chat(bot)
Friday, April 19, 2024
Plus: AI video's coming to mobile!