Thread by @le_james94

1/ My notes from @scale_AI Transform✍️It covers:
- Building good data
- Future of ML frameworks
- Challenges for scalable deployment
- How to assess ML maturity

Thanks @alexandr_wang and team for organizing the best AI conference of 2021 thus far. Enjoy!

jameskle.com/writes/scale-t…

2/ @AndrewYNg: "The single most important thing that an MLOps team needs to do is to ensure consistently high-quality data throughout all stages of the ML project life cycle."

3/ @fchollet
The 4 #DeepLearning Trends That Keras Will Support:
- An Ever-Growing Ecosystem of Reusable Parts
- Increasing Automation
- Large-Scale Workflows In The Cloud
- Real-World Deployment

4/ @chuchenghsieh
The 5-Level Framework To Assess AI Maturity:
- Level 1: Conductor
- Level 2: Practitioner
- Level 3: Craftsman
- Level 4: Adventurist
- Level 5: Pioneer

5/ @snsf
The 5 Challenges In Deploying At Scale @facebookai:
- Scaling Training Data
- Efficient Training and Inference
- Building AI Platforms
- Developing AI Responsibly
- Creating Best Practices

6/ @andyfang:
@DoorDash uses AI to scale their marketplace and make it easier for customers to find what they want while making it easier for merchants to position themselves in an online world - ranging from creating a rich item taxonomy to building an optimized delivery menu.

7/ @soumithchintala
The 3 Possible Futures of ML Frameworks:
- Transformers + ConvNets = Stable Architectures
- Scaling a currently niche fundamentally different architecture to show disruptive results
- Data-efficient models via Priors

8/ Drago Anguelov of @Waymo:
- Most interesting perception problems lie in the long-tail.
- To build a robust system, we can build a network that gives a notion of its own confidence in its prediction.
- Waymo Open Dataset is a testbed for behavior prediction systems.

9/ @kevin_scott:
- AI can help tackle climate change, provide better healthcare, and redefine the workforce.
- There is an opportunity to build more efficient algorithms to train large self-supervised language models.
- We have an enormous amount of work to build Responsible AI.

10/ @jeremyphoward and @math_rachel
The 4 prongs of @fastdotai:
- Start with Education
- Move to Research
- Build Software to Implement the Research
- Community Made It Special

11/ There are other excellent sessions that I did not cover - fireside chats with @sama and @drfeifei + panel with builders @flexport, @aurora_inno, @Cruise, @iRobot, @huggingface, @brexHQ, @blendlabsinc, and more. So check them out!

12/ If you had experience using tools to support building high-quality training data for the modern AI/ML stack, please reach out to trade notes and tell me more!

DM open🙋

13/ Finally, congrats @scale_AI on the massive $325M Series E funding round!