Session + Live Q&A
The Unreasonable Effectiveness of Zero Shot Learning
A long and expensive phase of machine learning projects is collecting and labelling data, and training ML models. In this talk I will show how one can get started deploying models without requiring any data. We talk about what foundational models are, and go through examples of them, such as GPT-3 and OpenAI CLIP. We will then take a practical look at how you can use them for your own problems!
Speaker
Roland Meertens
Product Manager @annotell
Roland has a passion for Artificial Intelligence, and is specialised in robotics projects. He set up machine learning projects all the way from sensor selection, data collection and labelling to deploying the model in production. Please approach him to talk about deep learning and neural networks.
Read moreDate
Tuesday Nov 9 / 12:10PM EST (40 minutes)
Track
Topics
Machine LearningArtificial IntelligenceData EngineeringAdd to Calendar
Add to calendarShare
From the same track
Streaming-first Infrastructure for Real-time ML
Tuesday Nov 9 / 01:10PM EST
Because of data drift, the accuracy of an ML model degrades over time. How well a model performs depends on how often it’s updated. While companies like Alibaba, ByteDance, Google, Facebook have been able to leverage real-time pipelines to continually update many of their models in...
Chip Huyen
Founder at stealth startup & Teaching ML Sys @Stanford
What You Should Know Before Deploying ML in Production
Tuesday Nov 9 / 11:10AM EST
MLOps provides tools that make building, deploying, and maintaining machine learning solutions easier than ever before. However, MLOps is not only a static set of tools that defines the way you operationalize your machine learning models, but is also (and most importantly) about your organization...
Francesca Lazzeri
Principal Data Scientist Manager @Microsoft
ML Panel: "ML in Production - What's Next?"
Tuesday Nov 9 / 02:10PM EST
The panel will discuss the current lessons learned with putting ML systems into production.What is working and what is not working, from building ML teams, dealing with large datasets, governance and ethics/privacy issues, and what's around the corner for production ML, and ML in computing...
Chip Huyen
Founder at stealth startup & Teaching ML Sys @Stanford
Shijing Fang
Principal Data Scientist @Microsoft
Vernon Germano
Senior Manager of Machine Learning Engineering @zillow