04-27, 15:30–16:15 (America/Los_Angeles), Kodiak Theatre
We aim to start the tutorial by giving a glimpse into the basics of machine learning in Python. And also set up some context into MLOps. This will be purely theoretical and delivered in a lecture format.
Post this, we will focus on setting up UnionML and give a walkthrough of an end-to-end machine-learning example with the help of UnionML. This will be part theoretical and part student exercise. The learners will go through the step-by-step process as we cover this example.
The difficulty of transitioning from research to production is a prevalent issue in the machine learning development life cycle. An ML team may need to modularize and rework their code to work more effectively in production. Occasionally, depending on whether the application requires offline, online, or streaming predictions, this can necessitate re-implementing and maintaining feature engineering or model prediction logic in several locations.
The audience will learn about an open-source microframework for creating machine learning applications in this session. UnionML, developed by the Flyte team, offers a straightforward, user-friendly interface for specifying the fundamental components of your machine learning application, from dataset curation and sampling to model training and prediction. UnionML automatically generates the procedures required to fine-tune your models and release them to production in various prediction use cases, such as offline, online, or streaming settings using these building blocks. There will be a live demonstration by taking an end-to-end machine learning-based example written in Python.
We can look to the web for ideas while we consider a solution to this issue. For instance, the HTTP protocol, which provides a backbone of techniques with clearly defined but flexible interfaces, standardizes the way we move data across the internet. We were interested in posing the question, "What if we could develop, automate, and monitor data and ML pipelines at scale?" as machine learning systems proliferate across industries. https://github.com/unionai-oss/unionml
1 .Basic introduction to Machine learning in Python and MLOps - 5%
2. Setup of UnionML - 5%
- How to install UnionML and How is UnionML relevant towards end to end machine learning development
3. Stepwise walkthrough of UnionML covering dependencies, model training, and evaluation - 40%
- Input data to be used for training
- Model to be used for training
- Model Training
- Model Validation
4. Deployment using 1. FastAPI, 2. Serverless - 30%
5. MLOps with Flyte and managing MLOps with UnionML 20%
Previous knowledge expected
Shivay Lamba is a software developer specializing in DevOps, Machine Learning and Full Stack Development.
He is an Open Source Enthusiast and has been part of various programs like Google Code In and Google Summer of Code as a Mentor and has also been a MLH Fellow. He is actively involved in community work as well. He is a TensorflowJS SIG member, Mentor in OpenMined and CNCF Service Mesh Community, SODA Foundation and has given talks at various conferences like Github Satellite, Voice Global, Fossasia Tech Summit, TensorflowJS Show & Tell.