Coffee Sessions #27

Practical MLOps

A “Gift” from Above In this session, Demetrios and Vishnu got to spend time with the inimitable Noah Gift. Noah is a data science educator, who teaches at Duke, Northwestern, and many other universities, as well as a technical leader through his company Pragmatic AI Labs and past companies. HOW is as important as WHAT In our conversation, Noah eloquently pointed out the numerous challenges of bringing ML into production, and especially for making sure it's used positively. It’s not enough to train great models; it’s important to make sure they impact the world positively as their productionized. How models are used is as important as what the model is. Noah specifically commented on externalities and how’s it incumbent on all MLOps practitioners to understand the externalities created by their models.

Take-aways

MLOps, Kaizen, Cloud - What MLOps is, the motivation behind it, and why it’s the next frontier in applied machine learning.  - Learn how to harness cloud technologies like AWS AppRunner to deploy and monitor machine learning models in production. - Summary of use cases and challenges in MLOps, and how to begin the MLOps journey in your organization. SESSION OUTLINE:  Doing MLOps INTRODUCTION (5 minutes) In this section of your talk, DataCamp will set expectations for Q&A, engage the audience with questions on what they want out of this session, and introduce you! MOTIVATION (5-10 minutes) - Why do we need MLOps and what is it? - How the Covid19 crisis revealed the need for MLOps  - How to get started with MLOps - What are major MLOPs platforms DEEP-DIVE (30-35 minutes) I - Doing MLOps - Learn to set up a Python project for CI/CD - Setup project scaffolding:  Makefile, tests, and linting - Configure testing with Github Actions - Learn to build Microservices using Python MLOps Cookbook  - Setup Python command-line tools - Setup Python Flask Microservice - Learn to deploy to AWS Cloud using AWS App runner - Setup an AWS App Runner project - Finalize a Continuous Deployment project - Verify a Machine Learning Prediction works CLOSING TALK & Q&A (10-15 minutes) - Summary of use cases and challenges - How to implement MLOps in your organization - Learn to implement CI/CD - Learn to perform Data Engineering Best Practices - Use KaizenML as a best practice - Q&A

In this episode

Noah Gift

Noah Gift

Founder Pragmatic AI Labs & Lecturer Duke/Northwestern Data Science, Pragmatic AI Labs

Noah Gift is the founder of Pragmatic A.I. Labs. Noah Gift lectures at MSDS, at Northwestern, Duke MIDS Graduate Data Science Program, the Graduate Data Science program at UC Berkeley, the UC Davis Graduate School of Management MSBA program, UNC Charlotte Data Science Initiative and University of Tennessee (as part of the Tennessee Digital Jobs Factory). He teaches and designs graduate machine learning, MLOps, A.I., Data Science courses, and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow. He works extensively with AWS and is an AWS Machine Learning Hero. He currently holds the following industry certifications for AWS: AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, and AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, AWS Academy Accredited Instructor, AWS Faculty Ambassador. He also is certified on both the Google and Azure platform: Google Certified Professional Cloud Architect, Certified Microsoft MTA on Python. He has published over 100 technical publications, including multiple books on subjects ranging from Cloud Machine Learning to DevOps. These works appear in Forbes, IBM, Red Hat, Microsoft, O’Reilly, Pearson, Udacity, Coursera, datascience.com, and DataCamp. Workshops and Talks worldwide for organizations, including NASA, PayPal, PyCon, Strata, O’Reilly Software Architecture Conference, and FooCamp. As an SME on Machine Learning for AWS, he helped created the AWS Machine Learning certification. He has worked in roles ranging from CTO, General Manager, Consulting CTO, Consulting Chief Data Scientist, and Cloud Architect. This experience has been with a wide variety of companies: ABC, Caltech, Sony Imageworks, Disney Feature Animation, Weta Digital, AT&T, Turner Studios, and Linden Lab, and industries: Television, Film, Games, SaaS, Sports, Telecommunications. He has film credits in many major motion pictures for technical work, including Avatar, Spider-Man 3, and Superman Returns. He has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had a global scale. He is currently consulting startups and other companies on Machine Learning, Cloud Architecture, and CTO level consulting as the founder of Pragmatic A.I. Labs. His most recent books are: Pragmatic A.I.: An introduction to Cloud-Based Machine Learning (Pearson, 2018) Python for DevOps (O’Reilly, 2020). Cloud Computing for Data Analysis, 2020 Practical MLOps (O'Reilly, 2021 est.)

LinkedIn

Demetrios Brinkmann

Demetrios Brinkmann

Host

Demetrios is one of the main organizers of the MLOps community and currently resides in a small town outside Frankfurt, Germany. He is an avid traveller who taught English as a second language to see the world and learn about new cultures. Demetrios fell into the Machine Learning Operations world, and since, has interviewed the leading names around MLOps, Data Science, and ML. Since diving into the nitty-gritty of Machine Learning Operations he felt a strong calling to explore the ethical issues surrounding ML. When he is not conducting interviews you can find him making stone stacking with his daughter in the woods or playing the ukulele by the campfire.

Vishnu Rachakonda

Vishnu Rachakonda

Host

Vishnu Rachakonda is the operations lead for the MLOps Community and co-hosts the MLOps Coffee Sessions podcast. He is a machine learning engineer at Tesseract Health, a 4Catalyzer company focused on retinal imaging. In this role, he builds machine learning models for clinical workflow augmentation and diagnostics in on-device and cloud use cases. Since studying bioengineering at Penn, Vishnu has been actively working in the fields of computational biomedicine and MLOps. In his spare time, Vishnu enjoys suspending all logic to watch Indian action movies, playing chess, and writing.