Practical MLOps, Doing MLOps
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.)
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.)
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., Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This training takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models. Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This training gives you a head start.