Practical MLOps, Doing MLOps
Noah Gift is the founder of Pragmatic A.I. Labs and lectures on cloud computing at top universities globally, including the Duke and Northwestern graduate data science programs. He designs graduate machine learning, MLOps, A.I., and data science courses, consults on machine learning and cloud architecture for AWS, and is a massive advocate of AWS Machine Learning and putting machine-learning models into production.
Noah has authored several books, including Practical MLOps, Pragmatic AI, Python for DevOps, and Cloud Computing for Data Analysis. He has created content around AWS for top course providers including Udacity, O'Reilly, Pearson, and DataCamp. You can find many AWS examples from Noah by following him on LinkedIn.
Noah Gift is the founder of Pragmatic A.I. Labs and lectures on cloud computing at top universities globally, including the Duke and Northwestern graduate data science programs. He designs graduate machine learning, MLOps, A.I., and data science courses, consults on machine learning and cloud architecture for AWS, and is a massive advocate of AWS Machine Learning and putting machine-learning models into production.
Noah has authored several books, including Practical MLOps, Pragmatic AI, Python for DevOps, and Cloud Computing for Data Analysis. He has created content around AWS for top course providers including Udacity, O'Reilly, Pearson, and DataCamp. You can find many AWS examples from Noah by following him on LinkedIn.
At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.
At the moment Demetrios is immersing himself in Machine Learning by interviewing experts from around the world in the weekly MLOps.community meetups. Demetrios is constantly learning and engaging in new activities to get uncomfortable and learn from his mistakes. He tries to bring creativity into every aspect of his life, whether that be analyzing the best paths forward, overcoming obstacles, or building lego houses with his daughter.
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.
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.
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.