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.
In this episode
Founder , Pragmatic AI Labs
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.
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.