Keys to Building Machine Learning Systems
You need to be signed in to add a collection
In this session, you learn engineering techniques for building machine learning systems. Machine learning methods are capable of delivering immense business value. But machine learning is still not leveraged in many organizations. Engineering challenges and lack of software and systems experience are leading causes. The solution is to apply engineering practices to machine learning systems development end-to-end. Garrett Smith presents keys to successful ML systems development. These include methods for working with data scientists, common tool sets, and automation. You learn the importance of short, iterative release cycles for data science. You learn how to show business value early in a project. With this information, you're better equipped to build successful, high value data-enabled systems.
Transcript
In this session, you learn engineering techniques for building machine learning systems. Machine learning methods are capable of delivering immense business value. But machine learning is still not leveraged in many organizations. Engineering challenges and lack of software and systems experience are leading causes. The solution is to apply engineering practices to machine learning systems development end-to-end.
Garrett Smith presents keys to successful ML systems development. These include methods for working with data scientists, common tool sets, and automation. You learn the importance of short, iterative release cycles for data science. You learn how to show business value early in a project. With this information, you're better equipped to build successful, high value data-enabled systems.
About the speakers
Creator of Guild AI and Chicago ML