You’ve spent hundreds of hours cleaning your data, engineering features, and training and tuning your model to pinpoint accuracy. But now it’s time to deploy your model into production.
Whether you lead a team of data scientists or are one yourself, you know how time-consuming and problematic deployment can be. Language and environment incompatibilities, manual and duplicative processes, out-of-control costs, and poor communication can destroy all the work you’ve put into building models and slow your machine learning efforts.
Learning common deployment architectures for machine learning in the real world and understanding how to build your model for a production environment can help you avoid pitfalls when scaling up. Diego Oppenheimer (Algorithmia) discusses common problems and solutions and shares best practices from leading organizations that have solved the deployment headache.
This session was recorded at the 2019 O'Reilly Strata Data Conference in San Francisco.
Table of contents
- Title: Automating DevOps for machine learning
- Release date: October 2019
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 0636920339885
You might also like
Software Engineering at Google
Today, software engineers need to know not only how to program effectively but also how to …
High Performance Python, 2nd Edition
Your Python code may run correctly, but you need it to run faster. Updated for Python …
Deep Learning for Coders with fastai and PyTorch
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. …
O'Reilly Strata Data and AI Superstream
You’ll get access to O’Reilly data and AI experts. Deep dives into some of the hottest …