If you’re going to use AI/ML to compete and win, you’ll need to optimize development productivity and make deployment, monitoring, and iterative improvement as frictionless and automated as possible. It’s no longer about getting ML to do cool stuff; it’s about making it do so repeatedly, reliably, and efficiently. In other words, it’s about production ML.

Getting there requires a full platform that supports data engineers and business users, in addition to data scientists and ML engineers. 

To learn more about the philosophy, technology, and methodology of production ML and some of the pitfalls to look out for, join us for this free webinar from GigaOm Research.

In this free 1-hour webinar, GigaOm Research Analyst Andrew Brust covers:

• Synergies between conventional analytics and AI/ML
• The importance of ML interpretability in its ethical application to business
• Why production ML platforms should be interoperable with other platforms
• The end-to-end production ML process, from data ingest to business impact

Why Attend

Enterprises and startups alike are moving past the novelty phase of artificial intelligence (AI) and machine learning (ML) into a mature phase of implementing it in a production capacity for real business.

If you’re going to use AI/ML to compete and win, you’ll need to optimize development productivity and make deployment, monitoring, and iterative improvement as frictionless and automated as possible.

It’s no longer about getting ML to do cool stuff; it’s about making it do so repeatedly, reliably, and efficiently. In other words, it’s about production ML.