Operationalizing AI: Out of the Lab and Into Production

Join us for this free 1-hour webinar from GigaOm Research. The webinar features GigaOm analyst Andrew Brust and special guest Katie Gross from Dataiku, a leader across the entire AI lifecycle.

In this 1-hour webinar, you will discover:

• How data engineering and data quality tie into machine learning
• How to manage ML code granularly and rigorously, beyond the notebook
• What to monitor in your ML models, other than generic accuracy
• Examples of ML success metrics, across industries and use cases

Why Attend

Why do a significant chunk of data science projects never make it out of the lab, when AI’s business value comes from deploying machine learning (ML) models operationally? One big reason is the hype-driven motivation behind many AI initiatives, which often overlooks concrete use cases, business objectives and measurable outcomes. Another reason, though, is the lack of tooling and well-adopted practices around ML operations (MLOps).

ML development is a form of software development, and it ought to be pursued with the same rigor. Automation and practices around model training, testing, packaging, release, and even rollback are key. And once the model is deployed, monitoring it, retraining it, and testing for data drift are important too. An ad hoc machine learning workflow won’t provide this, but a full ML platform with true MLOps capabilities does.

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