Systems | Development | Analytics | API | Testing

Best Practices for Succeeding with MLOps ft. Noah Gift - MLOps Live 18

As the MLOps practice matures, there is an accumulation of stories about what works well – and what doesn’t. If you’re building up your enterprise MLOps muscle, instead of trial and error, why not tap into the collective memory of thousands of organizations who have spent the last couple of years building their MLOps practices internally and learn from their experience?

From AutoML to AutoMLOps: Automated Logging & Tracking of ML - MLOps Live #19

In this session of the MLOps Live Webinar series, we discuss building services with ML baked-in, that continuously deliver bottom-line business value, by embracing AutoMLOps. AutoMLOps means automating engineering tasks so that your code is automatically ready for production. In this session, we outline the challenges, describe open-source tools available for Auto-MLOps, and finish off with a live demo.

Britive Provides a Unified Access Model Across Cloud and SaaS Platforms

Because conventional privileging and permissioning solutions were designed to run within on-premises data centers, they require dedicated IT security expertise to deploy and manage. Configuring them for multi-cloud environments is simply not workable. In this episode of “Powered by Snowflake,” Daniel Myers chats with Britive’s Field CTO John Morton, who explains how Britive has taken advantage of Snowflake’s compute and data-sharing capabilities to develop a solution that gives cloud-native, cloud-focused companies a new kind of cloud access management tool–one that easily scales as businesses grow and DevOps pipelines expand.

[MLOPS] From experiment management to model serving and back. A complete usecase, step-by-step!

The recording of our talk at the MLOps World summit. This talk covers a complete example, starting from experiment management and data versioning, building up into a pipeline and finally deploying using ClearML serving with drift monitoring. We then induce artifical drift to trigger the monitoring alerts and go back down the chain to quickly retrain a model and deploy it using canary deployment.

MLOps World Toronto: MLOps Beyond Training Simplifying and Automating the Operational Pipeline

Most data science teams start with building AI models and only think about operationalization later. But taking a production-first approach and automating components is the key to generating measurable ROI for the business. In this talk, Iguazio’s co-founder and CTO, Yaron Haviv, explains how to simplify and automate your production pipeline to bring data science to production faster and more efficiently. He displays real live use cases while going through all the different steps in the process.