AutoMLOps means automating engineering tasks so that your code is automatically ready for production. In this session, Yaron outlined the challenges, describe open-source tools available for Auto-MLOps, and more!

In this session, we will describe the challenges in operationalizing machine & deep learning. We’ll explain the production-first approach to MLOps pipelines - using a modular strategy, where the different components provide a continuous, automated, and far simpler way to move from research and development to scalable production pipelines. Without the need to refactor code, add glue logic, and spend significant efforts on data and ML engineering.

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?

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.

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.

Teams facing implementation challenges need a way to scale their operational pipelines, continuously roll out AI services faster, support real-time use cases and enable deployment in hybrid environments. The NetApp-AWS-Iguazio integrated FSx solution offers a one-stop-shop from storage to production, with full end-to-end MLOps capabilities—even at scale and in real-time.

Watch this MLRun tutorial with CTO and Co-Founder, Yaron Haviv to see a step by step tutorial of MLRun and how it's used to bring models to production.