Summary of my MLOps NYC talk, major AI/ML & Data challenges and how they will be solved with emerging open source technologies
Summary of my MLOps NYC talk, major AI/ML & Data challenges and how they will be solved with emerging open source technologies
Still waiting for ML training to be over? Tired of running experiments manually? Not sure how to reproduce results? Wasting too much of your time on devops and data wrangling?
2020 will be about simplifying the way from data science to production, with an emphasis on bringing real – and scalable - business value.
A step by step tutorial covering experiment tracking complexity concerns and how to solve them with MLRun, a new open source framework which optimizes the management of machine learning operations.
You’ve played around with machine learning, learned about the mysteries of neural networks, almost won a Kaggle competition and now you feel ready to bring all this to real world impact. It’s time to build some real AI-based applications.
Ever wonder if it’s possible to train machine learning (ML) models with regulated data which can’t be sent to the cloud? Has your edge solution gathered so much data that it just doesn’t make sense to send it all to
the cloud?