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?
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?
Deep learning use cases are one of the toughest to tackle, and the complexities of this subset of ML need some mitigation. Here's how MLRun can do just that, automating and orchestrating the entire DL pipeline.
The cool thing about being in the ML industry for so long is that I have a front row seat to a fascinating market characterized by rapid innovation. So before we toast to a new (and better!) year ahead, here are my predictions of what awaits the ML industry in 2022.
Gone are the days when data science can safely remain in its own silo. Modern AI applications require a continuous operational pipeline and a production-first approach to make it all feasible.
ODSC West Reconnect is the place to be for MLOps, data science, and AI. Here are our top 6 recommended sessions for this year's conference.
An online feature store enables data scientists and ML engineers to harness real-time data, perform complex calculations in real time and make fast decisions based on fresh data. Here’s how to do real-time feature engineering with a feature store.