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?
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.
Enterprises should take a production-first approach to support the data science process as they mature and scale AI.
Data storytelling focuses on communicating insights to audiences through the use of compelling visuals and narratives. It can give new perspectives on increasingly complex, expanding and rapidly changing data sets.
Extend Kubeflow’s functionality by enabling small teams to build complex real-time data processing and model serving pipelines.