How data engineers can leverage ML pipelines to support complex data management tasks across multiple compute environments, bringing ML applications to production faster and easier.
Data Science Salon wrapped up last week, with tons of insightful talks on a large range of topics in the Media, Arts and Entertainment industries. Check out our roundup of some noteworthy sessions.
How data engineers can leverage ML pipelines to support complex data management tasks across multiple compute environments, bringing ML applications to production faster and easier.
With MLOps you can deploy Python code straight into production without rewriting it, saving you time & resources without sacrificing accuracy or performance.
We are delighted to announce that Iguazio has been named a sample vendor in the 2020 Gartner Hype Cycle for Data Science and Machine Learning, as well as three additional Gartner Hype Cycles for Infrastructure Strategies, Compute Infrastructure and Hybrid Infrastructure Services, among industry leaders such as DataRobot, Amazon Web Services, Google Cloud Platform, IBM and Microsoft Azure.
The average for 1st day churn hovers at 70%. The solution? Predict user retention in the crucial first seconds and minutes after a new user onboards.
Effectively bringing machine learning to production is one of the biggest challenges that data science teams today struggle with. MLOps is the solution.
ML teams should be able to achieve MLOps by using their preferred frameworks, platforms, and languages to experiment, build & train their models.