Tag: MLOps
Sep 11, 2020 |
The rapid maturation of machine learning operations (ModelOps) tools is leading to a “breakout year” for ModelOps, Forrester says in a recent report.
The ML lifecycle is a potential nightmare for many organizations, write Forrester analysts Mike Gualtieri and Kjell Carlsson in an August report, titled “Introducing ModelOps to Operationalize AI.”
“This process takes too long and is fraught with technical and business challenges, just with one model,” the analysts write. Read more…
Jul 7, 2020 |
As data science platforms expand across enterprise applications like predictive analytics, automated machine learning vendors are steadily integrating AI models with emerging infrastructure to ease deployment and orchestration.
For example, data science automation specialist dotData this week released a container-based machine learning model aimed at real-time prediction. Read more…
May 4, 2020 |
As a growing percentage of enterprise AI projects stall, data science platform vendors are teaming with cloud and data management specialists to move AI projects from the model-building stage to production workloads. Read more…
Jan 27, 2020 |
There’s no shortage of resources and tools for developing machine learning algorithms. But when it comes to putting those algorithms into production for inference, outside of AWS’s popular SageMaker, there’s not a lot to choose from. Read more…
Dec 13, 2019 |
Automated machine learning softer provider DataRobot yesterday announced the acquisition of Paxata, a provider of self-service data preparation and data fabrics. And it didn’t take DataRobot long to release the first new product based on the acquisition, called AI Catalog. Read more…
Dec 11, 2019 |
Just as operational standards have been established for data management via DataOps, the industry needs to create open standards for machine learning operations, or MLOps, according to Cloudera, which today unveiled a call to action to the community to begin having that discussion. Read more…
Feb 21, 2018 |
A startup named ParallelM today unveiled new software aimed at alleviating data scientists from the burden of manually deploying, monitoring, and managing machine learning pipelines in production.
Dubbed MLOps, ParallelM‘s software helps to automate many of the operational tasks required to turn a machine learning model from a promising piece of code running nn Spark, Flink, TensorFlow, or PyTorch processing engines into a secure, governed, and production-ready machine learning system. Read more…
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