BigML News, Issue #108, September 2022
 
Hi <<Full Name>>,

We are excited to present BigML Ops, the latest addition to our platform that lets you easily build, deploy, and operate the most advanced Machine Learning workflows at scale!

BigML Ops automates the entire Machine Learning lifecycle so you can focus on solving your business problems instead of building and maintaining your own ML Ops infrastructure. BigML Ops saves time with end-to-end automation, and boosts data-driven productivity by enabling more predictive use cases in production without having to add extra DevOps headcount. Thanks to its containerized design, BigML Ops embodies an end-to-end Machine Learning development, deployment, and lifecycle management process to enable reproducible, testable, and evolvable ML applications for enterprises at scale.
BigML Ops focuses on systematically operationalizing entire ML workflows (not only single models) with built-in reproducibility and traceability. We have essentially codified years of lessons learned in helping our enterprise customers into BigML Ops so any organization can operate thousands of simultaneous machine-learned models in a best practices manner. The following features are worth highlighting as they set BigML Ops apart from the rest:
The BigML Ops enabled containers provide endpoints for each of the individual models they may contain.
Each model is automatically paired with an anomaly detector that tracks the performance of that model and triggers events if and when certain thresholds are reached. 
All of these capabilities are provided in an easy and intuitive user interface that will allow you to create and operate hundreds of concurrent Machine Learning applications seamlessly.
Read this blog post for more details or contact us at info@bigml.com to schedule a demo!
Learn more about BigML Ops
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