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Tag: data drift

Algorithmia, Datadog Team on MLOps

Tools continue to be introduced to allow machine learning developers to monitor model and application performance as well as anomalies like model and data drift—a trend one market tracker dubs “ModelOps.” The la Read more…

Staying On Top of ML Model and Data Drift

A lot of things can go wrong when developing machine learning models. You can use poor quality data, mistake correlation for causation, or overfit your model to the training data, just to name a few. But there are also a Read more…

Streamsets Gets $35M for DataOps

StreamSets, which bills itself as the "air traffic control" tasked with preventing collisions from occurring with big data, today announced that it raised $35 million, which it will use to continue building its data oper Read more…

Keeping on Top of Data Drift

Data is often thought to be constant and immutable. A given piece of data is defined by 1s and 0s, and it never changes. But there's an emerging school of thought in the big data world that sees data as constantly drifti Read more…

Finding a Single Version of Truth Within Big Data

There seems to be an implicit promise associated with the rise of big data analytics: By taking more measurements and calculations, that we can deliver deeper insights atop source data, and do so at quicker intervals tha Read more…

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