The MLOps Blog

JUNE 27, 2023

As you delve into the landscape of MLOps in 2023, you will find a plethora of tools and platforms that have gained traction and are shaping the way models are developed, deployed, and monitored. Open-source tools have gained significant traction due to their flexibility, community support, and adaptability to various workflows.

Machine Learning 134

Machine Learning Machine Learning ML ML 134

phData

JUNE 26, 2023

Data Source Tool Updates The data source tool has a number of use cases, as it has the ability to profile your data sources and take the resulting JSON to perform whatever action you want to take. Explore phData Toolkit The post phData Toolkit June 2023 Update appeared first on phData.

SQL 52

SQL Data Profiling Data Pipeline Data Governance 52

Precisely

JUNE 26, 2023

This digital boom raised the stakes for cyber security and data privacy – and it’s easy to see why. Data theft, leaks, and breaches will cost companies an estimated $8 trillion in 2023. Regulatory and consumer scrutiny with respect to how companies manage personal data is on the rise.

Data Quality 52

Data Quality Data Silos Analytics Analytics 52

phData

DECEMBER 29, 2022

We hope you’ve had a fantastic holiday season, filled up on delicious food, and are as excited as us to kick off the 2023 calendar year. The phData Toolkit continues to have additions made to it as we work with customers to accelerate their migrations , build a data governance practice , and ensure quality data products are built.

SQL 52

SQL Database Database Administration Data Profiling 52

Snorkel AI

MAY 12, 2023

Our data teams focus on three important processes. First, data standardization, then providing model-ready data for data scientists, and then ensuring there’s strong data governance and monitoring solutions and tools in place. For example, where verified data is present, the latencies are quantified.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

Our data teams focus on three important processes. First, data standardization, then providing model-ready data for data scientists, and then ensuring there’s strong data governance and monitoring solutions and tools in place. For example, where verified data is present, the latencies are quantified.

Machine Learning 52

Machine Learning Machine Learning ML ML 52