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In less than a year, MLflow has reached almost 500K monthly downloads, and gathered over 80 code contributors and 40 contributing organizations, confirming the need for an open source approach to help standardize the machine learning lifecycle across tools, teams, and processes. We are thrilled to host some of our key contributors and customers next...

On March 7th, our team hosted a live webinar—Managing the Complete Machine Learning Lifecycle—with Andy Konwinski, Co-Founder and VP of Product at Databricks. In this webinar, we walked you through how MLflow, an open source framework for the complete Machine Learning lifecycle, helps solve for challenges around experiment tracking, reproducible projects and model deployment. Specifically,...

We are excited to announce the general availability (GA) of Databricks Runtime for Machine Learning, as part of the release of Databricks Runtime 5.3 ML. Built on top of Databricks Runtime, Databricks Runtime ML is the optimized runtime for developing ML/DL applications in Databricks. It offers native integration with popular ML/DL frameworks, such as scikit-learn,...

From Static Data Warehouse to Scalable Insights and AI On-Demand Government agencies today are dealing with a wider variety of data at a much larger scale. From satellite imagery to sensor data to citizen records, petabytes of semi- and unstructured data is collected each day. Unfortunately, traditional data warehouses are failing to provide government agencies...

Simplify building big data pipelines for change data capture (CDC) and GDPR use cases. Databricks Delta, the next-generation engine built on top of Apache Spark™, now supports the MERGE command, which allows you to efficiently upsert and delete records in your data lakes. MERGE dramatically simplifies how a number of common data pipelines can be...

Building production machine learning applications is challenging because there is no standard way to record experiments, ensure reproducible runs, and manage and deploy models. To address these challenges, last June we introduced MLflow, an open source platform to manage the ML lifecycle that works with any machine learning library and environment. The project has grown...

Continued Innovation and Expanded Availability for the Next-gen Unified Analytics Engine Databricks Delta the next generation unified analytics engine, built on top of Apache Spark, and aimed at helping data engineers build robust production data pipelines at scale is continuing to make strides. Already a powerful approach to building data pipelines, new capabilities and performance...

In the previous blog post, we introduced the new built-in Apache Avro data source in Apache Spark and explained how you can use it to build streaming data pipelines with the from_avro and to_avro functions. Apache Kafka and Apache Avro are commonly used to build a scalable and near-real-time data pipeline. In this blog post,...