Detecting fraudulent patterns at scale is a challenge, no matter the use case. The massive amounts of data to sift through, the complexity of the constantly evolving techniques, and the very small number of actual examples of fraudulent behavior are comparable to finding a needle in a haystack while not knowing what the needle looks like. In the world of finance, the added concerns with security and the importance of explaining how fraudulent behavior was identified further increases the complexity of the task.

This blog is part 1 of our two-part series Using Dynamic Time Warping and MLflow to Detect Sales Trends. To go to part 2, go to Using Dynamic Time Warping and MLflow to Detect Sales Trends.

This blog is part 2 of our two-part series Using Dynamic Time Warping and MLflow to Detect Sales Trends.

Data and AI are ushering in a new era of precision medicine. The scale of the cloud, combined with advancements in machine learning, are enabling healthcare and life sciences organizations to use their mountains of data—such as electronic health records, genomics, real-world evidence, claims, and more—to drive innovation across the entire ecosystem, from accelerating drug discovery to preventing chronic disease.

Today we are excited to announce Brickchain, the next generation technology for zettabyte-scale analytics, by harnessing all the compute power on the planet. Brickchain is the most scalable, secure, and collaborative data technology ever invented.

Now available on PyPi and with docs online, you can install this new release with pip install mlflow as described in the MLflow quickstart guide.

Since the completion of the Human Genome Project in 2003, there has been an explosion in data fueled by a dramatic drop in the cost of DNA sequencing, from $3B1 for the first genome to under $1,000 today.

Big data practitioners grapple with data quality issues and data pipeline complexities—it’s the bane of their existence. Whether you are chartered with advanced analytics, developing new machine learning models, providing operational reporting or managing the data infrastructure, the concern with data quality is a common theme. Data engineers, in particular, strive to design and deploy robust data pipelines that serve reliable data in a performant manner so that their organizations can make the most of their valuable corporate data assets.

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, we introduce how to build more reliable pipelines in Databricks, with the integration of Confluent Schema Registry. This feature is available since Databricks Runtime 4.2.

We are excited to announce the release of Databricks Runtime 5.2 for Machine Learning. This release includes several new features and performance improvements to help developers easily use machine learning on the Databricks Unified Analytics Platform.