Accurately Building Genomic Cohorts at Scale with Delta Lake and Spark SQL
This is the second post in our “Genomic Analysis at Scale” series. In our first post, we explored a simple problem: how to provide real-time aggregates when sequencing large volumes of genomes. We solved this problem by using Delta Lake and a streaming pipeline built using Spark SQL. In this blog, we focus on the more advanced...
Announcing the MLflow 1.0 Release
MLflow is an open source platform to help manage the complete machine learning lifecycle. With MLflow, data scientists can track and share experiments locally (on a laptop) or remotely (in the cloud), package and share models across frameworks, and deploy models virtually anywhere. Today we are excited to announce the release of MLflow 1.0. Since...
Hyperparameter Tuning with MLflow, Apache Spark MLlib and Hyperopt
Hyperparameter tuning is a common technique to optimize machine learning models based on hyperparameters, or configurations that are not learned during model training. Tuning these configurations can dramatically improve model performance. However, hyperparameter tuning can be computationally expensive, slow, and unintuitive even for experts. Databricks Runtime 5.4 and 5.4 ML (Azure | AWS) introduce new...
Enhanced Hyperparameter Tuning and Optimized AWS Storage with Databricks Runtime 5.4 ML
We are excited to announce the release of Databricks Runtime 5.4 ML (Azure | AWS). This release includes two Public Preview features to improve data science productivity, optimized storage in AWS for developing distributed applications, and a number of Python library upgrades. To get started, you simply select the Databricks Runtime 5.4 ML from the...
Introducing Databricks Runtime 5.4 with Conda (Beta)
We are excited to introduce a new runtime: Databricks Runtime 5.4 with Conda (Beta). This runtime uses Conda to manage Python libraries and environments. Many of our Python users prefer to manage their Python environments and libraries with Conda, which quickly is emerging as a standard. Conda takes a holistic approach to package management by...
Advanced Analytics with HyperLogLog Functions in Apache Spark
This is a community guest blog from Sim Simeonov, the founder & CTO of Swoop and IPM.ai. Pre-aggregation is a common technique in the high-performance analytics toolbox. For example, 10 billion rows of website visitation data per hour may be reducible to 10 million rows of visit counts, aggregated by the superset of dimensions used...
Efficient Databricks Deployment Automation with Terraform
Managing cloud infrastructure and provisioning resources can be a headache that DevOps engineers are all too familiar with. Even the most capable cloud admins can get bogged down with managing a bewildering number of interconnected cloud resources - including data streams, storage, compute power, and analytics tools. Take, for example, the following scenario: a customer...
Announcing General Availability of Managed MLflow on Databricks
MLflow is an open source platform to help manage the complete machine learning lifecycle. With MLflow, data scientists can track and share experiments locally or in the cloud, package and share models across frameworks, and deploy models virtually anywhere. Today at the Spark + AI Summit, we announced the General Availability of Managed MLflow on...
Koalas: Easy Transition from pandas to Apache Spark
Today at Spark + AI Summit, we announced Koalas, a new open source project that augments PySpark’s DataFrame API to make it compatible with pandas. Python data science has exploded over the past few years and pandas has emerged as the lynchpin of the ecosystem. When data scientists get their hands on a data set,...
Managing the Complete Machine Learning Lifecycle: On-Demand Webinar now available!
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,...
