Using AutoML Toolkit’s FamilyRunner Pipeline APIs to Simplify and Automate Loan Default Predictions
Introduction In the post Using AutoML Toolkit to Automate Loan Default Predictions, we had shown how the Databricks Labs’ AutoML Toolkit simplified Machine Learning model feature engineering and model building optimization (MBO). It also had improved the area-under-the-curve (AUC) from 0.6732 (handmade XGBoost model) to 0.723 (AutoML XGBoost model). With AutoML Toolkit’s Release 0.6.1, we...
Simple, Reliable Upserts and Deletes on Delta Lake Tables using Python APIs
We are excited to announce the release of Delta Lake 0.4.0 which introduces Python APIs for manipulating and managing data in Delta tables. The key features in this release are: Python APIs for DML and utility operations (#89) - You can now use Python APIs to update/delete/merge data in Delta Lake tables and to run...
Diving Into Delta Lake: Schema Enforcement & Evolution
Data, like our experiences, is always evolving and accumulating. To keep up, our mental models of the world must adapt to new data, some of which contains new dimensions - new ways of seeing things we had no conception of before. These mental models are not unlike a table's schema, defining how we categorize and...
Engineering population scale Genome-Wide Association Studies with Apache Spark™, Delta Lake, and MLflow
The advent of genome-wide association studies (GWAS) in the late 2000s enabled scientists to begin to understand the causes of complex diseases such as diabetes and Crohn’s disease at their most fundamental level. However, academic bioinformatics tools to perform GWAS have not kept pace with the growth of genomic data, which has been doubling globally...
Using AutoML Toolkit to Automate Loan Default Predictions
Download the following notebooks and try the AutoML Toolkit today: Evaluating Risk for Loan Approvals using XGBoost (0.90) | Using AutoML Toolkit to Simplify Loan Risk Analysis XGBoost Model Optimization This blog was originally published on September 10th, 2019; it has been updated on October 2nd, 2019. In a previous blog and notebook, Loan Risk Analysis...
Productionizing Machine Learning with Delta Lake
Try out this notebook series in Databricks - part 1 (Delta Lake), part 2 (Delta Lake + ML) For many data scientists, the process of building and tuning machine learning models is only a small portion of the work they do every day. The vast majority of their time is spent doing the less-than-glamorous (but...
Announcing the Delta Lake 0.3.0 Release
We are excited to announce the release of Delta Lake 0.3.0 which introduces new programmatic APIs for manipulating and managing data in Delta tables. The key features in this release are: Scala/Java APIs for DML commands - You can now modify data in Delta tables using programmatic APIs for Delete (#44), Update (#43) and Merge...
Simplifying Streaming Stock Analysis using Delta Lake and Apache Spark: On-Demand Webinar and FAQ Now Available!
On June 13th, we hosted a live webinar — Simplifying Streaming Stock Analysis using Delta Lake and Apache Spark — with Junta Nakai, Industry Leader - Financial Services at Databricks, John O’Dwyer, Solution Architect at Databricks, and Denny Lee, Technical Product Marketing Manager at Databricks. This is the first webinar in a series of financial...
Understanding Dynamic Time Warping
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. The phrase “dynamic time warping,” at first read, might evoke images of Marty McFly driving his DeLorean at 88...
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. The phrase “dynamic time warping,” at first read, might evoke images of Marty McFly driving his DeLorean at 88 MPH in the Back to the Future series. Alas, dynamic time warping does not involve time travel; instead, it’s...