Introducing mlflow-apps: A Repository of Sample Applications for MLflow
Introduction This summer, I was a software engineering intern at Databricks on the Machine Learning (ML) Platform team. As part of my intern project, I built a set of MLflow apps that demonstrate MLflow's capabilities and offer the community examples to learn from. In this blog, I'll discuss this library of pluggable ML applications, all...
100x Faster Bridge between Apache Spark and R with User-Defined Functions on Databricks
SparkR User-Defined Function (UDF) API opens up opportunities for big data workloads running on Apache Spark to embrace R's rich package ecosystem. Some of our customers that have R experts on board use SparkR UDF API to blend R's sophisticated packages into their ETL pipeline, applying transformations that go beyond Spark’s built-in functions on the...
Building a Real-Time Attribution Pipeline with Databricks Delta
In digital advertising, one of the most important things to be able to deliver to clients is information about how their advertising spend drove results. The more quickly we can provide this, the better. To tie conversions or engagements to the impressions served in an advertising campaign, companies must perform attribution. Attribution can be a...
Loan Risk Analysis with XGBoost and Databricks Runtime for Machine Learning
For companies that make money off of interest on loans held by their customer, it’s always about increasing the bottom line. Being able to assess the risk of loan applications can save a lender the cost of holding too many risky assets. It is the data scientist’s job to run analysis on your customer data...
MLflow 0.4.2 Released
Today, we’re excited to announce MLflow v0.4.0, MLflow v0.4.1, and v0.4.2 which we released within the last week with some of the recently requested features. MLflow 0.4.2 is already available on PyPI and docs are updated. If you do pip install mlflow as described in the MLflow quickstart guide, you will get the recent release. In this...
Get Certified on Apache Spark™ with Databricks
In a world of rapidly changing products, companies investing in technology need well-trained experts to run it. Certifications are a key differentiator in a competitive job market because they validate your skills and expertise while keeping you relevant. In fact, certifications may impact career growth more than degrees, since business leaders perceive them as more...
Processing Petabytes of Data in Seconds with Databricks Delta
Introduction Databricks Delta is a unified data management system that brings data reliability and fast analytics to cloud data lakes. In this blog post, we take a peek under the hood to examine what makes Databricks Delta capable of sifting through petabytes of data within seconds. In particular, we discuss Data Skipping and ZORDER Clustering....
MLflow v0.3.0 Released
Today, we’re excited to announce MLflow v0.3.0, which we released last week with some of the requested features from internal clients and open source users. MLflow 0.3.0 is already available on PyPI and docs are updated. If you do pip install mlflow as described in the MLflow quickstart guide, you will get the recent release....
Simplify Advertising Analytics Click Prediction with Databricks Unified Analytics Platform
Advertising teams want to analyze their immense stores and varieties of data requiring a scalable, extensible, and elastic platform. Advanced analytics, including but not limited to classification, clustering, recognition, prediction, and recommendations allow these organizations to gain deeper insights from their data and drive business outcomes. As data of various types grow in volume, Apache...
Simplify Streaming Stock Data Analysis Using Databricks Delta
Traditionally, real-time analysis of stock data was a complicated endeavor due to the complexities of maintaining a streaming system and ensuring transactional consistency of legacy and streaming data concurrently. Databricks Delta helps solve many of the pain points of building a streaming system to analyze stock data in real-time. In the following diagram, we provide...
