Announcing Databricks Runtime 4.3
I'm pleased to announce the release of Databricks Runtime 4.3, powered by Apache Spark. We've packed this release with an assortment of new features, performance improvements, and quality improvements to the platform. We recommend moving to Databricks Runtime 4.3 in order to take advantage of these improvements. In our obsession to continually improve our platform’s...
How to Use MLflow to Experiment a Keras Network Model: Binary Classification for Movie Reviews
In the last blog post, we demonstrated the ease with which you can get started with MLflow, an open-source platform to manage machine learning lifecycle. In particular, we illustrated a simple Keras/TensorFlow model using MLflow and PyCharm. This time we explore a binary classification Keras network model. Using MLflow’s Tracking APIs, we will track metrics—accuracy...
New Features in MLflow v0.5.2 Release
Today, we’re excited to announce MLflow v0.5.0, MLflow v0.5.1, and MLflow v0.5.2, which were released last week with some new features. MLflow 0.5.2 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 post, we’ll...
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...
A Guide to Data Science, Developer, and Deep Dive Talks at Spark + AI Summit Europe
In October 2012, Harvard Business Review put a spotlight on the data science career with a dedicated issue and a catchy claim: Data Scientist: The Sexiest Job of the 21st Century. Last year in October, five years on, Forbes recast an answer on Quora, Why Data Science Is Such A Hot Career Right Now? Recent...
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...
