Engineering Blog › Machine Learning

The proliferation of subscription models has increased across industries: from direct-to-consumer brands for shaving supplies and prepared meals to streaming media services, at-home fitness, auto insurance and even automobiles themselves. Consumers are flocking to these new offerings while moving away from long-term contracts, which for subscription-based businesses means they have to prove their value to...

Download the notebooks to demo the solution covered below The subscription model is experiencing a renaissance.  Gone are the days of the penny music CD clubs, replaced by an ever-increasing assortment of digital streaming services delivering music, videos and more directly to consumers’ devices in exchange for a modest recurring fee. Today, 70% of US...

Watch the on-demand webinar Alternative Data Analytics with Python for a demonstration of the solution discussed in this blog and/or download the following notebooks to try it yourself. Stock Analysis - Plant-based Meat Historical Data GDELT News Source In Lakehouse Text Analytics on GDELT Alternative Data Time Series Foot Traffic Forecasting Introduction Why Alternative data...

The future of finance goes hand in hand with social responsibility, environmental stewardship and corporate ethics. In order to stay competitive, Financial Services Institutions (FSI)  are increasingly  disclosing more information about their environmental, social and governance (ESG) performance. By better understanding and quantifying the sustainability and societal impact of any investment in a company or...

Databricks is pleased to announce the release of Databricks Runtime 7.0 for Machine Learning (Runtime 7.0 ML) which provides preconfigured GPU-aware scheduling and adds enhanced deep learning capabilities for training and inference workloads. Preconfigured GPU-aware scheduling Project Hydrogen is a major Apache Spark™ initiative to bring state-of-the-art artificial intelligence (AI) and Big Data solutions together....

Databricks MLflow Model Serving provides a turnkey solution to host machine learning (ML) models as REST endpoints that are updated automatically, enabling data science teams to own the end-to-end lifecycle of a real-time machine learning model from training to production. When it comes to deploying ML models, data scientists have to make a choice based...

Download the Customer Lifetimes Part 2 notebook to demo the solution covered below, and watch the on-demand virtual workshop to learn more. You can also go to Part 1 to learn how to estimate customer lifetime duration. 20% of your customers account for 200% of your profits. You read that correctly. This seems a mathematical...

Today we announce the release of %pip and %conda notebook magic commands to significantly simplify python environment management in Databricks Runtime for Machine Learning. With the new magic commands, you can manage Python package dependencies within a notebook scope using familiar pip and conda syntax. For example, you can run %pip install -U koalas in...

Before you can provide personalized services and offers to your customers, you need to know who they are. In this virtual workshop, retail and media experts will demonstrate how to build advanced customer lifetime value (CLV) models. From there companies can provide the right investment into each customer in order to create personalized offers, save...

Genetic analyses are a critical tool in revolutionizing how we treat cancer. By understanding the mutations present in tumor cells, researchers can gain clues that lead to drug targets and eventually new therapies. At the same time, genetic characterizations of individual tumors enables physicians to tailor treatments to individual patients and improve outcomes while reducing...