A data-driven approach to Environmental, Social and Governance
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...
Announcing GPU-aware scheduling and enhanced deep learning capabilities
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....
Announcing MLflow Model Serving on Databricks
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...
Customer Lifetime Value Part 2: Estimating Future Spend
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...
Simplify Python environment management on Databricks Runtime for Machine Learning using %pip and %conda
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...
On-Demand Virtual Session: Customer Lifetime Value
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...
Accelerating Somatic Variant Calling with the Databricks TNSeq Pipeline
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...
Modernizing Risk Management Part 2: Aggregations, Backtesting at Scale and Introducing Alternative Data
Understanding and mitigating risk is at the forefront of any financial services institution. However, as previously discussed in the first blog of this two-part series, banks today are still struggling to keep up with the emerging risks and threats facing their business. Plagued by the limitations of on-premises infrastructure and legacy technologies, banks until recently...
How the Minnesota Twins Scaled Pitch Scenario Analysis to Measure Player Performance – Part 1
Statistical Analysis in the Game of Baseball A single pitch in Major League Baseball (MLB) generates tens of megabytes of data, from pitch movement to ball rotation to hitter behavior to the movement of each individual baseball player in response to a hit. How do you derive actionable insights from all this data over the...
Customer Lifetime Value Part 1: Estimating Customer Lifetimes
Download the Customer Lifetimes Part 1 notebook to demo the solution covered below, and watch the on-demand virtual workshop to learn more. You can also go to Part 2 to learn how to estimate future customer spend. The biggest challenge every marketer faces is how to best spend money to profitably grow their brand....
