Blog des ingénieurs › Data Science and ML

Behind the growth of every consumer-facing product is the acquisition and retention of an engaged user base. When it comes to customer acquisition, the goal is to attract high-quality users as cost effectively as possible. With marketing dollars dispersed across a wide array of different touchpoints -- campaigns, channels, and creatives -- measuring effectiveness is...

Our release of Databricks on Google Cloud Platform (GCP) was a major milestone toward a unified data, analytics and AI platform that is truly multi-cloud. Databricks on GCP, a jointly-developed service that allows you to store all of your data on a simple, open lakehouse platform, is based on standard containers running on top of...

This post is part of a series of posts on topic modeling. Topic modeling is the process of extracting topics from a set of text documents. This is useful for understanding or summarizing large collections of text documents.  A document can be a line of text, a paragraph or a chapter in a book. The...

Delta Lake and MLflow both come up frequently in conversation but often as two entirely separate products. This blog will focus on the synergies between Delta Lake and MLflow for machine learning use cases and explain how you can leverage Delta Lake to deliver strong ML results based on solid data foundations. If you are...

This is a guest post from the Stanford University Computer Science Department. We thank Daniel Kang, Deepti Raghavan and Peter Bailis of Stanford University for their contributions.   Machine learning (ML) models are increasingly used in a wide range of business applications. Organizations deploy hundreds of ML models to predict customer churn, optimal pricing, fraud...

Anti-Money Laundering (AML) compliance has been undoubtedly one of the top agenda items for regulators providing oversight of financial institutions across the globe. As AML evolved and became more sophisticated over the decades, so have the regulatory requirements designed to counter modern money laundering and terrorist financing schemes. The Bank Secrecy Act of 1970 provided...

Feature engineering is one of the most important and time-consuming steps of the machine learning process. Data scientists and analysts often find themselves spending a lot of time experimenting with different combinations of features to improve their models and to generate BI reports that drive business insights. The larger, more complex datasets with which data...

In a previous post, we looked at how to use PyMC3 to model the disease dynamics of COVID-19. This post builds on this use case and explores how to use Bayesian hierarchical models to infer COVID-19 disease parameters and the benefits compared to a pooled or an unpooled model. We fit an SIR model to...

This blog post is the second in a series on ML platforms, operations, and governance. For the first post, see Rafi Kurlansik’s post on the "Need for Data-centric ML Platforms." I recently spoke with a  Sr. Director of Data Platforms at a cybersecurity company, who commented, “I don’t understand how you can be future-proof for...

This blog is the first in a series on MLOps and Model Governance. The next blog will be by Joseph Bradley and will discuss how to choose the right technologies for data science and machine learning based on his experience working with customers. Introduction Recently, I learned that the failure rate for machine learning projects...