How much math knowledge do you need for machine learning and deep learning? Some people say not much. Others say a lot. Both are correct, depending on what you want to achieve.

Machine learning models are mathematical representations of real-world processes that are used to make predictions, and are created by providing training data for an algorithm to learn from.

Amazon, Microsoft, Databricks, Google, HPE, and IBM machine learning toolkits run the gamut in breadth, depth, and ease ...

Data analytics developer Databricks Inc. today announced the general availability of Databricks Model Serving, a serverless real-time inferencing service that deploys real-time machine learning models ...

The partnership between Snowflake and Databricks is a welcome sign. It brings best of both the worlds through the combination of an enterprise data warehouse and predictive analytics platforms.

The Databricks MLflow: open source machine learning platform is now released as an alpha. It is built with an open interface and so designed to work with any ML library, algorithm, deployment tool ...

Machine learning algorithms. Machine learning depends on a number of algorithms for turning a data set into a model. Which algorithm works best depends on the kind of problem you’re solving, the ...

Databricks Simplifies Distributed Deep Learning in Runtime 5.0 Model experimentation usually takes place on a single-node machine, locally or in the cloud, before scaling out computation as needed.

This 30-session course covers a wide variety of topics in machine learning and statistical modeling. The primary goal is to provide participants with the tools and principles needed to solve data ...