TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models

The past 12 months have revealed how valuable data science can be while also exposing its limitations. Expect big advances in the year to come.

The deep learning framework PyTorch has infiltrated the enterprise thanks to its relative ease of use.

Apple says the M1-compiled version of TensorFlow delivers several times faster performance on a number of benchmarks, while running existing TensorFlow scripts as-is

Filling a gap in their offerings, Nvidia hopes to gain money and market share with Arm's extensive reach

Tribuo offers tools for building and deploying classification, clustering, and regression models in Java, along with interfaces to TensorFlow, XGBoost, and ONNX

From government, health, and finance data to weather, baseball, and Star Trek, countless collections of free data are available to scratch your analytical itch

Data analytics migration to the cloud is now underway and is making on-premises data warehouses obsolete.

Finding success with data analytics requires good tools, good data management, and good strategy.

All 8 of these databases allow you to build machine learning models right where your data resides.