Anaconda-Intel Data Science Solution Center

MLOps, like its counterpart DevOps, focuses on building and maintaining the machine learning pipeline across all the different stages in the lifecycle of AI applications.

No matter how in-demand data science is in the employment numbers, equal pressure is rising for data scientists to deliver business value.

There are two main factors that make Python a widely-used programming language in scientific computing.

The lack of defined, repeatable process for ML model operations may be part of the reason so many do not reach production.

R and Python are the two most widely used programming languages by data scientists worldwide.

A discussion about the challenges data scientists face, how Python helps, and the need for enterprise-class features and support.

XGBoost just keeps getting faster. The latest XGBoost (v1.1) can give up to 4x cost savings using state of the art CPU software optimizations.

Learn how to boost prediction quality and performance using Intel accelerated gradient boosting.

Faster experimentation with predictable behavior. Intel accelerated scikit-learn enables drop-in acceleration with deterministic, mathematical equivalence.

Predicting house prices using linear regression.

Cluster ever-growing amounts of data, faster and cheaper.

Here's what companies that want the real value data provides need to do to democratize data across the enterprise.

The biggest challenge when adopting open-source tools is balancing innovation and risk mitigation.

An integrated set of data analysis and AI development tools is essential for improving data scientists' productivity. Here's what to look for in solutions.

Here's how businesses can build a production-grade, enterprise-ready, end-to-end solution to harness real data.