Data Science and Machine Learning Platforms: Should You Build or Buy?
This build-versus-buy whitepaper highlights the key considerations for evaluating a data science platform (in-house or external) and helps you determine the right platform solution for your company.
The success of an investment in a data science or machine learning platform depends on how well it addresses the needs and concerns of data scientists, the IT team, and executive leadership. In this guide, you’ll learn about organizational readiness, differences between platform types, and key considerations to evaluate vendors in this space. Download Now
There are thousands of open-source data science and machine learning packages to choose from. This guide will help both newcomer and veteran data scientists navigate the vast world of open-source tools and libraries. Read Now
Connect with Anaconda
With more than 20 million users, Anaconda is the world’s most popular data science platform and the foundation of modern machine learning. We pioneered the use of Python for data science, champion its vibrant community, and continue to steward open-source projects that make tomorrow’s innovations possible. Our enterprise-grade solutions enable corporate, research, and academic institutions around the world to harness the power of open-source for competitive advantage, groundbreaking research, and a better world.
Visit Anaconda.com to learn more.
Intel, the world leader in silicon innovation, delivers hardware and software technologies to continually advance how people work and live. For over two decades, Intel’s contributions to analytics and AI software projects – from one end of the solution stack to the other – have helped ensure that a breadth of solutions run exceptionally well on Intel® architecture. As a result, analytics and AI solutions, running on Intel® architecture, help unlock business opportunities, power businesses, connect people, and enhance lives.
Learn about Intel® Distribution for Python* and how it can accelerate Python* and speed up core computational packages.