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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.

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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.
Analytics Software
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.
Analytics Software
Learn how to boost prediction quality and performance using Intel accelerated gradient boosting.
Analytics Software
Faster experimentation with predictable behavior. Intel accelerated scikit-learn enables drop-in acceleration with deterministic, mathematical equivalence.
Analytics Software
Predicting house prices using linear regression.
Analytics Software
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.

Featured Resources

  • 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

About 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.

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About Intel

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.

Follow Intel Analytics Software on Medium.