Machine learning platforms

Enterprises need to make smart investments in machine learning platforms. With a range of features and price tags, making the right ML choice can seem like a daunting task. Discover machine learning platform comparison content, information on getting started with machine learning algorithms and best practices to gain the most from ML projects.

August 24, 2022 24 Aug'22
New funding for Anyscale boosts popular Ray AI platform

The startup raised $99 million and released a new version of its open source framework, which helps enterprises scale AI workloads and projects with the resources they have.
August 19, 2022 19 Aug'22
Why you need to consider using small data to train AI models

A smaller data set makes sense for certain applications, such as intelligent document processing. It is not helpful in cases in which a large volume is needed to avoid mistakes.
August 17, 2022 17 Aug'22
A hyper-personalized AI platform for community finance

Using the vendor's Cortex AI platform, DeepTarget created a digital experience platform that community finance institutions can use to predict what consumers will buy.
August 09, 2022 09 Aug'22
Nvidia targets metaverse with new Omniverse Avatar Engine

The vendor's new cloud engine will help organizations easily build avatars and digital assistants. Nvidia also revealed plans to evolve USD with partners such as Pixar.

Bring yourself up to speed with our introductory content

Karnaugh map (K-map)

A Karnaugh map (K-map) is a visual method used to simplify the algebraic expressions in Boolean functions without having to resort to complex theorems or equation manipulations. Continue Reading
AI winter

AI winter is a quiet period for artificial intelligence research and development. Continue Reading
truncation error

A truncation error is the difference between an actual and a truncated, or cut-off, value. Continue Reading

Weigh the pros and cons of technologies, products and projects you are considering.

Why banks need MLOps for digital transformation

Financial institutions should look to MLOps to ease the development, deployment and management of machine learning models. MLOps is often ignored, yet banking will benefit from it. Continue Reading
The future of data science: Career outlook and industry trends

The future of data science as a profession is unclear, as new technologies change the responsibilities of data scientists. It may also soon change the nature of the job. Continue Reading
Interpretability and explainability can lead to more reliable ML

Interpretability and explainability as machine learning concepts make algorithms more trustworthy and reliable. Author Serg Masís assesses their practical value in this Q&A. Continue Reading

Learn to apply best practices and optimize your operations.

Enterprise hybrid AI use is poised to grow

Hybrid AI is an approach for businesses that combines human insight with machine learning and deep learning networks. Despite certain challenges, experts believe it shows promise. Continue Reading
Automated machine learning improves project efficiency

Until recently, machine learning projects had a small chance of success given the amount of time they require. Automated machine learning software speeds up the process. Continue Reading
AutoML platforms push data science projects to the finish line

Data science projects often have trouble reaching the production phase, but automated machine learning platforms are accelerating data scientists' work to help them come to fruition. Continue Reading

We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

Solving the AI black box problem through transparency

Ethical AI black box problems complicate user trust in the decision-making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach. Continue Reading
Discover 2 unsupervised techniques that help categorize data

Two unsupervised techniques -- category discovery and pattern discovery -- solve ML problems by seeking similarities in data groups, rather than a specific value. Continue Reading
Combating racial bias in AI

By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models. Continue Reading