Machine learning platforms

Enterprises need to make smart investments in machine learning platforms. With a range of features and price tags, making the right 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 projects.

October 12, 2021 12 Oct'21
Google, partners launch new tools for Vertex AI ML platform

The tech giant released a series of new tools and applications, some with partners, for the automated machine learning platform it launched in May 2021.
October 11, 2021 11 Oct'21
IBM introduces Environmental Intelligence Suite

The tech giant said the new software suite from its weather subsidiary will help organizations prepare for climate risk and weather disasters such as wildfires and flooding.
October 07, 2021 07 Oct'21
Machine learning development for autonomous vehicles

AI experts from the autonomous vehicle technology industry discuss automation, spotting flaws in and speeding up machine learning systems and accounting for changes in real time.
October 05, 2021 05 Oct'21
Dell introduces Validated Design for AI at VMworld 2021

The tech vendor revealed a new partnership with AI GPU maker Nvidia at VMWorld 2021 aimed at providing an AI and machine learning platform for IT users in virtualized environments.

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10 top artificial intelligence certifications and courses for 2021

Numerous AI certifications and courses cover the basics and applications of artificial intelligence, so we narrowed the field to 10 of the more diverse and comprehensive programs. Continue Reading
Model optimization methods to cut latency, adapt to new data

This last part of the series on machine learning explains two final model optimization techniques: lightweight model implementation and incremental model learning. Continue Reading
6 top AI books to read in 2021

Many sources provide comprehensive information on the fundamentals and applications of artificial intelligence, so we narrowed the field to six of the more diverse AI books. Continue Reading

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

How neural network training methods are modeled after the human brain

Training neural nets to mirror the human brain enables deep learning models to apply learning to data they've never seen before. Continue Reading
Data science vs. machine learning vs. AI: How they work together

Data science, machine learning and AI are central to analytics and other enterprise uses. Here's what each involves and how combining them benefits organizations. Continue Reading
ML model optimization with ensemble learning, retraining

Making ML models better post-deployment can be accomplished. Learn the ins and outs of two key techniques: ensemble learning and frequent model retraining. Continue Reading

Learn to apply best practices and optimize your operations.

AI and climate change: The mixed impact of machine learning

AI can both help and hurt the environment. While companies use artificial intelligence to increase factory efficiency and lower energy costs, training AI demands a lot of energy. Continue Reading
Energy consumption of AI poses environmental problems

Data centers and large AI models use massive amounts of energy and are harmful to the environment. Businesses can take action to lower their environmental impact. Continue Reading
How to detect bias in existing AI algorithms

While enterprises can't eliminate bias from their data, they can significantly reduce bias by establishing a governance framework and employing more diverse employees. 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