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.

January 24, 2022 24 Jan'22
Silicon Labs launches new AI/ML chips, toolkit for edge

The chip vendor's new wireless SoC aims to bring AI and machine learning to the IoT. The systems also use the Matter IP-based protocol to wirelessly connect devices.
January 19, 2022 19 Jan'22
Nvidia releases updates to AI enterprise suite

The AI suite includes a full integration with Tanzu, giving enterprises the flexibility to run workloads on containers and VMs. The vendor also introduced two new servers.
January 13, 2022 13 Jan'22
Lucidworks releases AI-powered search platform

The vendor's SaaS platform includes a newly released application, Connected Search. It's the first of several releases expected over the next year.
December 28, 2021 28 Dec'21
Cryptocurrency broker uses Ada AI platform for better CX

LiteBit partnered with the customer service vendor in 2017 when the cryptocurrency market was booming. Since then, it has been using the vendor's AI-powered chatbot.

Bring yourself up to speed with our introductory content

predictive modeling

Predictive modeling is a mathematical process used to predict future events or outcomes by analyzing patterns in a given set of input data. Continue Reading
Tips and tricks for deploying TinyML

A typical TinyML deployment has many software and hardware requirements, and there are best practices that developers should be aware of to help simplify this complicated process. Continue Reading
5-step predictive analytics process cycle

A viable predictive model that yields valuable outcomes requires a methodical team approach to goal-setting, data integrity and model development, deployment and validation. Continue Reading

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

Why TinyML use cases are taking off

TinyML technology can successfully collect and analyze data in real scenarios, as demonstrated in various use cases. 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
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

Learn to apply best practices and optimize your operations.

Machine learning on microcontrollers enables AI

Using today's advanced AI systems to run machine learning on smaller devices like microprocessors offers benefits, but also limits, which experts are working to surmount. 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.

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
9 data quality issues that can sideline AI projects

The quality of your data affects how well your AI and machine learning models will operate. Getting ahead of these nine data issues will poise organizations for successful AI models. Continue Reading