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.

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April 16, 2018 16 Apr'18
How IBM's data science team quickens users' AI projects

In this Q&A, IBM's Seth Dobrin discusses the rising user interest in machine learning and AI projects and the help inexperienced users need to launch those projects.
March 02, 2018 02 Mar'18
AI experts advise developers on dev tools

New tools help developers build better, smarter AI apps, as machine learning toolkits have progressed exponentially over the last few years.
November 30, 2017 30 Nov'17
The brain-computer interface comes of age?

Two entrepreneurs on the forefront of emerging tech describe their progress on developing a brain-computer interface for commercial use.
July 17, 2017 17 Jul'17
Without communication, machine learning for data science goes nowhere

The chief data officer's next challenge may be to find storytellers who can explain machine learning for data science to decision-makers in the organization.

Bring yourself up to speed with our introductory content

Value of NLP applications varies for different AI uses

Chatbots and virtual assistants are built on sophisticated component pieces, like NLP tools and automated bot technology, which can be implemented on their own in some use cases. Continue Reading
Slow pace for AI implementation is a prudent business strategy

Enterprises eyeing AI development need to keep expectations under control and make sure projects align with business priorities to get real value from the technology. Continue Reading
predictive modeling

Predictive modeling is a process that uses data mining and probability to forecast outcomes. Continue Reading

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

Getting to machine learning in production takes focus

Bridging the gap between training and production is one of the biggest machine learning development hurdles enterprises face, but some are finding ways to streamline the process. Continue Reading
CTO Simon Crosby explains edge intelligence, talks AI uses and risks

Edge intelligence is the newborn child of edge analytics and machine learning. In this podcast, CTO Simon Crosby explains the tech and discusses AI uses, data breaches and more. Continue Reading
Big data throws big biases into machine learning data sets

AI holds massive potential for good, but it also amplifies negative outcomes if data scientists don't recognize data biases and correct them in machine learning data sets. Continue Reading

Learn to apply best practices and optimize your operations.

How one company thinks chatbots and AI can change insurance

Insurance agency management company In-Fi is hoping AI chatbots can streamline homeowner insurance applications and bring the process in line with customers' expectations. Continue Reading
Why machine learning models require a failover plan

Flawed machine learning models lead to failures and user interruptions. Expert Judith Myerson explains the causes for failures and how a failover plan can improve user experience. Continue Reading

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

Machine vision makes paper a thing of the past for insurers

The insurance industry is buried in paper-based processes. Former MetLife CIO Gary Hoberman aims to change that with a platform that runs on AI and machine vision. Continue Reading