Machine learning modeling

There are a number of ways to tackle machine learning modeling. Find content on supervised and unsupervised machine learning algorithm training and modeling processes, including expert tips to help you evaluate, choose and use the right machine learning model to deliver accurate predictive outputs to solve business problems.

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 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.
August 31, 2021 31 Aug'21
Databricks raises $1.6B more to boost data lakehouse

Investors are super confident in the growth potential for cloud data management and analytics as the impact of the Snowflake IPO continues to ripple through the industry.
July 27, 2021 27 Jul'21
DataRobot acquires Algorithmia to further MLOps goal

DataRobot continues to pursue its growth-by- acquisition strategy by buying MLOps vendor Algorithmia in a bid to become a full-service machine learning platform provider.

Bring yourself up to speed with our introductory content

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
How to build a machine learning model in 7 steps

Building a viable, reliable and agile machine learning model that streamlines operations and bolsters business planning takes patience, preparation and perseverance. Continue Reading
Unsupervised machine learning: Dealing with unknown data

Learn how machine learning works when dealing with unclassified, unlabeled data sets and how, using certain algorithms and other practices, the system can learn on its own. 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
Tackling the AI bias problem at the origin: Training data

Though data bias may seem like a back-end issue, the enterprise implications of an AI software using biased data can derail model implementation. Continue Reading