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.

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.
May 27, 2021 27 May'21
Databricks steps up in competitive machine learning market

At its Data + AI Summit, Databricks introduced AutoML, glass box tools and Delta Lake-based Feature Store capabilities for its AI platform.
May 21, 2021 21 May'21
Google racing to catch up in growing enterprise AI market

At Google I/O this week, the tech giant launched a new enterprise machine learning platform and language recognition model, and advanced other AI products under development.
May 17, 2021 17 May'21
IBM Think 2021 news, trends and analysis

The role of technologies like AI and the cloud in business is changing. This year's IBM Think conference seeks to help enterprises understand how to capitalize on these tools.

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artificial intelligence

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. Continue Reading
How do big data and AI work together?

Enterprises are leaning on big data to train AI algorithms and, in turn, are using AI to understand big data. The results are pushing operations forward. Continue Reading
4 main types of artificial intelligence: Explained

AI technology can exceed human performance in many areas, but it is still no match for the human brain. Learn about the four main types of AI. Continue Reading

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

3 ways to evaluate and improve machine learning models

Training performance evaluation, prediction performance evaluation and baseline modeling can refine machine learning models. Learn how they work together to improve predictions. Continue Reading
The top 5 benefits of AI in banking and finance

The strategic deployment of AI in banking and finance can bring substantial benefits. Learn about how AI tools are transforming financial services and the risks to be mindful of. Continue Reading
Wrangling data with feature discretization, standardization

A variety of techniques help make data useful in machine learning algorithms. This article looks into two such data-wrangling techniques: discretization and standardization. Continue Reading

Learn to apply best practices and optimize your operations.

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
Free machine learning course: Using ML algorithms, practices and patterns

This 13-lesson series offers an overview of machine learning and its applications for those hoping to break into the machine learning job market or learn more about this technology. Continue Reading

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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
How to troubleshoot 8 common autoencoder limitations

Autoencoders' ability for automated feature extraction, data preparation, and denoising are complicated by their common problems and limitations in usage. Continue Reading