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 15, 2020 15 Jul'20
Care provider uses automated machine learning in healthcare

An assisted living and transitional care provider uses DataRobot to automate the process of building and deploying machine learning models, enabling it to deploy models quickly.
November 07, 2019 07 Nov'19
Booz Allen releases Modzy AI platform and marketplace

Booz Allen Hamilton introduced an AI platform and marketplace made for uploading, deploying and managing AI models across a scalable environment.
October 30, 2019 30 Oct'19
Google releases TensorFlow Enterprise for enterprise users

With the new TensorFlow for enterprises, organizations running previous versions of TensorFlow can get long-term support, including security updates and select bug fixes.
August 09, 2019 09 Aug'19
IBM interprets machine models with AI Explainability kit

IBM's open source AI Explainability 360 toolkit packages algorithms and training examples to help humans better understand the decision-making process of machine learning models.

Bring yourself up to speed with our introductory content

GPT-3 AI language model sharpens complex text generation

GPT-3 is the latest natural language generation model, but its acquisition by Microsoft leaves developers wondering when, and how, they'll be able to use the model. Continue Reading
automated machine learning (AutoML)

Automated machine learning is the process of applying machine learning models to real-world problems using automation. Continue Reading
unsupervised learning

Unsupervised learning refers to the use of artificial intelligence (AI) algorithms to identify patterns in data sets containing data points that are neither classified nor labeled. Continue Reading

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

Explore the foundations of artificial neural network modeling

Dive into Giuseppe Bonaccorso's recent book 'Mastering Machine Learning Algorithms' with a chapter excerpt on modeling neural networks. Continue Reading
Bias in machine learning examples: Policing, banking, COVID-19

Human bias, missing data, data selection, data confirmation, hidden variables and unexpected crises can contribute to distorted machine learning models, outcomes and insights. Continue Reading
14 best machine learning platforms for 2020

Turn ever-growing volumes of data into enterprise insights with the right platform for machine learning. Learn more about the vendors and products in this cutting-edge market. Continue Reading

Learn to apply best practices and optimize your operations.

Machine learning and bias concerns weigh on data scientists

Data scientists are forever vigilant in their desire to identify and eliminate the many forms of bias that can compromise the credibility of machine learning models. Continue Reading
Responsible AI champions human-centric machine learning

Encompassing ethics, transparency and human centricity, responsible AI is an effective approach to deploying machine learning models and achieving actionable insights. 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

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

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
Data science's ongoing battle to quell bias in machine learning

Machine learning expert Ben Cox of H2O.ai discusses the problem of bias in predictive models that confronts data scientists daily and his techniques to identify and neutralize it. Continue Reading
Understanding how deep learning black box training creates bias

Bias in AI is a systematic issue that derails many projects. Dismantling the black box of deep learning algorithms is crucial to the advancement and deployment of the technology. Continue Reading