Big data and machine learning

Big data and machine learning is a powerful pair for data-driven enterprises. Data collection and storage using big data frameworks, data integration and data preparation can be difficult for even the most experienced data scientists. Get expert insights and best practices for working with big data and machine learning projects.

September 17, 2020 17 Sep'20
Rivals Cloudian, Scality embrace all-flash object storage

Primarily used as a disk repository, object storage use cases are evolving to support workloads that need fast flash. Rivals Cloudian and Scality provide the latest evidence.
August 17, 2020 17 Aug'20
Does technology increase the problem of racism and discrimination?

According to a publication from the MIT Technology Review, technology promotes racism. Most facial recognition algorithms discriminate against the Black population. And even certain concepts or technological terminology tend to be offensive, ...
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.
November 04, 2019 04 Nov'19
Microsoft launches Azure Synapse Analytics for the cloud

New Microsoft Azure analytics service provides cloud BI and machine learning, enabling users to do analytics on data in various sources, including data warehouses and data lakes.

Bring yourself up to speed with our introductory content

3 machine learning best practices to use in IoT projects

IoT machine learning takes a whole team of experts that can approach the project with the right mindset, effectively communicate, and facilitate user feedback and testing. Continue Reading
AI in security analytics is the enhancement you need

AI-powered analytics is critical to an effective, proactive security strategy. Learn how AI-enabled tools work and what your organization needs to do to reap their benefits. Continue Reading
A Timeline of Machine Learning History

Machine learning has evolved into a sophisticated technology with widespread applications. Learn about the milestones and achievements in the history of ML. Continue Reading

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

AI in cybersecurity ups your odds against persistent threats

AI capabilities can identify and take down cyberthreats in real time but are only part of what your team needs to come out on the winning side of the cybersecurity battle. Continue Reading
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

Learn to apply best practices and optimize your operations.

AI cybersecurity raises analytics' accuracy, usability

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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

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

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
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