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.

April 14, 2021 14 Apr'21
Data scientist shortage leaves organizations uncertain

Despite their desire to use data science in their decision-making process, some organizations can't find qualified data scientists to develop and run their data science initiatives.
March 30, 2021 30 Mar'21
Fungible computational storage eyes composable use cases

Fungible's computational storage anchors a new offering for composable infrastructure. The Fungible Data Center license allows IT teams to consume resources on demand.
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, ...

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11 data science skills for machine learning and AI

As companies realize the power of data, they're tasked with finding data science practitioners with AI and ML skill sets to help them use the data to make better business decisions. Continue Reading
8 big data use cases for businesses and industry examples

As the understanding of big data requirements increases, so do big data use cases. Here are eight ways businesses are using big data to improve operations. Continue Reading
machine learning

Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input ... Continue Reading

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

Big data vs. machine learning: How they differ and relate

Big data and machine learning are a powerful analytics pairing. Here's an explanation of the difference between them and how they can be used together. Continue Reading
In-depth guide to machine learning in the enterprise

Enterprises are adopting machine learning technologies at rapid rates. In this machine learning guide, we break down what you need to know about this transformative technology. Continue Reading
Synthetic data for machine learning combats privacy, bias issues

Synthetic data generation for machine learning can combat bias and privacy concerns while democratizing AI for smaller companies with data set issues. 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
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
Speech to text for deaf users aids in accessibility

For the millions of people who are hard of hearing, speech-to-text advancements have improved their ability to complete daily tasks -- but the tech still has a long way to go. 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