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.
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Big data and machine learning News
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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.
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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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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.
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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.
Big data and machine learning Get Started
Bring yourself up to speed with our introductory content
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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
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supervised learning
Supervised learning is an approach to creating artificial intelligence, where the program is given labeled input data and the expected output results. Continue Reading
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Learn the business value of AI's various techniques
To drive business value from AI, business managers need to distinguish between the various AI techniques, starting with the many flavors of machine learning. Continue Reading
Evaluate Big data and machine learning Vendors & Products
Weigh the pros and cons of technologies, products and projects you are considering.
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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
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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
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Applications of generative adversarial networks hold promise
Generative adversarial networks are tied to fake online content known as 'deepfakes,' but GANs can help data-poor enterprises supplement their data needs. Continue Reading
Manage Big data and machine learning
Learn to apply best practices and optimize your operations.
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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
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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
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AI in tax preparation gets a boost from classification tech
Tax filers and tax collectors are using AI tools to make the process of paying and collecting taxes simpler. The data-rich, complex processes of tax collection are an ideal use case for AI. Continue Reading
Problem Solve Big data and machine learning Issues
We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.
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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
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How to create a data set for machine learning with limited data
A shortage of data for machine learning training sets can halt a company's AI development in its tracks. Turning to external sources and hidden data can solve the problem. Continue Reading
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Machine learning for data storage optimizes data analysis