Machine learning platforms
Enterprises need to make smart investments in machine learning platforms. With a range of features and price tags, making the right choice can seem like a daunting task. Discover machine learning platform comparison content, information on getting started with machine learning algorithms and best practices to gain the most from projects.
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Machine learning platforms 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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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.
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June 17, 2020
17
Jun'20
DataRobot training aims to upskill citizen data scientists
DataRobot unveiled a new data science training program, and enhancements to its enterprise AI platform, including a geospatial capability and automated time series anomaly detection.
Machine learning platforms 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 Machine learning platforms 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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Autoencoders' example uses augment data for machine learning
Autoencoders are neural networks that serve machine learning models -- from denoising to dimensionality reduction. Seven use cases explore the practical application of autoencoder technology. Continue Reading
Manage Machine learning platforms
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 operations management relieves pressure on IT teams
AI, when combined with IT operations and DevOps teams, forms AIOps that can greatly improve how IT assets are developed, produced and managed. Continue Reading
Problem Solve Machine learning platforms 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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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
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6 ways to reduce different types of bias in machine learning
As adoption of machine learning grows, companies must become data experts -- or risk results that are inaccurate, unfair or even dangerous. Here's how to combat ML bias. Continue Reading