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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We designed this expert guide to help you better understand all of the considerations for building and maintaining the infrastructure and engine that support the initiatives. Plus, learn about the products and players, like Amazon, Google, IBM, and 16 more to help you make the best buying decision.
Machine learning platforms News
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January 06, 2020
06
Jan'20
U.S. restricts exports of geospatial AI software
The Trump administration's move to limit export of AI-based geospatial imaging software extends U.S. restrictions on technology that foreign countries could use to develop weapons systems.
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December 12, 2019
12
Dec'19
DataRobot acquisition of Paxata adds data prep to AI platform
By buying data prep vendor Paxata, DataRobot plans to fill out its machine learning and AI platform with tools to automate data prep processes and provide an underlying data fabric.
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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.
Machine learning platforms Get Started
Bring yourself up to speed with our introductory content
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The peaks and pitfalls of hyper-personalization marketing
As consumers begin to revolt against unlimited personal data collection and usage, the longevity of hyper-personalized communication may be cut short. Continue Reading
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UX defines chasm between explainable vs. interpretable AI
From deep learning to simple code, all algorithms should be transparent. The frameworks of AI interpretability and explainability aim to make machine learning understandable to humans. Continue Reading
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How to build a neural network from the ground floor
Deep learning is powering the development of AI. To build your own neural network, start by understanding the basics: how neural networks learn, correlate and stack with data. 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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AI and machine learning in cybersecurity: Trends to watch
AI and machine learning in cybersecurity are not so much useful to security teams today as they are necessary. Examine cybersecurity automation trends and benefits. Continue Reading
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Autonomous retail cuts operational costs, personalizes shopping
Companies are starting to deploy popular AI technologies like RFID, facial recognition and sensor tracking to provide consumers with autonomous shopping experiences. Continue Reading
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How Getty Images reduces bias in AI algorithms to avoid harm
In applications from internal job recruiting to law enforcement technology, AI bias is a widespread issue. Here's what enterprises can do to reduce bias in training and deployment. Continue Reading
Manage Machine learning platforms
Learn to apply best practices and optimize your operations.
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Machine learning ops to lead AI in 2020
The increased usage of pre-trained models, machine learning ops coming to the fore and increased transparency are all poised to lead the new year in AI. Continue Reading
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AI in networking helps keep systems running
Network administrators are increasingly using AI tools to help them manage the growing complexity of their network infrastructure, a task that's getting more complicated by the day. Continue Reading
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How to achieve explainability in AI models
At its core, AI is a complex modeling process with layers of information. In order to be able to explain the algorithm's decision-making process, start with its input data. 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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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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Brands must allay worries for AI in transportation to take hold
The personal mobility market is turning to emotional analysis and AI to negate fear and trepidation around emerging vehicle technology and the future of transportation. Continue Reading
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Serverless machine learning reduces development burdens
Getting started with machine learning throws multiple hurdles at enterprises. But the serverless computing trend, when applied to machine learning, can help remove some barriers. Continue Reading