AI infrastructure
Enterprises investing in deep learning platforms need AI infrastructure sufficient enough to synthesize a massive amount of data. Here, you'll find the information you need to make decisions about AI-specific compute architectures -- from GPU-packed servers to highly-scalable clustered computing systems built for big data and machine learning applications.
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Download: Free AI Buyer's Guide
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.
AI infrastructure News
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December 03, 2019
03
Dec'19
SageMaker Studio makes model building, monitoring easier
New SageMaker tools from AWS to open up machine learning models by making it easier for non-expert developers make, run, monitor and debug machine learning models.
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December 03, 2019
03
Dec'19
New Amazon Kendra AI search tool indexes enterprise data
With Kendra, a new cognitive search tool from Amazon, enterprise users can more easily query their data. The tool uses machine learning and NLP to understand context, according to AWS.
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November 05, 2019
05
Nov'19
Microsoft Power Platform gets AI and RPA updates
At Microsoft Ignite, the tech giant released new updates to ease RPA and AI building across its Power Platform, as well as new capabilities for Cortana within Microsoft 365.
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October 30, 2019
30
Oct'19
Google releases TensorFlow Enterprise for enterprise users
With the new TensorFlow for enterprises, organizations running previous versions of TensorFlow can get long-term support, including security updates and select bug fixes.
AI infrastructure Get Started
Bring yourself up to speed with our introductory content
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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
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3 in-demand AI skills that boost data scientists' development
AI encompasses a wide range of disciplines, from advanced math to application development, and building a strong AI team starts with incredibly skilled data scientists. Continue Reading
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How to develop a successful, modern AI infrastructure
Before AI can revolutionize business processes or decision-making, companies need a strong foundation. These tools, platforms and applications help enterprises get started with AI. Continue Reading
Evaluate AI infrastructure Vendors & Products
Weigh the pros and cons of technologies, products and projects you are considering.
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AI-driven development trends shake up the cloud market
Enterprises are increasingly interested in AI, and cloud vendors are in a race to adapt their platforms to meet those needs. See what initiatives are underway to meet the next wave of app development. Continue Reading
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3 intelligent process automation use cases and how they work
Enterprises are pursuing intelligent process automation to take their digital transformation and RPA applications to the next level. Here's a look at three use cases. Continue Reading
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Ethical concerns of AI call growing adoption into question
AI tools are getting easier to use every day, putting powerful tools into the hands of potentially malicious users. The time to think about the ethics of AI advances is now. Continue Reading
Manage AI infrastructure
Learn to apply best practices and optimize your operations.
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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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AI and climate change: The mixed impact of machine learning
While AI can play a role in helping researchers and conservationists address the impacts of climate change, it can also contribute to carbon emissions, making it a mixed blessing. 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
Problem Solve AI infrastructure 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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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
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Experts discuss pressing data science problems and solutions
Most data science projects end up facing similar problems, such as lack of robustness and data quality issues. In this feature, experts offer tips on how to overcome these challenges. Continue Reading
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Berklee uses SnapLogic for its AI in higher education needs
Berklee College of Music needed intelligent integration for its two student portals after a merger with the Boston Conservatory. The college chose SnapLogic to connect the systems. Continue Reading