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.

September 15, 2021 15 Sep'21
DataRobot AI Cloud gives users multi-cloud capabilities

DataRobot unveiled its cloud, giving users access to technologies and applications the vendor obtained with a series of recent and older acquisitions.
September 14, 2021 14 Sep'21
AI hardware and software: The key to eBay's marketplace

At the AI Hardware summit 2021, an eBay exec discusses how its similarity system enables it to build an AI-driven marketplace for better latency and throughput.
August 31, 2021 31 Aug'21
Databricks raises $1.6B more to boost data lakehouse

Investors are super confident in the growth potential for cloud data management and analytics as the impact of the Snowflake IPO continues to ripple through the industry.
August 23, 2021 23 Aug'21
IBM boosts AI speed, security on next-gen mainframe processor

Hoping to make the mainframe more relevant to users implementing AI, IBM plans to introduce next year a processor that runs AI applications faster and with better security.

Bring yourself up to speed with our introductory content

Why transparency in AI matters for businesses

To ensure model accuracy, businesses need to understand why their machine learning models make their decisions. Certain tools and techniques can help with that. Continue Reading
What is artificial intelligence?

Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. Continue Reading
How to formulate a winning AI strategy

Executives are aware of the value artificial intelligence in its many forms can bring to enterprises yet devising a viable AI strategy can be as complex as the technology itself. Continue Reading

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

Why companies should use AI for fraud management, detection

AI is involved in many cybersecurity processes. Now it's making inroads in fraud management and detection. The benefits, however, are not without AI's nagging bias challenge. Continue Reading
A guide to artificial intelligence in the enterprise

AI in the enterprise will change how work is done, but companies must overcome various challenges to derive value from this powerful and rapidly evolving technology. Continue Reading
9 top AI and machine learning trends for 2021

Tiny ML, multi-modal learning, responsible AI -- learn about the top trends in AI for 2021 and how they promise to transform how business gets done. Continue Reading

Learn to apply best practices and optimize your operations.

AI and climate change: The mixed impact of machine learning

AI can both help and hurt the environment. While companies use artificial intelligence to increase factory efficiency and lower energy costs, training AI demands a lot of energy. Continue Reading
10 steps to achieve AI implementation in your business

Maximizing the value of insights into your business, industry and competition requires a thoughtful, creative, experimental, incremental and team approach to deploying AI. Continue Reading
Advanced SQL skills boost data scientists' value

Learning advanced SQL skills can help data scientists effectively query their databases and unlock new insights into data relationships, resulting in more useful information. Continue Reading

We’ve gathered up expert advice and tips from professionals like you so that the answers you need are always available.

Expanding explainable AI examples key for the industry

Improving AI explainability and interpretability are keys to building consumer trust and furthering the technology's success. Continue Reading
Addressing 3 infrastructure issues that challenge AI adoption

One of the biggest problems enterprises run into when adopting AI infrastructure is using a development lifecycle that doesn't work when building and deploying AI models. Continue Reading
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