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.

June 10, 2021 10 Jun'21
Microsoft's acquisition of Nuance clears antitrust hurdle

The U.S. government didn't flag the Microsoft-Nuance acquisition as violating antitrust laws, but scrutiny of big tech continues as governments try to reign in powerful companies.
June 09, 2021 09 Jun'21
DataRobot integrates AI modeling tools with Snowflake

The AI vendor and cloud data warehouse vendors' latest collaboration will enable joint customers to develop and deploy AI models with fewer production lags.
June 09, 2021 09 Jun'21
M&A wave rising in hot AI market

The market is ripe for more AI M&A deals as enterprise demand for AI talent, MLOps and vertical applications rose during the pandemic.
June 02, 2021 02 Jun'21
Nvidia extends its AI reach with cloud development hub

Nvidia debuted a development platform to speed the development and delivery of AI projects, along with a monthly subscription plan that allows access to its DGX SuperPod servers.

Bring yourself up to speed with our introductory content

hyperautomation

Hyperautomation is a framework and set of advanced technologies for scaling automation in the enterprise; the ultimate goal of hyperautomation is to develop a process for automating enterprise automation. Continue Reading
AWS SageMaker training, making machine learning accessible

Making machine learning more accessible and helping developers with AWS SageMaker training is at the core of Julien Simon's book, 'Learn Amazon SageMaker.' Continue Reading
artificial intelligence

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Continue Reading

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

Benefits and drawbacks of AI in cloud computing

AI and cloud computing work hand in hand to improve upon various processes, but the combination of these technologies comes with a price. Continue Reading
The role of automation in SD-WAN

To achieve the benefits of SD-WAN automation -- such as improved QoS, reduction of human error and faster operations -- API integration, machine learning and AIOps are a must. Continue Reading
Designing and building artificial intelligence infrastructure

Building an artificial intelligence infrastructure requires a serious look at storage, networking and AI data needs, combined with deliberate and strategic planning. Continue Reading

Learn to apply best practices and optimize your operations.

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
Data democratization strategy for machine learning enterprise

In the enterprise, data democratization works to break down data silos by opening access to an organization's data across teams in an effort to improve workflows. Continue Reading
Finding the balance between edge AI vs. cloud AI

Centralized cloud resources allow AI to continuously improve while edge AI allows for real-time decision-making and larger models. The best approach combines them. Continue Reading

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

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
Expanding explainable AI examples key for the industry

Improving AI explainability and interpretability is key to the continued building of trust with consumers and the continued success of the technology. 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