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.

May 05, 2022 05 May'22
Domino Data Lab releases latest MLOps platform update

Domino 5.2 includes new features for data scientists to find the best model development environment and use Snowflake by automating model deployment to Snowflake's Data Cloud.
April 27, 2022 27 Apr'22
HPE targets machine learning with AI platforms

The Machine Learning Development System is a full stack of software and hardware for training and building models. Swarm Learning enables enterprises to share data at the edge.
April 14, 2022 14 Apr'22
Combating AI bias in the financial sector

Companies must use explainable AI to avoid making unfair and biased decisions about consumers. Some use machine learning tools; others avoid personally identifying information.
April 14, 2022 14 Apr'22
How Cerebras CS-2 update stands up to competitors' offerings

The AI vendor's update now includes tighter support for PyTorch and TensorFlow. Focused on complex deep learning applications, Cerebras competes with Microsoft Azure and Nvidia.

Bring yourself up to speed with our introductory content

conversational AI

Conversational AI is a type of artificial intelligence that enables consumers to interact with computer applications the way they would with other humans. Continue Reading
data splitting

Data splitting is when data is divided into two or more subsets. Typically, with a two-part split, one part is used to evaluate or test the data and the other for training the model. Continue Reading
Learn the benefits of interpretable machine learning

In this excerpt from 'Interpretable Machine Learning with Python,' read how machine learning models and algorithms add value when they are both interpretable and explainable. Continue Reading

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

Interpretability and explainability can lead to more reliable ML

Interpretability and explainability as machine learning concepts make algorithms more trustworthy and reliable. Author Serg Masís assesses their practical value in this Q&A. Continue Reading
10 top AI and machine learning trends for 2022

Tiny ML, multi-modal learning, responsible AI -- learn about the top trends in AI for 2022 and how they promise to transform how business gets done. Continue Reading
Why TinyML use cases are taking off

TinyML technology can successfully collect and analyze data in real scenarios, as demonstrated in various use cases. Continue Reading

Learn to apply best practices and optimize your operations.

Enterprise hybrid AI use is poised to grow

Hybrid AI is an approach for businesses that combines human insight with machine learning and deep learning networks. Despite certain challenges, experts believe it shows promise. Continue Reading
Automated machine learning improves project efficiency

Until recently, machine learning projects had a small chance of success given the amount of time they require. Automated machine learning software speeds up the process. Continue Reading
AutoML platforms push data science projects to the finish line

Data science projects often have trouble reaching the production phase, but automated machine learning platforms are accelerating data scientists' work to help them come to fruition. Continue Reading

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

Solving the AI black box problem through transparency

Ethical AI black box problems complicate user trust in the decision-making of algorithms. As AI looks to the future, experts urge developers to take a glass box approach. Continue Reading
Discover 2 unsupervised techniques that help categorize data

Two unsupervised techniques -- category discovery and pattern discovery -- solve ML problems by seeking similarities in data groups, rather than a specific value. Continue Reading
Combating racial bias in AI

By employing a diverse team to work on AI models, using large, diverse training sets, and keeping a sharp eye out, enterprises can root out bias in their AI models. Continue Reading