ODSC West 2022: Ops Focus Emphasizes the Move to Production Systems
ODSC brought renewed attention to the need for operationalized data processes when using advanced analytics, machine learning, or artificial intelligence.
Topic: Machine Learning
Machine learning is an application of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Emerging Technology to Help Farmers Feed, Clothe, and Provide For an Expanding Population
Advanced technology helps farmers work more efficiently, saving time and money, and ultimately can help them address modern challenges ranging from changing …
Retail is the Ultimate Testing Ground for AI/ML
The complexity of retail is begging for more investment in AI/ML to determine how much it can improve the way companies interact with customers and each
Mortgage Fraud AI Improves Detection By 30%
An AI document forensics solution improved fraud detection at an online mortgage broker by 30 percent, alongside reducing workload for
Machine Learning Isn’t An Instant Fix For Fraud
Machine learning is being deployed by organizations in finance and retail to prevent fraud, but the deployment of ML is not an instant
To Batch or To Stream–That Is the Question of the Day
Claypot AI founder, Chip Huyen, designs machine-learning systems that combine streaming data and batch processing to accommodate multiple data
Maximizing Software Quality with Artificial Intelligence
Testing solutions that use artificial intelligence help development teams more easily analyze and understand where and what to fix and gives them the ability …
Why AutoML Should Become a Key Tool for Enterprises
With the potential to democratize AI and ML, AutoML is the answer many enterprises across industry verticals have been seeking to take AI projects from pilots …
How Hybrid Cloud Can Be Used For Analytics and Machine Learning
A hybrid cloud approach lets businesses essentially pick and choose the various aspects of their analytics and ML workloads that they want to keep on-premises …
3 Considerations for Adding Real-Time ML to Applications
Data that is ready for machine learning will be observable, supported by real-time infrastructure, and primarily processed with streaming technologies.