Remove Consulting Remove Experimentation Remove Testing
article thumbnail

AI-native software engineering may be closer than developers think

CIO Business Intelligence

Despite critics, most, if not all, vendors offering coding assistants are now moving toward autonomous agents, although full AI coding independence is still experimental, Walsh says. Caylent, an AWS cloud consulting partner, uses AI to write most of its code in specific cases, says Clayton Davis, director of cloud-native development there.

Software 141
article thumbnail

Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

Weve seen this across dozens of companies, and the teams that break out of this trap all adopt some version of Evaluation-Driven Development (EDD), where testing, monitoring, and evaluation drive every decision from the start. What breaks your app in production isnt always what you tested for in dev! The way out?

Testing 168
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

The DataOps Vendor Landscape, 2021

DataKitchen

Testing and Data Observability. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Testing and Data Observability. Production Monitoring and Development Testing.

Testing 300
article thumbnail

6 keys to genAI success in 2025

CIO Business Intelligence

While genAI has been a hot topic for the past couple of years, organizations have largely focused on experimentation. Find a change champion and get business users involved from the beginning to build, pilot, test, and evaluate models. Click here to learn more about how you can advance from genAI experimentation to execution.

article thumbnail

AI poised to replace entry-level positions at large financial institutions

CIO Business Intelligence

Large banking firms are quietly testing AI tools under code names such as as Socrates that could one day make the need to hire thousands of college graduates at these firms obsolete, according to the report. But that’s just the tip of the iceberg for a future of AI organizational disruptions that remain to be seen, according to the firm.

article thumbnail

AI Product Management After Deployment

O'Reilly on Data

In Bringing an AI Product to Market , we distinguished the debugging phase of product development from pre-deployment evaluation and testing. During testing and evaluation, application performance is important, but not critical to success. require not only disclosure, but also monitored testing. Debugging AI Products.

article thumbnail

Disrupting the enterprise: How AI is redefining people, process, and productivity

CIO Business Intelligence

Experimentation drives momentum: How do we maximize the value of a given technology? Via experimentation. This can be as simple as a Google Sheet or sharing examples at weekly all-hands meetings Many enterprises do “blameless postmortems” to encourage experimentation without fear of making mistakes and reprisal.