Remove Experimentation Remove Optimization Remove Strategy
article thumbnail

Practical Skills for The AI Product Manager

O'Reilly on Data

AI PMs should enter feature development and experimentation phases only after deciding what problem they want to solve as precisely as possible, and placing the problem into one of these categories. Experimentation: It’s just not possible to create a product by building, evaluating, and deploying a single model.

article thumbnail

Robust Experimentation and Testing | Reasons for Failure!

Occam's Razor

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

Insiders

Sign Up for our Newsletter

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

article thumbnail

Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 364
article thumbnail

Where CIOs should place their 2025 AI bets

CIO Business Intelligence

As gen AI heads to Gartners trough of disillusionment , CIOs should consider how to realign their 2025 strategies and roadmaps. AI innovation can not and should not exist without parallel investment in governance to ensure its responsible and effective integration, says Henry Umney, MD of GRC strategy at Mitratech.

article thumbnail

The key to operational AI: Modern data architecture

CIO Business Intelligence

People : To implement a successful Operational AI strategy, an organization needs a dedicated ML platform team to manage the tools and processes required to operationalize AI models. As a result, ​developers — regardless of their expertise in machine learning — will be able to develop and optimize business-ready large language models (LLMs).

article thumbnail

Escaping POC Purgatory: Evaluation-Driven Development for AI Systems

O'Reilly on Data

ML apps needed to be developed through cycles of experimentation (as were no longer able to reason about how theyll behave based on software specs). The skillset and the background of people building the applications were realigned: People who were at home with data and experimentation got involved! Some seemed better than others.

Testing 174
article thumbnail

How AI orchestration has become more important than the models themselves

CIO Business Intelligence

To integrate AI into enterprise workflows, we must first do the foundation work to get our clients data estate optimized, structured, and migrated to the cloud. Once the data foundation is in place, it is important to then select and embed the best combination of AI models into the workflow to optimize for cost, latency, and accuracy.

Modeling 116