O'Reilly on Data

JULY 28, 2020

The first step in building an AI solution is identifying the problem you want to solve, which includes defining the metrics that will demonstrate whether you’ve succeeded. It sounds simplistic to state that AI product managers should develop and ship products that improve metrics the business cares about. Agreeing on metrics.

Marketing 363

Marketing Experimentation Metrics Testing 363

O'Reilly on Data

MARCH 31, 2020

This means that the AI products you build align with your existing business plans and strategies (or that your products are driving change in those plans and strategies), that they are delivering value to the business, and that they are delivered on time. AI product estimation strategies.

Management 339

Management Machine Learning Experimentation Metrics 339

Corinium

JUNE 6, 2019

Beyond that, we recommend setting up the appropriate data management and engineering framework including infrastructure, harmonization, governance, toolset strategy, automation, and operating model. It is also important to have a strong test and learn culture to encourage rapid experimentation.

Insurance 250

Insurance Analytics Forecasting Deep Learning 250

Rocket-Powered Data Science

JULY 6, 2021

2) MLOps became the expected norm in machine learning and data science projects. MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. 4) AIOps increasingly became a focus in AI strategy conversations.

Machine Learning 279

Machine Learning Data-driven IoT Deep Learning 279

DataKitchen

APRIL 13, 2021

A complete DataOps program will have a unified, system-wide view of process metrics using a common data store. Metis Machine — Enterprise-scale Machine Learning and Deep Learning deployment and automation platform for rapid deployment of models into existing infrastructure and applications.

Testing 300

Testing Machine Learning Consulting Data Quality 300

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Ensure that product managers work on projects that matter to the business and/or are aligned to strategic company metrics.

Management 110

Management Machine Learning Experimentation Metrics 110