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What you need to know about product management for AI

O'Reilly on Data

Machine learning adds uncertainty. Underneath this uncertainty lies further uncertainty in the development process itself. There are strategies for dealing with all of this uncertainty–starting with the proverb from the early days of Agile: “ do the simplest thing that could possibly work.”

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Key Data Trends And Forecasts In The Energy Sector

Smart Data Collective

With the Coronavirus pandemic, the world has been thrown into complete uncertainty. According to a new study called Global Big Data Analytics in the Energy Sector Market, provides a comprehensive look at the industry. The uncertainty comes with a major market shift, the dimensions of data software cannot be ignored.

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Real-time Data, Machine Learning, and Results: The Evidence Mounts

CIO Business Intelligence

In the new report, titled “Digital Transformation, Data Architecture, and Legacy Systems,” researchers defined a range of measures of what they summed up as “data architecture coherence.” But the urgency and the upside of modernizing and optimizing the data architecture keeps coming into sharper focus.

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Q&A with Chris Ortega: Dealing With Uncertainty Through Technology

Jet Global

To implement AI, you need four main resources: an algorithm, at least 15 years of data, massive amounts of data over that time period, and a way to test the algorithm and get feedback on its accuracy. It’s part of a mixed bag of tools that we use for data collection, tracking, reporting, and analysis.

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An AI Data Platform for All Seasons

Rocket-Powered Data Science

To see this, look no further than Pure Storage , whose core mission is to “ empower innovators by simplifying how people consume and interact with data.”

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The importance of governance: What we’re learning from AI advances in 2022

IBM Big Data Hub

This includes data collection, instrumenting processes and transparent reporting to make needed information available for stakeholders. At IBM, we have an AI Ethics Board that supports a centralized governance, review, and decision-making process for IBM ethics policies, practices, communications, research, products and services.

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Machine Learning Product Management: Lessons Learned

Domino Data Lab

The last step for a PM is to “use derived data from the system to build new products” as this provides another way to ensure ROI across the business. Addressing the Uncertainty that ML Adds to Product Roadmaps. Here, Pete outlines common challenges and key questions for PMs to consider.