Domino Data Lab

MAY 15, 2019

Unfortunately, a common challenge that many industry people face includes battling “ the model myth ,” or the perception that because their work includes code and data, their work “should” be treated like software engineering. I was fortunate to see an early iteration of Pete Skomoroch ’s ML product management presentation in November 2018.

Machine Learning 111

Machine Learning Management Experimentation Uncertainty 111

O'Reilly on Data

MARCH 31, 2020

For example, if engineers are training a neural network, then this data teaches the network to approximate a function that behaves similarly to the pairs they pass through it. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies.

Management 332

Management Machine Learning Experimentation Metrics 332

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams. This is not that.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch presented “ Product Management for AI ” at Rev. Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. Session Summary.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

MARCH 23, 2010

Having two tools guarantees you are going to be data collection, data processing and data reconciliation organization. Post my keynote the feedback I got was: "Your presentation was powerful, you made a compelling case for how we can do the things you have outlined to take advantage of the opportunity.

Analytics 118

Analytics Consulting Metrics Experimentation 118

datapine

JANUARY 31, 2022

Because things are changing and becoming more competitive in every sector of business, the benefits of business intelligence and proper use of data analytics are key to outperforming the competition. BI software uses algorithms to extract actionable insights from a company’s data and guide its strategic decisions.

Business Intelligence 214

Business Intelligence Analytics Cost-Benefit ROI 214