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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ongoing monitoring of critical metrics is yet another form of experimentation.

Marketing 362
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Methods of Study Design – Experiments

Data Science 101

We all are familiar with experiments , we read about them in books or newspapers. Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. We collect some participants and let them stay in a house where they can be observed.

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The AIgent: Using Google’s BERT Language Model to Connect Writers & Representation

Insight

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. often without even looking at it.

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The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

We are far too enamored with data collection and reporting the standard metrics we love because others love them because someone else said they were nice so many years ago. This thought was in my mind as I was reading Lean Analytics a new book by my friend Alistair Croll and his collaborator Benjamin Yoskovitz.

Metrics 156
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Web Analytics: An Hour A Day

Occam's Razor

I am thrilled to say that my book Web Analytics: An Hour A Day has been published and is now widely available. It has been such an amazing journey to write the book, and for it to come up almost exactly a year after I started this blog. Damini, Chirag and now the book! :). Part One: The book (my side of the story, details).

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Understanding Causal Inference

Domino Data Lab

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. Data collected from this system reflects the way the world works when we just observe it. An Example.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

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.