Cloudera

FEBRUARY 16, 2022

In this example, the Machine Learning (ML) model struggles to differentiate between a chihuahua and a muffin. Will the model correctly determine it is a muffin or get confused and think it is a chihuahua? The extent to which we can predict how the model will classify an image given a change input (e.g. Model Visibility.

Machine Learning 81

Machine Learning Modeling Metadata Recreation/Entertainment 81

O'Reilly on Data

MARCH 31, 2020

Instead of writing code with hard-coded algorithms and rules that always behave in a predictable manner, ML engineers collect a large number of examples of input and output pairs and use them as training data for their models. The model is produced by code, but it isn’t code; it’s an artifact of the code and the training data.

Management 332

Management Machine Learning Experimentation Metrics 332

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often Galbraith eventually opted to publish Cuckoo’s Calling through an acquaintance of sorts. but the publishing industry failed to see it. The AIgent was built with BERT, Google’s state-of-the-art language model.

Modeling 59

Modeling Metadata Publishing Sales 59

Data Science and Beyond

APRIL 5, 2021

Well, no one has compiled a meta-post of my public work from 2020 (that I know of), so it’s finally time to publish it myself. The world has adapted quickly, though it seems like Automattic’s globally-distributed model is still quite unusual. Remote work. Only time will tell. Sustainability. Technical work.

Experimentation 61

Experimentation Testing Publishing Measurement 61

Insight

MARCH 27, 2019

It utilizes Bayesian optimization for discovering data augmentation strategies tailored to your image dataset. Introduction Data is the most critical piece of AI applications. Not having enough labeled data often leads to overfitting, which means the model will not be able to generalize to unseen examples.

Optimization 75

Optimization Cost-Benefit Modeling Strategy 75

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.

Data-driven 279

Data-driven Machine Learning IoT Deep Learning 279

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