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

O'Reilly on Data

MARCH 31, 2020

You might establish a baseline by replicating collaborative filtering models published by teams that built recommenders for MovieLens, Netflix, and Amazon. It may even be faster to launch this new recommender system, because the Disney data team has access to published research describing what worked for other teams.

Management 327

Management Machine Learning Experimentation Metrics 327

Cloudera

FEBRUARY 16, 2022

It is also possible to create your own AMP and publish it in the AMP catalogue for consumption. Support for multiple sessions within a project allows data scientists, engineers and operations teams to work independently alongside each other on experimentation, pipeline development, deployment and monitoring activities in parallel.

Machine Learning 83

Machine Learning Modeling Metadata Recreation/Entertainment 83

Ontotext

NOVEMBER 6, 2019

GraphDB Workbench is the interface for Ontotext’s semantic graph database, which provides the core infrastructure including modelling agility, data integration, relationship exploration and cross-enterprise semantic data publishing and consumption. The Plugins.

Experimentation 45

Experimentation Deep Learning Software Modeling 45

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. The first two are from editions of my newsletter, The Marketing – Analytics Intersect (it goes out weekly, and is now my primary publishing channel, sign up!). Deep Learning.

Marketing 136

Marketing Analytics Machine Learning Strategy 136

Domino Data Lab

MAY 8, 2019

For example, in the case of more recent deep learning work, a complete explanation might be possible: it might also entail an incomprehensible number of parameters. They also require advanced skills in statistics, experimental design, causal inference, and so on – more than most data science teams will have.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

O'Reilly on Data

SEPTEMBER 11, 2019

Deep learning,” for example, fell year over year to No. Since 1977, for example, the Institute of Electrical and Electronics Engineers (IEEE) has published the Data Engineering Bulletin , a quarterly journal that focuses on engineering data for use with database systems [2]. 40; it peaked at Strata NY 2018 at No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20