O'Reilly on Data

MARCH 19, 2020

Our analysis of ML- and AI-related data from the O’Reilly online learning platform indicates: Unsupervised learning surged in 2019, with usage up by 172%. Deep learning cooled slightly in 2019, slipping 10% relative to 2018, but deep learning still accounted for 22% of all AI/ML usage.

Deep Learning 276

Deep Learning Machine Learning Data-driven Modeling 276

FineReport

DECEMBER 19, 2019

If you want to learn more about self-service BI tools, you can take a look at this review: 5 Most Popular Business Intelligence (BI) Tools in 2019 , to understand your own needs and then choose the tool that is right for you. Data Analysis Libraries. Pandas is a Python data science library that is constantly improving.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

O'Reilly on Data

MARCH 18, 2020

The new survey, which ran for a few weeks in December 2019, generated an enthusiastic 1,388 responses. Supervised learning is the most popular ML technique among mature AI adopters, while deep learning is the most popular technique among organizations that are still evaluating AI. But what kind?

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

DataKitchen

APRIL 13, 2021

Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs. Prefect Technologies — Open-source data engineering platform that builds, tests, and runs data workflows. Genie — Distributed big data orchestration service by Netflix.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

JULY 2, 2019

In other words, using metadata about data science work to generate code. In this case, code gets generated for data preparation, where so much of the “time and labor” in data science work is concentrated. Scale the problem to handle complex data structures. BTW, videos for Rev2 are up: [link].

Metadata 105

Metadata Machine Learning Data Science Data-driven 105

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. 221) to 2019 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

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. Machine learning model interpretability. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105