Cloudera

NOVEMBER 3, 2021

Have you ever asked a data scientist if they wanted their code to run faster? You would probably get a more varied response asking if the earth is flat. It really isn’t any different from anything else in tech, faster is almost always better. While deep learning is an excellent use of the processing power of a graphics card, it is not the only use.

Deep Learning 115

Deep Learning Machine Learning Data Science Cost-Benefit 115

O'Reilly on Data

MARCH 18, 2020

Last year, when we felt interest in artificial intelligence (AI) was approaching a fever pitch, we created a survey to ask about AI adoption. When we analyzed the results , we determined the AI space was in a state of rapid change, so we eagerly commissioned a follow-up survey to help find out where AI stands right now. Role of survey respondents.

Enterprise 311

Enterprise Deep Learning Data Governance Risk 311

Domino Data Lab

FEBRUARY 12, 2020

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested in additional insights, register for the upcoming Ray Summit. Introduction. If your team has started using ? and you’re wondering what it is, this post is for you.

Experimentation 137

Experimentation Modeling Data Science Management 137

datapine

SEPTEMBER 16, 2022

1) “The Visual Display of Quantitative Information” by Edward R. Previously, we discussed the top 19 big data books you need to read, followed by our rundown of the world’s top business intelligence books as well as our list of the best SQL books for beginners and intermediates. back on every dollar spent. click for book source**.

Visualization 304

Visualization Dashboards Data-driven Statistics 304

Domino Data Lab

JUNE 15, 2021

These companies often undertake large data science efforts in order to shift from “data-driven” to “model-driven” operations, and to provide model-underpinned insights to the business. The typical data science journey for a company starts with a small team that is tasked with a handful of specific problems. Why Snowflake UDFs.

Modeling 69

Modeling Data Science Data-driven Data Warehouse 69

Domino Data Lab

JULY 1, 2020

Recently there has been an influx of newcomers, and data scientists now have a wide range of implementations to choose from, such as Juptyer, Zeppelin, R Markdown, Spark Notebook, and Polynote. Notebooks are increasingly crucial in the data scientist’s toolbox. What do your readers need to learn from it?

Testing 116

Testing Data Science Machine Learning Data-driven 116

AWS Big Data

FEBRUARY 14, 2023

There are nine steps to complete this tutorial: Set up a source endpoint for AWS DMS. Teams often try to replicate thousands of jobs from relational databases with the same extract, transform, and load (ETL) pattern. There is lot of effort in maintaining the job states and scheduling these individual jobs.

Data Lake 78

Data Lake Statistics Data Architecture Finance 78

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Introduction. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science. Modernists and structuralists beware. Of course!!”

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

datapine

AUGUST 29, 2022

The rate at which data is generated has increased exponentially in recent years. To put this into perspective, 40,000 search queries are performed per second via Google alone – this equates to 3.46 million searches per day and 1.2 trillion each year. Essential Big Data And Data Analytics Insights.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

Domino Data Lab

AUGUST 22, 2019

A complementary Domino project is available. Introduction. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry. Chapter Introduction: Natural Language Processing.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Operating successfully as a data scientist in industry in such an environment is not only a matter of mathematics, but also one of intuition and discretion. by ERIC HOLLINGSWORTH A large part of the data we data scientists are asked to analyze was not collected with any specific analysis in mind, or perhaps any particular purpose at all.

Experimentation 61

Experimentation Testing Statistics Metrics 61