KDnuggets

DECEMBER 7, 2022

Find out where you need to be to start your Machine Learning journey and what you need to do to succeed in the field.

Machine Learning 122

Machine Learning 122

Cloudera

DECEMBER 7, 2022

In case you were not aware, there’s a little event called the World Cup that’s happening right now. This World Cup has been notable for a couple reasons. The first being the timing — no summer watch party barbeques this time around, instead FIFA is breaking from tradition and running the tournament in the northern hemisphere winter months to spare the players the experience of playing soccer (Cloudera is headquartered in the US, so it is “soccer”) in temperatures exceeding 41.5°C (Cloudera is he

Machine Learning 73

Machine Learning Technology Cloud Algorithm 73

KDnuggets

DECEMBER 7, 2022

Top 10 Data Science Myths Busted • 4 Useful Intermediate SQL Queries for Data Science • Scikit-Learn Cheat Sheet for Machine Learning • How I got 4 Data Science Offers and Doubled my Income 2 Months after being Laid off • 8 Best Python Image Manipulation Tools.

Data Science 95

Data Science SQL Machine Learning Python 95

LinkedIn Engineering

DECEMBER 7, 2022

We are constantly striving to improve the experience on LinkedIn for our members and customers, with research and experimentation, such as A/B Testing, playing a key role in that work.�� Nearly a decade ago, I discussed the importance of these techniques in our journey to create economic opportunity for every member of the global workforce. Today we have a strong principled approach to how we design and run A/B tests on everything from UI designs to AI algorithms, and feature launches to bug fix

Algorithm 55

Algorithm Data Analysis Designing Engineering 55

Speaker: Timothy Chan, PhD., Head of Data Science

Are you ready to move beyond the basics and take a deep dive into the cutting-edge techniques that are reshaping the landscape of experimentation? 🌐 From Sequential Testing to Multi-Armed Bandits, Switchback Experiments to Stratified Sampling, Timothy Chan, Data Science Lead, is here to unravel the mysteries of these powerful methodologies that are revolutionizing how we approach testing.

Data Science

KDnuggets

DECEMBER 7, 2022

What’s the problem with running a transformer model on a book with 1 million tokens? What can be a solution to this problem?

Machine Learning 110

Machine Learning 110

Scott Logic

DECEMBER 7, 2022

Sadly my time working with a colleague had come to an end and I wanted to give him a token of my appreciation. In these days of hybrid working, I thought what better way to show my appreciation to an infrequent Vim user, than to add another rarely useful peripheral to their bag! Just what is a Vim clutch? In case you’re not familiar with vim itself, a very quick recap.

Building 52

Building Coding Programming Manufacturing 52

Meltano

DECEMBER 7, 2022

ELT is becoming the default choice for data architectures and yet, many best practices focus primarily on “T”: the transformations. But the extract and load phase is where data quality is determined for transformation and beyond. As the saying goes, “garbage in, garbage out.” Robust EL pipelines provide the foundation for delivering accurate, timely, and error-free data.

Raw Data 52

Raw Data Metadata Data Database 52

KDnuggets

DECEMBER 7, 2022

A brief overview of what moment-generating functions are and how they are used in probability and statistics.

Data Science 108

Data Science Data 108

Confluent

DECEMBER 7, 2022

Learn how to build a real-time streaming application using Apache Kafka® and.NET producer and consumer clients.

Building 57

Building Kafka Process 57

Monte Carlo

DECEMBER 7, 2022

“Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’” – Randall Munroe In this article, Ryan Kearns, co-author of O’Reilly’s Data Quality Fundamentals and a data scientist at Monte Carlo, discusses the limitations of segmentation analysis when it comes to root cause analysis for data teams, and proposes a better approach: ELT schedules as Bayesian Networks.

Data Engineering 52

Data Engineering Data Engineer Engineering Data 52

Speaker: Anne Steiner and David Laribee

As a concept, Developer Experience (DX) has gained significant attention in the tech industry. It emphasizes engineers’ efficiency and satisfaction during the product development process. As product managers, we need to understand how a good DX can contribute not only to the well-being of our development teams but also to the broader objectives of product success and customer satisfaction.

Engineering

Monte Carlo

DECEMBER 7, 2022

GitLab has made a name for itself as a company that prioritizes radical transparency – in other words, building an open and collaborative culture, not just internally but with the broader technical community. We sat down with Rob Parker, Senior Director of Data & Analytics, to understand how he applies this concept to his work building and scaling GitLab’s data platform with collaboration, scalability, and trust in mind.

Building 52

Building Pipeline-centric Data Data Programming 52