KDnuggets

JANUARY 16, 2020

With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond.

Technology 160

Technology 160

Uber Engineering

JANUARY 15, 2020

Uber leverages real-time analytics on aggregate data to improve the user experience across our products, from fighting fraudulent behavior on Uber Eats to forecasting demand on our platform. . As Uber’s operations became more complex and we offered additional features and … The post Engineering SQL Support on Apache Pinot at Uber appeared first on Uber Engineering Blog.

SQL 134

SQL Engineering Aggregated Data Data 134

Data Engineering Podcast

JANUARY 13, 2020

Summary The modern era of software development is identified by ubiquitous access to elastic infrastructure for computation and easy automation of deployment. This has led to a class of applications that can quickly scale to serve users worldwide. This requires a new class of data storage which can accomodate that demand without having to rearchitect your system at each level of growth.

SQL 100

SQL PostgreSQL MongoDB Database Design 100

Confluent

JANUARY 14, 2020

Part 1 of this series discussed the basic elements of an event streaming platform: events, streams, and tables. We also introduced the stream-table duality and learned why it is a […].

Kafka 94

Kafka IT Process Data 94

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

JANUARY 17, 2020

This article will tell you about the top 9 mobile apps that help the user in learning and practicing data science and hence is improving their productivity.

Data Science 157

Data Science Data 157

Teradata

JANUARY 12, 2020

Learn about the different ways to use R and Python with Vantage and the pros and cons of each option. Read more from our Teradata expert.

Python 80

Python SQL 80

Confluent

JANUARY 16, 2020

Now that we’ve learned about the processing layer of Apache Kafka® by looking at streams and tables, as well as the architecture of distributed processing with the Kafka Streams API […].

Kafka 26

Kafka Architecture Process 26

KDnuggets

JANUARY 17, 2020

This summary overviews the keynote at TensorFlow World by Jeff Dean, Head of AI at Google, that considered the advancements of computer vision and language models and predicted the direction machine learning model building should follow for the future.

Machine Learning 136

Machine Learning Building 136

Rockset

JANUARY 17, 2020

In this blog we will set up a real-time SQL API on Kafka using AWS Lambda and Rockset. At the time of writing (in early 2020) the San Francisco 49ers are doing remarkably well! To honor their success, we will focus on answering the following question. What are the most popular hashtags in tweets that mentioned the 49ers in the last 20 minutes? Because Twitter moves fast, we will only look at very recent tweets.

Kafka 40

Kafka SQL AWS Accessible 40

KDnuggets

JANUARY 16, 2020

This post is about fast-tracking the study and explanation of tree concepts for the data scientists so that you breeze through the next time you get asked these in an interview.

Algorithm 122

Algorithm Data Science Data 122

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

KDnuggets

JANUARY 15, 2020

Math for Programmers teaches you the math you need to know for a career in programming, concentrating on what you need to know as a developer.

Programming 122

Programming 122

KDnuggets

JANUARY 13, 2020

Building Machine Learning models is fun, but making sure we build the best ones is what makes a difference. Follow this quick guide to appreciate how to effectively evaluate a classification model, especially for projects where accuracy alone is not enough.

Machine Learning 112

Machine Learning Project Building 112

KDnuggets

JANUARY 15, 2020

In this post I want to show how to use public available (open) data to create geo visualizations in python. Maps are a great way to communicate and compare information when working with geolocation data. There are many frameworks to plot maps, here I focus on matplotlib and geopandas (and give a glimpse of mplleaflet).

Python 97

Python Data 97

KDnuggets

JANUARY 14, 2020

All you need to know about decision trees and how to build and optimize decision tree classifier.

Algorithm 122

Algorithm Building 122

Speaker: Aarushi Kansal, AI Leader & Author and Tony Karrer, Founder & CTO at Aggregage

Software leaders who are building applications based on Large Language Models (LLMs) often find it a challenge to achieve reliability. It’s no surprise given the non-deterministic nature of LLMs. To effectively create reliable LLM-based (often with RAG) applications, extensive testing and evaluation processes are crucial. This often ends up involving meticulous adjustments to prompts.

Building

KDnuggets

JANUARY 16, 2020

Whereas a data warehouse will need rigid data modeling and definitions, a data lake can store different types and shapes of data. In a data lake, the schema of the data can be inferred when it’s read, providing the aforementioned flexibility. However, this flexibility is a double-edged sword.

Data Lake 76

Data Lake Data Warehouse Data 76

KDnuggets

JANUARY 15, 2020

Which algorithm works best for unbalanced data? Are there any tradeoffs?

Algorithm 115

Algorithm Machine Learning Data 115

KDnuggets

JANUARY 13, 2020

When machine learning tools are developed by technology first, they risk failing to deliver on what users actually need. It can also be difficult for development teams to establish meaningful direction. This article explores the challenges of designing an interface that enables users to visualise and interact with insights from graph machine learning, and explores the very new, uncharted relationship between machine learning and UX.

Machine Learning 75

Machine Learning Designing Technology IT 75

KDnuggets

JANUARY 13, 2020

The new technique can really improve how deep learning models are trained at scale.

Deep Learning 98

Deep Learning 98

Advertisement

Are you trying to decide which entity resolution capabilities you need? It can be confusing to determine which features are most important for your project. And sometimes key features are overlooked. Get the Entity Resolution Evaluation Checklist to make sure you’ve thought of everything to make your project a success! The list was created by Senzing’s team of leading entity resolution experts, based on their real-world experience.

Project

KDnuggets

JANUARY 13, 2020

Also: The Book to Start You on Machine Learning; An Introductory Guide to NLP for Data Scientists with 7 Common Techniques; A Comprehensive Guide to Natural Language Generation; The Book to Start You on Machine Learning; 10 Python Tips and Tricks You Should Learn Today.

Data Science 61

Data Science Machine Learning Data Engineering Data Engineer 61

KDnuggets

JANUARY 15, 2020

Also: The Book to Start You on Machine Learning - KDnuggets; Top KDnuggets tweets, Jan 1-7: Introduction to #DataVisualization and Storytelling: A Guide For The #DataScientist #eBook; 7 Steps to a Job-winning Data Science Resume - KDnuggets; Tips for open-sourcing research code.

Data Engineering 68

Data Engineering Data Engineer Engineering Machine Learning 68

KDnuggets

JANUARY 14, 2020

Learn the basics of verifying segmentation, analyzing the data, and creating segments in this tutorial. When reviewing survey data, you will typically be handed Likert questions (e.g., on a scale of 1 to 5), and by using a few techniques, you can verify the quality of the survey and start grouping respondents into populations.

Data 60

Data 60

KDnuggets

JANUARY 16, 2020

Visit Deep Learning World, 11-12 May in Munich, to broaden your knowledge, deepen your understanding and discuss your questions with other Deep Learning experts!

Deep Learning 70

Deep Learning 70

Speaker: Margaret-Ann Seger, Head of Product, Statsig

Experimentation is often seen as an aspirational practice, especially at smaller, fast-moving companies who are strapped for time and resources. So, how can you get your team making decisions in a more data-driven way while continuing to remain lean and maintaining ship velocity? In this webinar, Margaret-Ann Seger, Head of Product at Statsig, will teach you how to build an experimentation culture from the ground-up, graduating from just getting started with data-driven development to operating

Building

KDnuggets

JANUARY 13, 2020

This online course is available – for free – to anyone interested in building practical skills in using data to solve problems better.

Building 59

Building Education Data 59

KDnuggets

JANUARY 14, 2020

Here are 7 powerful AI led use cases both for linear television and for OTT apps that are transforming the live sports production landscape.

Machine Learning 60

Machine Learning 60

KDnuggets

JANUARY 15, 2020

This week: learn the 5 must-have data science skills for the new year; find out which book is THE book to get started learning machine learning; pick up some Python tips and tricks; learn SQL, but learn it the hard way; and find an introductory guide to learning common NLP techniques.

Machine Learning 50

Machine Learning Data Science SQL Python 50

KDnuggets

JANUARY 15, 2020

This year’s NEURIPS-2019 Vancouver conference recently concluded and featured a dozen papers on disentanglement in deep learning. What is this idea and why is it so interesting in machine learning? This summary of these papers will give you initial insight in disentanglement as well as ideas on what you can explore next.

Deep Learning 48

Deep Learning Machine Learning IT 48

Speaker: David Bard, Principal at VP Product Coaching

In the fast-paced world of digital innovation, success is often accompanied by a multitude of challenges - like the pitfalls lurking at every turn, threatening to derail the most promising projects. But fret not, this webinar is your key to effective product development! Join us for an enlightening session to empower you to lead your team to greater heights.

Certification

Confluent

JANUARY 13, 2020

This four-part series explores the core fundamentals of Kafka’s storage and processing layers and how they interrelate. In this first part, we begin with an overview of events, streams, tables, […].

Kafka 25

Kafka Process 25

Confluent

JANUARY 15, 2020

Part 2 of this series discussed in detail the storage layer of Apache Kafka: topics, partitions, and brokers, along with storage formats and event partitioning. Now that we have this […].

Kafka 17

Kafka Process 17