KDnuggets
DECEMBER 6, 2019
Are you interested in studying machine learning over the holidays? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to improving your machine learning skills.
Uber Engineering
DECEMBER 19, 2019
Every day around the world, millions of trips take place across the Uber network, giving users more reliable transportation through ridesharing, bikes, and scooters, drivers and truckers additional opportunities to earn, employees and employers more convenient business travel, and hungry … The post Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction appeared first on Uber Engineering Blog.
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Data Engineering Podcast
DECEMBER 30, 2019
Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how
Netflix Tech
DECEMBER 10, 2019
Netflix Hack Day?—?Fall 2019 By Tom Richards , Carenina Garcia Motion , and Leslie Posada Hack Day at Netflix is an opportunity to build and show off a feature, tool, or quirky app. The goal is simple: experiment with new ideas/technologies, engage with colleagues across different disciplines, and have fun! We know even the silliest idea can spur something more.
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.
Teradata
DECEMBER 11, 2019
Teradata's team of experts are chiming in on their top technology and business predictions for 2020 - from AI to Customer Experience to the Cloud. Read more!
Dataquest
DECEMBER 18, 2019
Today, Stacey Ustian is a data engineer. But the path that led her here wasn’t always easy, and there were a few bumps and twists along the way. Her journey to data science started in a rather unusual place: the law library. After earning her Master’s degree in Library and Information Science, Stacey had taken a job working in the library of a law firm.
Data Engineering Digest brings together the best content for data engineering professionals from the widest variety of industry thought leaders.
Uber Engineering
DECEMBER 17, 2019
Uber’s busy 2019 included our billionth delivery of an Uber Eats order, 24 million miles covered by bike and scooter riders on our platform, and trips to top destinations such as the Empire State Building, the Eiffel Tower, and the … The post Uber’s Data Platform in 2019: Transforming Information to Intelligence appeared first on Uber Engineering Blog.
Data Engineering Podcast
DECEMBER 22, 2019
Summary Transactional databases used in applications are optimized for fast reads and writes with relatively simple queries on a small number of records. Data warehouses are optimized for batched writes and complex analytical queries. Between those use cases there are varying levels of support for fast reads on quickly changing data. To address that need more completely the team at Materialize has created an engine that allows for building queryable views of your data as it is continually update
Netflix Tech
DECEMBER 10, 2019
Netflix Hack Day?—?Fall 2019 By Tom Richards , Carenina Garcia Motion , and Leslie Posada Hack Day at Netflix is an opportunity to build and show off a feature, tool, or quirky app. The goal is simple: experiment with new ideas/technologies, engage with colleagues across different disciplines, and have fun! We know even the silliest idea can spur something more.
Teradata
DECEMBER 29, 2019
How do you organize your business for analytics? Here are six steps your enterprise should take when creating an analytics team. Read more!
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.
Preset
DECEMBER 15, 2019
Announcing Elasticsearch in Superset, powered by a new open-source Python library from Preset
KDnuggets
DECEMBER 3, 2019
What follows is a set of broad recommendations, and it will inevitably require a lot of adjustments in each implementation. Given that caveat, here are our curriculum recommendations.
Uber Engineering
DECEMBER 10, 2019
Michelangelo , Uber’s machine learning (ML) platform, powers machine learning model training across various use cases at Uber, such as forecasting rider demand , fraud detection , food discovery and recommendation for Uber Eats , and improving the accuracy of … The post Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber appeared first on Uber Engineering Blog.
Data Engineering Podcast
DECEMBER 16, 2019
Summary Building clean datasets with reliable and reproducible ingestion pipelines is completely useless if it’s not possible to find them and understand their provenance. The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools.
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.
Netflix Tech
DECEMBER 10, 2019
Netflix Hack Day?—?Fall 2019 By Tom Richards , Carenina Garcia Motion , and Leslie Posada Hack Day at Netflix is an opportunity to build and show off a feature, tool, or quirky app. The goal is simple: experiment with new ideas/technologies, engage with colleagues across different disciplines, and have fun! We know even the silliest idea can spur something more.
Teradata
DECEMBER 9, 2019
Identifying and focusing on priority analytic use cases within your organization will ensure you are asking the right business questions. Find out more.
Confluent
DECEMBER 23, 2019
When KSQL was released, my first blog post about it showed how to use KSQL with Twitter data. Two years later, its successor ksqlDB was born, which we announced this […].
KDnuggets
DECEMBER 31, 2019
With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.
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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.
Rockset
DECEMBER 9, 2019
As a data engineer, my time is spent either moving data from one place to another, or preparing it for exposure to either reporting tools or front end users. As data collection and usage have become more sophisticated, the sources of data have become a lot more varied and disparate, volumes have grown and velocity has increased. Variety, Volume and Velocity were popularised as the three Vs of Big Data and in this post I’m going to talk about my considerations for each when selecting technologies
Data Engineering Podcast
DECEMBER 8, 2019
Summary Data warehouses have gone through many transformations, from standard relational databases on powerful hardware, to column oriented storage engines, to the current generation of cloud-native analytical engines. SnowflakeDB has been leading the charge to take advantage of cloud services that simplify the separation of compute and storage. In this episode Kent Graziano, chief technical evangelist for SnowflakeDB, explains how it is differentiated from other managed platforms and traditiona
Netflix Tech
DECEMBER 3, 2019
by David Berg, Ravi Kiran Chirravuri, Romain Cledat, Savin Goyal, Ferras Hamad, Ville Tuulos Continue reading on Netflix TechBlog ».
Teradata
DECEMBER 2, 2019
The cloud’s flexibility is becoming an essential success factor for businesses. But moving your data analytics to the cloud isn't just lift and shift. Read more.
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Outdated or absent analytics won’t cut it in today’s data-driven applications – not for your end users, your development team, or your business. That’s what drove the five companies in this e-book to change their approach to analytics. Download this e-book to learn about the unique problems each company faced and how they achieved huge returns beyond expectation by embedding analytics into applications.
Confluent
DECEMBER 18, 2019
The amount of time it takes for a message to move through a system plays a big role in the performance of distributed systems like Apache Kafka®. In Kafka, the […].
KDnuggets
DECEMBER 19, 2019
The third part in a series on leveraging techniques to take a look inside the black box of AI, this guide considers methods that try to explain each prediction instead of establishing a global explanation.
KDnuggets
DECEMBER 18, 2019
Ready to try to get hired as a data scientist for the first time? Avoiding these common mistakes won’t guarantee an offer, but not avoiding them is a sure fire way for your application to be tossed into the trash bin.
KDnuggets
DECEMBER 4, 2019
What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.
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.
KDnuggets
DECEMBER 5, 2019
Increase your confidence to perform data cleaning with a broader perspective of what datasets typically look like, and follow this toolbox of code snipets to make your data cleaning process faster and more efficient.
Data Engineering Podcast
DECEMBER 2, 2019
Summary The financial industry has long been driven by data, requiring a mature and robust capacity for discovering and integrating valuable sources of information. Citadel is no exception, and in this episode Michael Watson and Robert Krzyzanowski share their experiences managing and leading the data engineering teams that power the business. They shared helpful insights into some of the challenges associated with working in a regulated industry, organizing teams to deliver value rapidly and re
KDnuggets
DECEMBER 19, 2019
Over the years new alternative providers have risen to provided a solitary data science environment hosted on the cloud for data scientist to analyze, host and share their work.
KDnuggets
DECEMBER 18, 2019
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Automatic Text Summarization and the various ways it is used.
Speaker: Jon Harmer, Product Manager for Google Cloud
Move from feature factory to customer outcomes and drive impact in your business! This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. You will deepen your understanding of your customers and their needs as well as identifying and de-risking the different kinds of hypotheses built into your roadmap.
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