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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Transportation Engineering Architecture 144

KDnuggets

NOVEMBER 28, 2019

Open source is becoming the standard for sharing and improving technology. Some of the largest organizations in the world namely: Google, Facebook and Uber are open sourcing their own technologies that they use in their workflow to the public.

Data Science 144

Data Science Project Data Technology 144

Confluent

NOVEMBER 20, 2019

Today marks a new release of KSQL, one so significant that we’re giving it a new name: ksqlDB. Like KSQL, ksqlDB remains freely available and community licensed, and you can […].

IT 111

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Netflix Tech

APRIL 29, 2019

By Pythonistas at Netflix, coordinated by Amjith Ramanujam and edited by Ellen Livengood As many of us prepare to go to PyCon, we wanted to share a sampling of how Python is used at Netflix. We use Python through the full content lifecycle, from deciding which content to fund all the way to operating the CDN that serves the final video to 148 million members.

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Speaker: Dr. Greg Loughnane and Chris Alexiuk

Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. They're often developing using prompting, Retrieval Augmented Generation (RAG), and fine-tuning (up to and including Reinforcement Learning with Human Feedback (RLHF)), typically in that order. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are le

Process

Cloudera

JULY 10, 2019

Open source has been core to the missions of both Hortonworks and Cloudera and central to our values and culture. With more than 700 engineers in the new Cloudera, our company writes a prodigious amount of open source code each year that’s contributed to more than 30 different open source projects. We’re also a very innovative open source company, having collectively launched more than a dozen new open source projects since the founding of the two companies. .

Kafka 108

Kafka Project Data Science Architecture 108

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 142

Data Engineering Building Big Data 142

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.

Machine Learning 140

Machine Learning Deep Learning Python 140

KDnuggets

DECEMBER 26, 2019

Getting ready to leap into the world of Data Science? Consider these top machine learning courses curated by experts to help you learn and thrive in this exciting field.

Machine Learning 138

Machine Learning Data Science Data Deep Learning 138

Uber Engineering

NOVEMBER 26, 2019

When something goes wrong with a piece of code, engineers want to know all the relevant details of the error immediately so they can get right to work remedying the malfunction. . However, as technology has advanced, measuring system metrics and … The post Optimizing Observability with Jaeger, M3, and XYS at Uber appeared first on Uber Engineering Blog.

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Engineering Coding Systems Technology 130

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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.

Data Science 126

Data Science Data IT Education 126

KDnuggets

NOVEMBER 28, 2019

This article will walk through an extractive text summarization process, using a simple word frequency approach, implemented in Python.

Python 142

Python Process 142

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.

Machine Learning 121

Machine Learning Data Engineering Food 121

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.

Data Science 118

Data Science Machine Learning Data 118

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.

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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.

Data 114

Data 114

KDnuggets

NOVEMBER 27, 2019

Also: Deep Learning for Image Classification with Less Data; How to Speed up Pandas by 4x with one line of code; 25 Useful #Python Snippets to Help in Your Day-to-Day Work; Automated Machine Learning Project Implementation Complexities.

Coding 111

Coding Python Deep Learning Machine Learning 111

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.

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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 108

Data Datasets Coding Process 108

KDnuggets

NOVEMBER 5, 2019

Artificial Intelligence continues to fill the media headlines while scientists and engineers rapidly expand its capabilities and applications. With such explosive growth in the field, there is a great deal to learn. Dive into these 10 free books that are must-reads to support your AI study and work.

Engineering 111

Engineering Media IT 111

Uber Engineering

OCTOBER 16, 2019

Michelangelo , Uber’s machine learning (ML) platform, supports the training and serving of thousands of models in production across the company. Designed to cover the end-to-end ML workflow, the system currently supports classical machine learning, time series forecasting, and deep … The post Evolving Michelangelo Model Representation for Flexibility at Scale appeared first on Uber Engineering Blog.

Machine Learning 111

Machine Learning Engineering Systems Designing 111

KDnuggets

OCTOBER 7, 2019

Are you looking to learn natural language processing? This collection of 10 free top notch courses will allow you to do just that, with something for every approach to learning NLP and its varied topics.

Process 111

Process IT 111

KDnuggets

AUGUST 23, 2019

Entirely implemented with NumPy, this extensive tutorial provides a detailed review of neural networks followed by guided code for creating one from scratch with computational graphs.

Coding 111

Coding Python 111

KDnuggets

SEPTEMBER 17, 2019

We identify two main groups of Data Science skills: A: 13 core, stable skills that most respondents have and B: a group of hot, emerging skills that most do not have (yet) but want to add. See our detailed analysis.

Data Science 111

Data Science Data Scala Deep Learning 111

KDnuggets

AUGUST 16, 2019

As a data scientist, you are in high demand. So, how can you increase your marketability even more? Check out these current trends in skills most desired by employers in 2019.

Data 111

Data Data Science 111

KDnuggets

AUGUST 8, 2019

Graph Machine Learning uses the network structure of the underlying data to improve predictive outcomes. Learn how to use this modern machine learning method to solve challenges with connected data.

Machine Learning 111

Machine Learning Data 111

KDnuggets

OCTOBER 22, 2019

For full-stack data science mastery, you must understand data management along with all the bells and whistles of machine learning. This high-level overview is a road map for the history and current state of the expansive options for data storage and infrastructure solutions.

Data Management 111

Data Management Data Data Storage Management 111

Uber Engineering

MAY 14, 2019

Uber operates on a global scale across more than 600 cities, with our apps relying entirely on wireless connectivity from over 4,500 mobile carriers. To deliver the real-time performance expected from Uber’s users, our mobile apps require low-latency and highly … The post Employing QUIC Protocol to Optimize Uber’s App Performance appeared first on Uber Engineering Blog.

Engineering 112

Engineering Architecture 112

Confluent

MARCH 26, 2019

Apache-Kafka ® -based applications stand out for their ability to decouple producers and consumers using an event log as an intermediate layer. One result of this is that producers and consumers don’t know about each other, as there is no direct communication between them. This enables choreographed service collaborations, where many components can subscribe to events stored in the event log and react to them asynchronously.

Kafka 111

Kafka Metadata Architecture Database 111

Confluent

MARCH 13, 2019

Kafka Connect is part of Apache Kafka ® and is a powerful framework for building streaming pipelines between Kafka and other technologies. It can be used for streaming data into Kafka from numerous places including databases, message queues and flat files, as well as streaming data from Kafka out to targets such as document stores, NoSQL, databases, object storage and so on.

Kafka 111

Kafka Bytes Metadata Data 111

Uber Engineering

MARCH 7, 2019

Reliability engineering teams at Uber build the tools, libraries, and infrastructure that enable engineers to operate our thousands of microservices reliably at scale. At its essence, reliability engineering boils down to actively preventing outages that affect the mean time between … The post Using Machine Learning to Ensure the Capacity Safety of Individual Microservices appeared first on Uber Engineering Blog.

Machine Learning 111

Machine Learning Engineering Building IT 111

Uber Engineering

AUGUST 22, 2019

As Uber Freight marked its second anniversary, we went back to the drawing board to redesign its app. The original carrier app was successful for owner-operators with one or two drivers, but it wasn’t optimized for larger fleets—feedback we … The post Building the New Uber Freight App as Lists of Modular, Reusable Components appeared first on Uber Engineering Blog.

Building 111

Building Engineering IT Architecture 111

Confluent

JUNE 25, 2019

It’s official: Apache Kafka ® 2.3 has been released! Here is a selection of some of the most interesting and important features we added in the new release. Core Kafka. KIP-351 and KIP-427: Improved monitoring for partitions which have lost replicas. In order to keep your data safe, Kafka creates several replicas of it on different brokers. Kafka will not allow writes to proceed unless the partition has a minimum number of in-sync replicas.

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