Evolution of a modern cloud-based data lake
March 3, 2020
Building a data lake is a hard task. You have to centralize all the data of the company in one place, it must be easily accessible, and governance has to be done right. And, last but not least, the price has to stay reasonable. All those aspects come up as quite a challenge. But never fear. Viacheslav Inozemtsev outlines the experience of building Zalando's data lake.
Fixing HTTP/2 and preparing for HTTP/3 over QUIC
February 28, 2020
Deploying HTTP/2 correctly can be challenging in practice, and HTTP/3 will make things even more difficult as the underlying QUIC protocol runs over user datagram protocol (UDP). Robin Marx explores practical proxying, caching, load balancing, and routing issues and how to overcome them.
Lightning Talk: Using a Raspberry Pi and Linux As a Middle Schoolers Desktop Computer
February 26, 2020
The Raspberry Pi is a popular hobbyist computer which is also capable of being a desktop computer. Running linux, a Raspberry Pi can be used to perform many tasks. As a student in middle school, I use …
Machine learning over real-time streaming data with TensorFlow
February 23, 2020
In many applications where data is generated continuously, combining machine learning with streaming data is imperative to discover useful information in real time. Yong Tang explores TensorFlow I/O, which can be used to easily build a data pipeline with TensorFlow and stream frameworks such as Apache Kafka, AWS Kinesis, or Google Cloud PubSub.
Executive Briefing: How the growth of voice-based AI stands to blur the lines of big data
February 19, 2020
Voiced-based AI continues to gain popularity among customers, businesses, and brands, but its important to understand that, while it presents a slew of new data at our disposal, the technology is still in its infancy. Andreas Kaltenbrunner examines three ways voice assistants will make big data analytics more complex and the various steps you can take to manage this in your company.
Implementing an AI multicloud broker
February 19, 2020
Holger Kyas details the AI multicloud broker, which is triggered by Amazon Alexa and mediates between AWS Comprehend (Amazon), Azure Text Analytics (Microsoft), GCP Natural Language (Google), and Watson Tone Analyzer (IBM) to compare and analyze sentiment. The extended AI part generates new sentences (e.g., marketing slogans) with a recurrent neural network (RNN).
TFX: Production ML pipelines with TensorFlow
February 18, 2020
Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe and Pedram Pejman explore Google's TFX, an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.
Staying safe in the AI era
February 10, 2020
Machine learning and artificial intelligence are no longer science fiction, so now you have to address what it takes to harness their potential effectively, responsibly, and reliably. Based on lessons learned at Google, Cassie Kozyrkov offers actionable advice to help you find opportunities to take advantage of machine learning, navigate the AI era, and stay safe as you innovate.
The future of Google Cloud data processing (sponsored by Google Cloud)
February 9, 2020
Open source has always been a core pillar of Google Clouds data and analytics strategy. James Malone examines how, as the community continues to set industry standards, the company continues to integrate those standards into its services so organizations around the world can unlock the value of data faster.
Data science + design thinking: A perfect blend to achieve the best user experience
February 6, 2020
Design thinking is a methodology for creative problem-solving developed at the Stanford d.school. The methodology is used by world-class design firms like IDEO and many of the world's leading brands like Apple, Google, Samsung, and GE. Michael Radwin prepares a recipe for how to apply design thinking to the development of AI/ML products.
TFX: Production ML pipelines with TensorFlow
February 2, 2020
Putting together an ML production pipeline for training, deploying, and maintaining ML and deep learning applications is much more than just training a model. Robert Crowe explores Google's open source community TensorFlow Extended (TFX), an open source version of the tools and libraries that Google uses internally, made using its years of experience in developing production ML pipelines.
Ask not what Brands can do for you
February 1, 2020
You'll see a lot of companies on the OSCON 2019 keynote stage, each sharing how much they love free and open source software. You may even (sarcastically) think, "Gosh am I ever glad I got to hear from all of these Brands!" VM Brasseur explains why this perspective isn't very helpful for the companies trying to do open source correctly. They need usour knowledge, experience, and compassion.
Be a docs star (sponsored by Google Cloud)
January 31, 2020
The world has enough rock stars; lets get some more docs stars. Join Megan Byrd-Sanicki to learn why docs is the superpower your project needs to grow adoptionand how Google supports open source with insights and programs that will help your project.
Developing serverless applications on Kubernetes with Knative (sponsored by Pivotal)
January 30, 2020
There's too much fragmentation for developers when it comes to deciding the right open source FaaS solution. Bryan Friedman and Brian McClain detail Knative, an open source project from Google, Pivotal, and other industry leaders that provides a set of common tooling on top of Kubernetes to help developers build functions.
Real-time streaming APIs: From data center to internet clients
January 26, 2020
When designing APIs such as the new GCP Firestore real-time database and Google Assistant, how did Google decide which trade-offs to make? Wenbo Zhu dives deep into the challenges faced while deploying a real-time streaming API designed for clients from data centers to the internet and details the trade-offs API developers need be aware of when designing such an API.
January 24, 2020
Your open source project is up and running - now what? Paris Pittman will walk through some of the practical, tactical work you can do to ensure your community doesn't just grow, but thrive. Through examples from multiple Google-supported open source communities, you'll learn how to create "gardeners" that do the hard but essential work that builds and sustains healthy communities.