News

Under the Hood of Uber ATG’s Machine Learning Infrastructure

Managing multiple machine learning models to enable self-driving vehicles is a challenge. Uber ATG developed a model life cycle for quick iterations, continuous delivery, and dependency management. As Uber experienced exponential growth over the last few years, now supporting 14 million trips each day, our engineers proved they could build for scale.

Introducing WebAssembly to Envoy and Istio.

Since adopting Envoy in 2016, the Istio project has always wanted to provide a platform on top of which a rich set of extensions could be built, to meet the diverse needs of our users. There are many reasons to add capability to the data plane of a service mesh — to support newer protocols, integrate with proprietary security controls, or enhance observability with custom metrics, to name a few. Over the last year and a half our team here at Google has been working on adding dynamic extensibility to the Envoy proxy using WebAssembly.

How N26 Scales Technology through Hypergrowth

As N26 grew fast, they had to scale their technology to keep up. This meant scaling not only their infrastructure, but also their teams; for instance, they had to decide how to distribute work over teams and what technology to use or not use. Folger Fonseca, software engineer and Tech Lead at N26, shared his experience from scaling technology at N26 at QCon London 2020.

4 CNN Networks Every Machine Learning Engineer Should Know

Over the years, variants of CNN architectures have been developed, leading to amazing advances in the field of deep learning. A good measure of this progress is the error rates in competitions such as the ILSVRC ImageNet challenge. In this competition, the top-5 error rate for image classification fell from over 26% to less than 3%.

Decoding NLP Attention Mechanisms

In this blogpost series, we will walk you through the rise of the transformer architecture. Our first stop will be a focus on the attention mechanism, which is the key component of this architecture. We will then move on to the transformer itself in part II, and finally, we’ll introduce BERT in part III.

Is your machine learning/quantum computer lying to you?

If you ask a machine learning model to give you an answer, how would you know if it’s skewed? How would you know if the inputs or outputs have been tampered with? RSA 2020 – Is your machine learning/quantum computer lying to you?

Container-Native Multi-Cluster Global Load Balancing on GCP

During a recent project, a Jetstack customer wanted to load balance global traffic to multiple Google Kubernetes Engine (GKE) clusters, while also benefiting from Google’s Cloud Armor to protect against denial of service (DoS) attacks. Additionally, they wanted to make use of container-native load balancing for improved traffic visibility and network performance. Google Cloud Platform (GCP) offers various load balancing solutions which are generally well documented and easy to use.

The Winding Road to Better Machine Learning Infrastructure Through Tensorflow Extended andKubeflow

Project LightSpeed: Rewriting the Messenger codebase

To build this new version of Messenger, we needed to rebuild the architecture from the ground up and rewrite the entire codebase. We are excited to begin rolling out the new version of Messenger on iOS. To make the Messenger iOS app faster, smaller, and simpler, we rebuilt the architecture and rewrote the entire codebase, which is an incredibly rare undertaking and involved engineers from across the company.

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