The topic for my talk at the Microsoft Build conference yesterday was “Migrating Existing Open Source Machine Learning to Azure”. The idea behind the talk was to show how you can take the open-source tools and workflows you already use for machine learning and data science, and easily transition them to the Azure cloud to take advantage of its capacity and scale. The theme for the talk was “no surprises”, and other than the Azure-specific elements I tried to stick to standard OSS tools rather than Microsoft-specific things, to make the process as familiar as possible. In the talk I covered: Using Visual Studio Code as a cross-platform, open-source editor and interface to Azure services Using the Azure CLI to script the deployment, functions, and deletion of resources in the Azure cloud Using the range of data science and machine learning tools…
Original Post: Open-Source Machine Learning in Azure
[unable to retrieve full-text content]To help data science teams adopt Docker and apply DevOps best practices to streamline machine learning delivery pipelines, we open-sourced a toolkit based on the popular cookiecutter project structure.
Original Post: Torus for Docker-First Data Science
Posted by Pi-Chuan Chang, Software Engineer and Lizzie Dorfman, Technical Program Manager, Google Brain TeamLast December we released DeepVariant, a deep learning model that has been trained to analyze genetic sequences and accurately identify the differences, known as variants, that make us all unique. Our initial post focused on how DeepVariant approaches “variant calling” as an image classification problem, and is able to achieve greater accuracy than previous methods.Today we are pleased to announce the launch of DeepVariant v0.6, which includes some major accuracy improvements. In this post we describe how we train DeepVariant, and how we were able to improve DeepVariant’s accuracy for two common sequencing scenarios, whole exome sequencing and polymerase chain reaction sequencing, simply by adding representative data into DeepVariant’s training process.Many Types of Sequencing DataApproaches to genomic sequencing vary depending on the type of DNA sample…
Original Post: DeepVariant Accuracy Improvements for Genetic Datatypes
Posted by Samuel Yang, Research Scientist, Google Accelerated Science TeamMany scientific imaging applications, especially microscopy, can produce terabytes of data per day. These applications can benefit from recent advances in computer vision and deep learning. In our work with biologists on robotic microscopy applications (e.g., to distinguish cellular phenotypes) we’ve learned that assembling high quality image datasets that separate signal from noise is a difficult but important task. We’ve also learned that there are many scientists who may not write code, but who are still excited to utilize deep learning in their image analysis work. A particular challenge we can help address involves dealing with out-of-focus images. Even with the autofocus systems on state-of-the-art microscopes, poor configuration or hardware incompatibility may result in image quality issues. Having an automated way to rate focus quality can enable the detection, troubleshooting and…
Original Post: Using Deep Learning to Facilitate Scientific Image Analysis
[unable to retrieve full-text content]Interactive visualization of large datasets on the web has traditionally been impractical. Apache Arrow provides a new way to exchange and visualize data at unprecedented speed and scale.
Original Post: Supercharging Visualization with Apache Arrow
[unable to retrieve full-text content]What I truly envision for deep school is that this will build a whole lot of Meetup nodes across the world where people will learn, mentor and network around sharing AI knowledge.
Original Post: DeepSchool.io: Deep Learning Learning