To start building an ML Platform, you should support the basic ML user journey of notebook prototyping to scaled training to online serving. If your organization has multiple teams, you may additionally need to support administrative requirements of multi-user support with identity-based authentication and authorization. Two popular OSS projects – Kubeflow and Ray – together can support these needs. Kubeflow provides the multi-user environment and interactive notebook management. Ray orchestrates distributed computing workloads across the entire ML lifecycle, including training and serving.

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve as a web app using Microsoft Azure Web App Services. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline on Google Kubernetes Engine. Previously we demonstrated how to deploy a ML pipeline on Heroku PaaS and how to deploy a ML pipeline on Azure Web Services with a Docker container.

Spotify has open-sourced their Terraform module for running machine-learning pipeline software Kubeflow on Google Kubernetes Engine (GKE). By switching their in-house ML platform to Kubeflow, Spotify engineers have achieved faster time to production and are producing 7x more experiments than on the previous platform. In a recent blog post, Spotify's product manager Josh Baer and ML engineer Samuel Ngahane described Spotify's "Paved Road" for machine learning: "an opinionated set of products and configurations to deploy an end-to-end machine learning solution using our recommended infrastructure." By adopting these standards, Spotify's machine learning engineers no longer need to build or maintain infrastructure and instead can focus on their ML experiments. Since launching the platform in mid-2019, about 100 internal users have adopted it and run up to 18,000 experiments.

With Kafka Streams, if something breaks, it just picks up right where it left off. We can write distributed applications without having to worry about all the typical worries that engineers at Confluent have already dealt with." In addition to the usual machine-learning challenges, one of the challenges Recursion is trying to solve is cultural / organizational. "One of the things that makes us really unique, especially in the biotech space, is that we're very collaborative and cross-function in our teams," Mabey says. "Our data scientists are working side-by-side with our software engineers and our biologists." This creates technical challenges, too -- and technical solutions. "In some stacks, you'll see engineers taking data scientists' work in Python and recoding it in Java.

It's a catalog of reusable models that can be quickly deployed to one of the execution environments of AI Platform. The catalog has a collection of models based on popular frameworks such as Tensorflow, PyTorch, Keras, XGBoost and Scikit-learn. Each of the models is packaged in a format that can be deployed in Kubeflow, deep learning VMs backed by GPU or TPU, Jupyter Notebooks, or Google's own AI APIs. Each model is tagged with labels that make it easy to search and discover content based on a variety of attributes. AI Platform Deep Learning VM Image makes it easy and fast to instantiate a VM image containing the most popular deep learning and machine learning frameworks on a Google Compute Engine instance.

Do you want to try out a new version of Apache Spark without waiting on the entire release process? Does testing bleeding-edge builds on production data sound fun to you? (Hint: it's safer not to.) Then this is the blog post for you, my friend! We'll help you experiment with code that hasn't even been reviewed yet. If you're a little cautious, following my advice might sound like a bad idea, and often it is, but if you need to ensure that a pull request (PR) really fixes your bug, or your application will keep running after the release candidate (RC) process is finished, this post will help you try out new versions of Spark with a minimum amount of fuss.

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions. This document details the steps needed to run the Kubeflow project in any environment in which Kubernetes runs. Our goal is to help folks use ML more easily, by letting Kubernetes to do what it's great at: Because ML practitioners use so many different types of tools, it is a key goal that you can customize the stack to whatever your requirements (within reason), and let the system take care of the "boring stuff." While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way for spinning up best of breed OSS solutions. This document details the steps needed to run the kubeflow project in any environment in which Kubernetes runs. Our goal is to help folks use ML more easily, by letting Kubernetes to do what it's great at: Because ML practitioners use so many different types of tools, it is a key goal that you can customize the stack to whatever your requirements (within reason), and let the system take care of the "boring stuff." While we have started with a narrow set of technologies, we are working with many different projects to include additional tooling.