Crypto-Mining Attacks Targeting Kubernetes Clusters via Kubeflow Instances. Microsoft warns of a large-scale cryptocurrency mining malware campaign that targets Kubernetes clusters through Kubeflow machine learning instances.
KRSH (pronounce, krush) is a tool that allows you to declaratively manage Kubeflow's pipelines. By managing Kubeflow Pipeline through KRSH, developers can reduce the cost of managing Pipeline Versions and deploy pipelines much faster than ever before. KRSH was greatly inspired by Terraform's behavior through Write, Plan, and Apply Cycle and its declarative management of resources. Also, since KRSH provides the KRSH Project Boilerplate through the krsh create command, the developer who develops the pipeline no longer needs to worry about which project structure to choose to manage the Kubeflow Pipeline.
From Experimentation to Products: The Production Machine Learning Journey • Robert will discuss the use of ML pipeline architectures for implementing production ML applications, and in particular we review Google’s experience with TensorFlow Extended (TFX), as well as the advantages of containerizing pipeline architectures using platforms such as Kubeflow.
Machine Learning Orchestration on Kubernetes using Kubeflow. In this article, let's explore how you can deploy machine learning workflows on Kubernetes in simple, portable, and scalable way using Kubeflow.
NVIDIA has created DeepOps primarily for installing Kubernetes on a set of hosts with GPUs. But, it can also be used to target non-GPU hosts. Tutorial: Install Kubernetes and Kubeflow on a GPU Host with NVIDIA DeepOps.
Scaling ML pipelines with KALE — the Kubeflow Automated Pipeline Engine - In this talk, you will learn about KALE — the Kubeflow Automated Pipeline Engine. With KALE you can finally link the work done by data scientists in Jupyter Notebooks to a production-grade pipeline that trains the models at scale and serves them in real-time.
ML Ops design patterns with Kubeflow Pipelines - When moving your ML workflows from notebook exploration to production, many new problems can arise. We'll talk about some of the reasons this transition can be difficult; discuss patterns that can address these problems; then show how Kubeflow Pipelines (KFP) can be used to support and implement these patterns, and demo KFP in action.
Why we abandoned Kubeflow for our machine learning reference architecture. We're building a reference machine learning architecture: a free set of documents and scripts to combine our chosen open source tools into a reusable machine ...
Kubeflow From the End User’s Perspective: The Good, The Bad, and The Ugly - Kubernetes has become the most popular open-source container orchestration platform for managing cloud-native workloads. Furthermore, given the ubiquitousness of Artificial Intelligence (AI), Kubeflow has risen as a new open-source project tailoring Kubernetes to optimize for Machine Learning (ML) stacks, solving the devops chores and performance bottleneck usually plaguing a production ML system. Despite the promise, we (as a contributor to Kubeflow) have identified glitches of Kubeflow when being applied in the wild. We have surveyed 50+ real Kubeflow users (both system administrators and machine learning developers) from our enterprise customers. We will share how Kubeflow solves their pain-points, what pitfalls and disappointments they encountered in daily usage scenario, and how to evolve Kubeflow to be more practical and generally applicable..
Operating Deep Learning Pipelines Anywhere Using Kubeflow - Kubeflow makes it very easy for data scientist to build their own data science pipeline with Jupyter Notebooks, TensorFlow, TensorBoard and Model serving. In this talk we will walk through building a production grade data science pipeline using Kubeflow and open source data, streaming and CI/CD automation tools. Audience will learn about need for data preparation (which is frequently performed using Apache Spark or Apache Flink), data storage (using HDFS, Cassandra), automation via CI/CD (using Jenkins) and request streaming (using Apache Kafka). In this talk we look at building and operate a complete deep learning pipeline around Kubeflow for multiple tenants and topics such as: * Data Preparation/Cleansing (using Apache Spark) * Data and Model Storage * Model Serving * Distributed Training * Monitoring * Automation using CI/CD * Infrastructure Management across multiple tenants
A year ago, we introduced the Kubeflow project to make end-to-end ML pipelines on Kubernetes composable, portable & scalable. Today, thanks to passionate contributors from all over the world, we have the most popular ML platform for Kubernetes. At this Kubecon, we are announcing Kubeflow 1.0, graduating the project to generally available. In this talk, we will cover never before seen features: a web-based UI, simplified setup & sophisticated ML tooling including hyperparameter search and Google's TensorFlow Extended project. Additionally, we will be demonstrating the newly integrated Pipelines project wiring together multi-cloud ML with continuous training and hosted services. Thanks to Kubernetes native extensibility, we are able to bring ML to an entirely new audience, where as long as you can code, you can build complete end-to-end solutions.
In this episode of AI Adventures, we will cover how to manage a production machine learning pipeline using TFX. This video showcases how to solve a binary classification problem using a dataset from BigQuery and then tests the results in Kubeflow.
Deploying Kubeflow on A Local Kubernetes Cluster (minikube). This is a tutorial on deploying Kubeflow on a local Kubernetes cluster from scratch.
Learn how to use Kubeflow to set up machine learning orchestration on Kurbetenes for your AI projects. Kubeflow is a curated collection of machine learning frameworks and tools. Kubeflow abstracts the Kubernetes components by providing UI, CLI, and easy workflows that non-kubernetes users can use. For the ML capabilities, Kubeflow integrates the best framework and tools such as TensorFlow, MXNet, Jupyter Notebooks, PyTorch, and Seldon Core.
Kubeflow 1.3 Will Make You Fall in Love with MLOps. Kubeflow 1.3 is a big feature release, and it aims at bringing Kubeflow closer to the Data Scientist, making it a top choice for doing ML on Kubernetes.
Tensorflow Extended(TFX) is a production-scaled machine learning platform, taking advantage of the best qualities from Tensorflow and Sibyl. TFX contains a sequence of components to implement ML pipelines that are scalable and give high-performance machine learning tasks.
In this article, let's explore how you can deploy machine learning workflows on Kubernetes in simple, portable, and scalable way using Kubeflow.
Learn how to easily deploy ML models to production. One of the known truths of the Machine Learning(ML) world is that it takes a lot longer to deploy ML models to production than to develop it.¹ Let’s discuss some different options you have when it comes to deploying ML models.
Learn how to install and configure kubeflow on your local machine in order to be able to start using kubeflow locally without the need for a cloud provider.
I’ll show you how you can build an automated and cost-efficient ML model retraining pipeline using Kubeflow Pipelines. Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers. Kubeflow is designed to benefit from Kubernetes strengths and that’s what makes it very attractive.