日本のMLOpsトレーニング

MLOps (Machine Learning Operations) is the practice of integrating data science and operations to help manage the ML lifecycle. MLOps provides the ability to automate the reproduction of machine learning model development and training.This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.By the end of this training, participants will be able to:

• Build reproducible workflows and machine learning models.
• Manage the machine learning lifecycle.
• Track and report model version history, assets, and more.
• Deploy production ready machine learning models anywhere.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

MLOps is a set of tools and methodologies for combining Machine Learning and DevOps practices. The goal of MLOps is to automate and optimize the deployment and maintenance of ML systems in production.This instructor-led, live training (online or onsite) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.By the end of this training, participants will be able to:

• Install and configure various MLOps frameworks and tools.
• Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
• Prepare, validate and version data for use by ML models.
• Understand the components of an ML Pipeline and the tools needed to build one.
• Experiment with different machine learning frameworks and servers for deploying to production.
• Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is a machine learning library and Kubernetes is an orchestration platform for managing containerized applications. This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an AWS EC2 server. By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow and other needed software on AWS.
• Use EKS (Elastic Kubernetes Service) to simplify the work of initializing a Kubernetes cluster on AWS.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud. By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Google Cloud Platform (GCP). By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow and other needed software on GCP and GKE.
• Use GKE (Kubernetes Kubernetes Engine) to simplify the work of initializing a Kubernetes cluster on GCP.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other GCP services to extend an ML application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to IBM Cloud Kubernetes Service (IKS). By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow and other needed software on IBM Cloud Kubernetes Service (IKS).
• Use IKS to simplify the work of initializing a Kubernetes cluster on IBM Cloud.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other IBM Cloud services to extend an ML application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

MLflow is an open source platform for streamlining and managing the machine learning lifecycle. It supports any ML (machine learning) library, algorithm, deployment tool or language. Simply add MLflow to your existing ML code to share the code across any ML library being used within your organization.This instructor-led, live training (online or onsite) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.By the end of this training, participants will be able to:

• Install and configure MLflow and related ML libraries and frameworks.
• Appreciate the importance of trackability, reproducability and deployability of an ML model
• Deploy ML models to different public clouds, platforms, or on-premise servers.
• Scale the ML deployment process to accommodate multiple users collaborating on a project.
• Set up a central registry to experiment with, reproduce, and deploy ML models.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

このインストラクター主導の日本でのライブトレーニング（オンラインまたはオンサイト）は、Kubernetesで機械学習ワークフローを構築、デプロイ、管理したい開発者やデータサイエンティストを対象としています。 このトレーニングが終了するまでに、参加者は次のことができるようになります。

• AWS EKS（Elastic Kubernetes Service）を使用して、オンプレミスとクラウドにKubeflowをインストールして構成します。
• DockerコンテナとKubernetesに基づいてMLワークフローを構築、デプロイ、管理します。
• さまざまなアーキテクチャとクラウド環境で機械学習パイプライン全体を実行します。
• Kubeflowを使用してJupyterノートブックを生成および管理します。
• MLトレーニング、ハイパーパラメータ・チューニング、および複数のプラットフォームにわたるワークロードの提供を構築します。

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications. OpenShift is a cloud application development platform that uses Docker containers, orchestrated and managed by Kubernetes, on a foundation of Red Hat Enterprise Linux. This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to an OpenShift on-premise or hybrid cloud.

• By the end of this training, participants will be able to:
• Install and configure Kubernetes and Kubeflow on an OpenShift cluster.
• Use OpenShift to simplify the work of initializing a Kubernetes cluster.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Call public cloud services (e.g., AWS services) from within OpenShift to extend an ML application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Kubeflow is a toolkit for making Machine Learning (ML) on Kubernetes easy, portable and scalable.This instructor-led, live training (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.By the end of this training, participants will be able to:

• Install and configure Kubeflow on premise and in the cloud.
• Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
• Run entire machine learning pipelines on diverse architectures and cloud environments.
• Using Kubeflow to spawn and manage Jupyter notebooks.
• Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.
• To learn more about Kubeflow, please visit: https://github.com/kubeflow/kubeflow