28 hours (usually 4 days including breaks)
- An understanding of machine learning concepts.
- Knowledge of cloud computing concepts.
- A general understanding of containers (Docker) and orchestration (Kubernetes).
- Some Python programming experience is helpful.
- Experience working with a command line.
- Data science engineers.
- DevOps engineers interesting in machine learning model deployment.
- Infrastructure engineers interesting in machine learning model deployment.
- Software engineers wishing to automate the integration and deployment of machine learning features with their application.
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.
- Kubeflow on IKS vs on-premise vs on other public cloud providers
Overview of Kubeflow Features on IBM Cloud
- IBM Cloud Object Storage
Overview of Environment Setup
- Preparing virtual machines
- Setting up a Kubernetes cluster
Setting up Kubeflow on IBM Cloud
- Installing Kubeflow through IKS
Coding the Model
- Choosing an ML algorithm
- Implementing a TensorFlow CNN model
Reading the Data
- Accessing the MNIST dataset
Pipelines on IBM Cloud
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Running an ML Training Job
- Training an MNIST model
Deploying the Model
- Running TensorFlow Serving on IKS
Integrating the Model into a Web Application
- Creating a sample application
- Sending prediction requests
- Monitoring with Tensorboard
- Managing logs
Securing a Kubeflow Cluster
- Setting up authentication and authorization
Summary and Conclusion
Adjusting to our needs
Sumitomo Mitsui Finance and Leasing Company, Limited