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Course Outline
Introduction
- Kubeflow on OpenShift vs public cloud managed services
Overview of Kubeflow on OpenShift
- Code Read Containers
- Storage options
Overview of Environment Setup
- Setting up a Kubernetes cluster
Setting up Kubeflow on OpenShift
- Installing Kubeflow
Coding the Model
- Choosing an ML algorithm
- Implementing a TensorFlow CNN model
Reading the Data
- Accessing a dataset
Kubeflow Pipelines on OpenShift
- Setting up an end-to-end Kubeflow pipeline
- Customizing Kubeflow Pipelines
Running an ML Training Job
- Training a model
Deploying the Model
- Running a trained model on OpenShift
Integrating the Model into a Web Application
- Creating a sample application
- Sending prediction requests
Administering Kubeflow
- Monitoring with Tensorboard
- Managing logs
Securing a Kubeflow Cluster
- Setting up authentication and authorization
Troubleshooting
Summary and Conclusion
Requirements
- 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.
Audience
- 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.
Testimonials
Trainer was very knowledgeable and open to questions, liked to draw diagrams and explained things in a pretty good way
Deep Learning with TensorFlow 2.0 Course
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
TensorFlow Extended (TFX) Course
Very knowledgeable
Usama Adam - TWPI
Natural Language Processing with TensorFlow Course
The way he present everything with examples and training was so useful
Ibrahim Mohammedameen - TWPI
Natural Language Processing with TensorFlow Course
Organization, adhering to the proposed agenda, the trainer's vast knowledge in this subject
Ali Kattan - TWPI
Natural Language Processing with TensorFlow Course
I started with close to zero knowledge, and by the end I was able to build and train my own networks.
Huawei Technologies Duesseldorf GmbH
TensorFlow for Image Recognition Course
About face area.
- 中移物联网
Deep Learning for NLP (Natural Language Processing) Course
Very updated approach or api (tensorflow, kera, tflearn) to do machine learning
Paul Lee
TensorFlow for Image Recognition Course
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