Course Code: machinemlops
Duration: 28 hours
Prerequisites:
  • Experience with Python
Overview:

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.
  • Plan, build, and deploy machine learning models in KNIME.
  • Implement end to end data science projects.
  • Make data driven decisions for operations.

Format of the Course

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

Introduction

Overview of Kubeflow Features and Components

  • Containers, manifests, etc.

Overview of a Machine Learning Pipeline

  • Training, testing, tuning, deploying, etc.

Deploying Kubeflow to a Kubernetes Cluster

  • Preparing the execution environment (training cluster, production cluster, etc.)
  • Downloading, installing and customizing.

Running a Machine Learning Pipeline on Kubernetes

  • Building a TensorFlow pipeline.
  • Building a PyTorch pipleline.

Visualizing the Results

  • Exporting and visualizing pipeline metrics

Customizing the Execution Environment

  • Customizing the stack for diverse infrastructures
  • Upgrading a Kubeflow deployment

Running Kubeflow on Public Clouds

  • AWS, Microsoft Azure, Google Cloud Platform

Managing Production Workflows

  • Running with GitOps methodology
  • Scheduling jobs
  • Spawning Jupyter notebooks

Troubleshooting

Getting Started with Knime

  • What is KNIME?
  • KNIME Analytics
  • KNIME Server

Machine Learning

  • Computational learning theory
  • Computer algorithms for computational experience

Preparing the Development Environment

  • Installing and configuring KNIME

KNIME Nodes

  • Adding nodes
  • Accessing and reading data
  • Merging, splitting, and filtering data
  • Grouping and pivoting data
  • Cleaning data

Modeling

  • Creating workflows
  • Importing data
  • Preparing data
  • Visualizing data
  • Creating a decision tree model
  • Working with regression models
  • Predicting data
  • Comparing and matching data

Learning Techniques

  • Working with random forest techniques
  • Using polynomial regression
  • Assigning classes
  • Evaluating models

Summary and Conclusion