KubeFlow Bootcamp

Name: KubeFlow Bootcamp
Rating: 3.8 (323 reviews)

Learn how to use Kubeflow for Machine Learning at scale on Google Cloud!

Created byJose Portilla, Pierian Training

Last updated 7/2023

English

What you'll learn

Understand the core concepts of Kubeflow and its role in building scalable and portable machine learning workflows on Google Cloud.
Learn to deploy and manage Kubeflow pipelines to automate end-to-end machine learning workflows on Google Cloud.
Gain proficiency in utilizing Kubeflow components
Develop skills in leveraging Google Cloud's AI Platform for training and serving machine learning models within the Kubeflow environment.
Explore the integration of Kubeflow with other Google Cloud services
Master the use of Kubeflow's monitoring and logging capabilities to ensure effective tracking and debugging of machine learning workflows.
Learn best practices for scaling and optimizing machine learning workloads using Kubeflow on Google Cloud
Understand security and governance considerations when working with Kubeflow on Google Cloud

Course content

5 sections • 55 lectures • 8h 10m total length

FAQ AND COURSE DOWNLOADS0:39
Course Overview2:51

Overview of Google Cloud Section1:36
What is Cloud? A Cloud Computing Overview12:02
GCP Network Infrastructure12:19
Explore how Google Cloud network infrastructure connects computers worldwide through regions, zones, and multi-region locations, enabling fast storage and compute with fault tolerance.
GCP Network Connections18:58
Why Choose GCP?12:37
Google Cloud Account Set-Up6:59
Billing and Budgets4:41
Billing Tour: DEMO7:05
Setting a Budget Alert: DEMO4:16
Google Cloud Storage Options5:39
Cloud Storage Overview23:16
Cloud Storage DEMO16:45

Introduction to Kubernetes Section1:17
Kubernetes Basics Overview7:37
Understanding Containers9:07
Understanding Nodes and Control Plane4:26
Understanding Kubernetes API6:47
Learn how to use the Kubernetes API to deploy a hello world app, compare imperative and declarative workflows, and manage deployments, pods, and services with kubectl and gcloud.
Container Images6:47
Cloud Build DEMO15:49
Set up Cloud Build and Google Container Registry, create a hello world shell script and Dockerfile, and deploy using a declarative cloudbuild.yaml with history in the registry.
Google Kubernetes Engine - GKE4:13
Kubernetes Architecture15:44
GKE via GUI DEMO5:33
Kubectl5:18
GKE via Command Line - DEMO19:24
Kubernetes Deployments7:56
Kubernetes Deployment Updates3:28
Kubernetes Deployment Strategies8:12
Kubernetes Deployment - DEMO12:07
Demonstrates deploying nginx with a Kubernetes deployment and triggering rollouts via kubectl, including a deployment manifest and a two-node GKE cluster setup in Cloud Shell.
Kubernetes Pod Networking4:59
Kubernetes Storage with Volumes7:29
Volume Storage - DEMO12:49

Introduction to Machine Learning Fundamentals1:48
Machine Learning Pathway10:16
Why Machine Learning?9:15
Types of Machine Learning Algorithms7:47
Supervised Learning Process13:41
Master the supervised learning process: from defining features and labels to train-test splits, model training, and evaluating predictions with hyperparameter adjustments and cross-validation.
AI vs ML5:24
AI and ML on Google Cloud8:29
Explore Google Cloud AI and ML options, from custom model coding to AutoML and pre-trained API services, under Vertex AI.
Vertex AI Overview5:38
Explore Vertex AI, a unified Google Cloud platform that builds, deploys, and scales ML models from data prep to production, with AutoML, notebooks, and model monitoring.
Vertex AI Workbench: Notebooks in the Cloud8:55
Launch and manage Vertex AI Workbench notebooks on Google Cloud to run Jupyter labs, configuring CPU or GPU, and using Python libraries like pandas, scikit-learn, and TensorFlow.

Kubeflow Section Overview6:14
Vertex AI Pipelines13:02
Vertex AI Pipelines and SDKs5:08
Compare Kubeflow pipelines SDK with TFX and learn how Vertex AI pipelines orchestrate scalable, serverless ML workflows on Google Cloud.
Vertex AI Pipeline DEMO13:48
AI Pipeline via Notebook DEMO25:28
Kubeflow Overview6:15
Kubeflow DSL6:41
Kubeflow Pre-Built Components3:42
Kubeflow Python Components5:01
Kubeflow Custom Components5:51
Kubeflow Compilation4:45
Compile Kubeflow pipelines to YAML with the PHP SDK compiler to produce a hermetic pipeline representation and read the top-of-file comments for name, description, inputs, and outputs.
Machine Learning Kubeflow Pipeline DEMO25:09
Demonstrates building a Kubeflow pipeline on Google Cloud, using BigQuery data, training a scikit-learn model, and deploying the endpoint via Vertex AI; includes setup, compilation, and debugging tips.
Continuous Training with Kubeflow9:17

Requirements

Understanding of general Cloud Service Providers (we provide a Google Cloud intro)
Understand general machine learning concepts
Permissions to subscribe to Google Cloud (credit card may be required)
Some Python Programming Experience

Description

Unlock the Power of Machine Learning Workflows on Google Cloud with Kubeflow!

Supercharge your data science skills and revolutionize your machine learning workflows with our comprehensive Udemy course on Kubeflow on Google Cloud. Dive into the world of scalable and portable ML pipelines with this step-by-step guide to harnessing the full potential of Kubeflow.

Master the art of automating end-to-end machine learning workflows using Kubeflow and discover how it seamlessly integrates with the robust infrastructure of Google Cloud. Whether you're a data scientist, ML engineer, or aspiring AI enthusiast, this course equips you with the knowledge and hands-on experience to take your projects to new heights.

What you'll learn:

Unleash the true potential of Kubeflow by understanding its core concepts and its role in building scalable ML workflows.
Deploy and manage Kubeflow pipelines effortlessly to automate and streamline your ML projects on Google Cloud.
Harness the power of Kubeflow's components to optimize hyperparameter tuning and workflow orchestration.
Maximize the potential of Google Cloud's AI Platform for efficient model training and deployment within the Kubeflow ecosystem.
Integrate Kubeflow seamlessly with other Google Cloud services like BigQuery and Cloud Storage for enhanced data processing and storage.
Master the art of monitoring and logging in Kubeflow to ensure the success of your ML projects with real-time insights and debugging capabilities.
Scale and optimize your ML workloads effectively using Kubeflow, leveraging distributed training and resource allocation techniques.
Embrace best practices in security and governance, ensuring compliance and data privacy when working with Kubeflow on Google Cloud.

Don't miss out on this opportunity to become a Kubeflow expert and accelerate your career in the rapidly evolving field of AI and ML. Enroll now and unlock the full potential of Kubeflow on Google Cloud with our comprehensive Udemy course!

Who this course is for:

Cloud Developers interested in using Kubeflow on Google Cloud

KubeFlow Bootcamp

What you'll learn

Explore related topics

Course content

Course Introduction2 lectures • 4min

Google Cloud Overview and Setup12 lectures • 2hr 6min

Kubernetes Overview19 lectures • 2hr 39min

Understanding Machine Learning Fundamentals9 lectures • 1hr 11min

Kubeflow Pipelines with Google Cloud13 lectures • 2hr 10min

Requirements

Description

Who this course is for: