FREE Weekly Kubeflow and MLOps Workshops

Register for an upcoming 60 minute virtual workshop led by the Arrikto Developer Relations team.

Developing Kubeflow Pipelines: Kaggle’s House Prices – Advanced Regression Techniques Competition

Kubeflow familiarity: Intermediate to Advanced

Predict sales prices and practice feature engineering, RFs, and gradient boosting.

About the Workshop:
In this workshop we’ll show how to turn Kaggle’s House Prices – Advanced Regression Techniques competition into a Kubeflow Pipeline using the KFP SDK and the Kale JupyterLab extension.

About the Kaggle Competition:
With 79 explanatory variables describing nearly every aspect of residential homes located in Ames, Iowa, Kaggle’s House Prices competition challenges data scientists to predict the final price of each home.

Notebooks & Pipelines Workshop: Kaggle’s Udacity Dog Breed Classification Example

Kubeflow familiarity: Beginner to Intermediate 

This workshop is for individuals already familiar with machine learning, but new to Kubeflow.

We’ll cover the following topics:

  • Overview of Kubeflow
  • Installing Kubeflow
  • About the Dog Breed Classification Example
  • Deploying a Notebook Server
  • Getting the Dog Breed Classification Example Up and Running
  • Exploring the Notebook
  • Deploying and Running a Pipeline
  • Examining the Results

Developing Kubeflow Pipelines: Kaggle’s Natural Language Processing with Disaster Tweets Competition

Kubeflow familiarity: Intermediate to Advanced

Predict which Tweets are about real disasters and which ones are not.

About the Workshop:
In this workshop we’ll show how to turn Kaggle’s Natural Language Processing with Disaster Tweets competition into a Kubeflow Pipeline using the KFP SDK and the Kale JupyterLab extension.

About the Kaggle Competition:
In this competition, you’re challenged to build a machine learning model that predicts which Tweets are about real disasters and which one’s aren’t. You’ll have access to a dataset of 10,000 tweets that were hand classified.

Notebooks & Pipelines Workshop: Kaggle’s OpenVaccine Machine Learning Example

Kubeflow familiarity: Intermediate to Advanced

This workshop is for individuals already familiar with machine learning, but new to Kubeflow. We’ll cover the following topics:

 

  • Overview of Kubeflow
  • Installing Kubeflow
  • About the OpenVaccine Example
  • Deploying a Notebook Server
  • Getting the OpenVaccine Example Up and Running
  • Exploring the Notebook
  • Deploying and Running a Pipeline
  • Examining the Results

Developing Kubeflow Pipelines: Kaggle’s Facial Keypoints Detection Competition

Kubeflow familiarity: Beginner to Intermediate

In this workshop we’ll show how to turn Kaggle’s Facial Keypoints Detection competition into a Kubeflow Pipeline using the KFP SDK and the Kale JupyterLab extension.

The objective of the Facial Keypoints Detection Kaggle competition, as the site notes, is to predict keypoint positions on face images. This can be used as a building block in several applications, such as:

  • Tracking faces in images and video
  • Analyzing facial expressions
  • Detecting dysmorphic facial signs for medical diagnosis
  • Biometrics/face recognition

In the world of machine learning, it is well known that detecting facial keypoints is a very difficult problem to solve. This is because facial features vary significantly from person to person, and even for a specific individual, there is a large amount of variation in facial images due to changing conditions such as 3D pose, size, position, viewing angle, and lighting. Although computer vision research has come a long way in addressing these difficulties, there still remain many opportunities for improvement!

 

Distributed Training Workshop

Kubeflow familiarity: Intermediate to Advanced

This workshop is for people that have some familiarity with Kubeflow and want to understand more about how to use distributed training for your models. In this 45 minute workshop, we will be using MiniKF to cover the following topics:

 

  • Getting Started with Kubeflow as a Service
  • Understanding the Capabilities of Kale
  • Review of Distributed Training
  • Decorating Python Code with Kale
  • Deploying a PyTorch Distributed Training  Job
  • Monitoring the Training Process
  • Setting Up Inference Serving
  • Cleaning Up Resources

Notebooks & Pipelines Workshop: Kaggle’s Blue Book for Bulldozers Machine Learning Example

Kubeflow familiarity: Beginner to Intermediate

This workshop is for individuals already familiar with machine learning, but new to Kubeflow. We’ll cover the following topics:

 

  • Overview of Kubeflow
  • Installing Kubeflow
  • About the Blue Book for Bulldozers Example
  • Deploying a Notebook Server
  • Getting the Bluebook for Bulldozers Example Up and Running
  • Exploring the Notebook
  • Deploying and Running a Pipeline
  • Examining the Results

Notebooks & Pipelines Workshop: Kaggle’s Titanic Disaster Machine Learning Example

Kubeflow familiarity: Beginner to Intermediate

This workshop is for individuals already familiar with machine learning, but new to Kubeflow. We’ll cover the following topics:

  • Overview of Kubeflow
  • Installing Kubeflow
  • About the Titanic Disaster Example
  • Deploying a Notebook Server
  • Getting the Titanic Disaster Example Up and Running
  • Exploring the Notebook
  • Deploying and Running a Pipeline
  • Examining the Results

Developing Kubeflow Pipelines: Kaggle’s Digit Recognizer Competition

Kubeflow familiarity: Beginner to Intermediate 

Learn computer vision fundamentals with the famous MNIST data.

About the Workshop:
In this workshop we’ll show how to turn Kaggle’s Digit Recognizer competition into a Kubeflow Pipeline using the KFP SDK and the Kale JupyterLab extension.

About the Kaggle Competition:
MNIST (“Modified National Institute of Standards and Technology”) is the de facto “hello world” dataset of computer vision. Since its release in 1999, this classic dataset of handwritten images has served as the basis for benchmarking classification algorithms. As new machine learning techniques emerge, MNIST remains a reliable resource for researchers and learners alike. In this competition, your goal is to correctly identify digits from a dataset of tens of thousands of handwritten images.

From Kubernetes to Kubeflow to MLOps: How Kubernetes and Kubeflow come together

Kubeflow familiarity: Beginner to Intermediate

This workshop is for individuals already familiar with machine learning, but new to Kubeflow. We’ll cover the following topics:

  • Overview of Kubernetes
  • Kubeflow Notebooks
  • Kubernetes & Kubeflow
  • Using Notebook Servers in Academy

FAQ

Who are these workshops for?
Data scientists, machine learning engineers, DevOps engineers and infrastructure operators.

How are the workshops delivered?
These instructor led workshops are delivered over Zoom.

What are the prerequisites for the workshops?
A basic understanding of cloud computing, Kubernetes and machine learning concepts is very helpful.