Classroom Activity - Building an AI ML OPS Model Using Python H2O

Objective: To understand and reinforce the concepts of applying Python H2O to build a simple AI Machine Learning Operations (MLOPS) model.
Materials Needed:
A Computer with Python and H2O installed
Internet access
Activity Details:
Introduction (Duration: 15 minutes)
Start with a brief discussion on the concepts of AI, Machine Learning, and Operations (MLOPS), and how Python H2O is used in this field.
Demonstration (Duration: 30 minutes)
Demonstrate the process of building a simple MLOPS model using Python H2O.
Start by importing the necessary libraries.
Explain the roles of these libraries in the model-building process.

Building an AI ML OPS Model Using Python H2O - Classroom Activity


To understand the basic concepts of MLOPS and be able to build a simple MLOPS model using Python H2O.

Instruction Sheet:

Step 1: Importing Necessary Libraries

The first step in building an MLOPS model using Python H2O involves importing the necessary Python libraries. These libraries provide the tools and functions that we'll use throughout the model building process.
Here's how you do it:
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import h2o
from h2o.automl import H2OAutoML

Role of Imported Libraries:

h2o: This is the main H2O library we'll be using. It provides an open-source software to setup your h2o platform to do data analysis. It is used for preparing data and training models, and it provides algorithms for machine learning tasks.
H2OAutoML: This is a class from the H2O library that provides Automated Machine Learning (AutoML). It automates the process of training and tuning a large selection of candidate models. It includes data pre-processing, variable selection, model selection, and hyperparameter tuning.

Step 2: Initializing H2O Cluster: This is a Java Class, run under the JRE

After importing the necessary libraries, the next step is to start the H2O cluster. This is done with the h2o.init() function.
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The h2o.init() function starts the H2O Java Web Server in the JVM on your OS with specified configurations. It connects to an existing H2O JVM instance based on the PORT Number or attempts to start a new one.


We need to install the JRE on our Operating System.
Remember to check the versions of your libraries to ensure compatibility.
You can use the print(h2o.__version__) function to print the version of h2o currently installed on your machine.
Next steps will include:
importing and cleaning the data
defining the model
training the model\
evaluating model performance.
These will be covered in the subsequent sessions. Next week, I will give you some training data to practice on.

Expected Outcome:

By the end of this step, you should have successfully set up your Python environment with the necessary libraries for building an MLOPS model using Python H2O.
You should also have a basic understanding of what these libraries do.
Load a sample dataset and explain its features and target variable.
Discuss how to split the data into a training set and a test set.
Show them how to initialize H2O and create an H2O frame.
Demonstrate how to train the model using the training set and H2O's automatic machine learning function.
Discuss how to make predictions using the test set and evaluate the model's performance.
Hands-On Activity (Duration: 60 minutes)
Assign each student a different dataset and guide them through the process of building their own MLOPS model using Python H2O.
Students should be able to import the necessary libraries, load their dataset, split the data, initialize H2O, create an H2O frame, train the model, make predictions, and evaluate their model's performance.
Discussion and Review (Duration: 15 minutes)
Once everyone has finished, facilitate a discussion about the challenges encountered during the activity and how they overcame them. Review the key concepts learned and answer any questions students may have.
By the end of this activity, students should be able to understand the basic concepts of MLOPS and be able to build a simple MLOPS model using Python H2O.
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