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Lesson Plan for Productionizing H2O for building Machine Learning Models

Related Lab Activity:
Class Introduction: Building AI/ML Models with H2O in Python
Background context for using H2O to build Large Language Models.
LLMS will be the next go to market channel for companies to connect and integrate themselves with Customers.
The goal has always been and will continue to be to integrate the company’s value delivery processes with Customer Needs. The better we can do at the job of predicting and satisfying customer needs: The more successful we will be.
LLMs will supercede what over the past 40 years of Business IT Development which saw the use of computers, business productivity software, the integration of these into DataMarts in the 1990s, the integration of website portals, and then social media, and Big Data.
The best of breed toolset we have to make this happen right now is H20.
H2O is a Java application. We can access it in our PYTHON programs using the PYTHON H2O Library,

Welcome, everyone. Today, we will be exploring H2O, a versatile, open-source machine learning platform that serves as an excellent tool for building AI and Machine Learning models using Python.
A PYTHON AI ML Application is what we have been refering to as the ML OPS MODEL.
H2O.ai is a software company that specializes in the development of artificial intelligence and machine learning products.
The Information Economy started in the 1970s when computers got cheap enough for everyone to afford.
Now, generative AI Language models are available to everyone.
We are now in the Cognition Economy.
Their main offering, H2O, is a software designed for data analysis and machine learning that has been adopted widely across various industries, from finance and healthcare to retail and telecommunications.
H2O provides an extensive suite of machine learning algorithms, including (but not limited to):
gradient boosting machines (GBM)
generalized linear models (GLM)
random forests,
deep learning.

Popular machine learning algorithms, including but not limited to:

Gradient Boosting Machines (GBM): GBMs are a family of powerful machine-learning techniques that have shown considerable success in a wide range of practical applications. They are highly customizable to the particular needs of the application, like being learned with respect to different loss functions. GBMs are used for regression and classification problems, and they produce a prediction model in the form of an ensemble of weak prediction models, typically decision trees
.
Generalized Linear Models (GLM): GLMs are a class of linear models that allow the response variable to have a non-normal distribution. They are used for regression and classification problems, and they can handle a wide range of response distributions, including binary, count, and continuous data
.
Random Forests: Random forests are a type of ensemble learning method that combines multiple decision trees to improve the accuracy of predictions. They are used for classification and regression problems, and they are particularly useful when dealing with high-dimensional data
.
Deep Learning: Deep learning is a subset of machine learning that uses artificial neural networks to model and solve complex problems. It is particularly useful for tasks such as image and speech recognition, natural language processing, and autonomous driving
.
Other popular machine learning algorithms include k-nearest neighbors (KNN), support vector machines (SVM), and naive Bayes classifiers.

What sets H2O apart is its performance and scalability, enabling users to handle large datasets efficiently, which is often a critical requirement in professional machine learning tasks.
One of the great advantages of H2O is its compatibility with Python.
Using the h2o-py library, Python developers can leverage H2O's capabilities directly from their Python scripts, making the process of building, validating, and deploying models smoother and more intuitive.
This may be applicable to your project.
The Python API provides a high degree of flexibility, allowing you to control all aspects of model:
training scoring
evaluation
In today's class, we will learn how to use H2O in Python for various tasks such as
data import and export
data transformation (data cleasing and reformatting)
model training
model validation
model deployment: Making it available for customers to use.
We will also take a closer look at some of H2O's unique features, such as AutoML, which automates the process of training and tuning a large selection of candidate models, and its POJO (Plain Old Java Object), and MOJO model formats, which simplify the process of deploying models in production environments.

By the end of this class, you'll be equipped with a robust toolset for handling a wide range of machine learning tasks.
Regardless of whether you are a beginner just starting out in machine learning or an experienced professional looking to broaden your toolkit, understanding H2O will give you a competitive edge in your data science journey.
Let's dive in and explore the exciting world of H2O and machine learning in Python!

Productionizing H2O for building Machine Learning Models
Briefly introduce H2O.ai and its role in the AI/ML ecosystem
.
Explain the importance of productionizing AI/ML models and how H2O can help
.
Discuss the difference between Plain Old Java Objects and MOJOs in H2O
.
Hands-on Activities (3 hours)
Activity 1: Setting up H2O (30 minutes)
Guide students through the process of installing and setting up H2O
.
Demonstrate how to access H2O from R, Python, and Flow
.
Activity 2: Building Models with H2O (1 hour)
Explain the key features of H2O, including leading algorithms, AutoML, and distributed in-memory processing
.
Walk students through solving a binary classification problem and a regression use-case using H2O Python
.
Introduce H2O's AutoML functionality and demonstrate how to use it
.
Activity 3: Generating and Deploying MOJOs (1 hour)
Explain the process of generating MOJOs from H2O models
.
Show students how to build and implement a MOJO using the H2O documentation
.
Discuss the importance of seamless collaboration between data science, DevOps, and IT teams in model deployment
.
Activity 4: MLOps with H2O.ai (30 minutes)
Introduce the concept of MLOps and its relevance in the AI/ML ecosystem
.
Discuss how H2O.ai can help with MLOps, including model deployment and operations
.
Wrap-up and Q&A (30 minutes)
Summarize the key takeaways from the lesson.
Encourage students to ask questions and clarify any doubts.
Provide resources for further learning, such as H2O.ai's Learning Center
and self-paced courses
.
This lesson plan will provide a comprehensive introduction to productionizing H2O in an AI/ML class, covering essential topics and hands-on activities to ensure students gain a solid understanding of the subject.

What is H2O and how is it used in AI/ML
H2O.ai overview, H2O.ai AI ML applications
References:
Lecture:
H2O is an open-source, distributed in-memory machine learning platform with linear scalability.
It supports widely used statistical and machine learning algorithms, including gradient boosted machines, generalized linear models, deep learning, and more.
H2O also offers an industry-leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models
.
In the AI/ML ecosystem, H2O is used to build predictive models and gain insights from data quickly and easily. It enables data scientists, machine learning engineers, and software developers to develop real-time interactive AI applications with sophisticated visualizations. H2O takes advantage of the computing power of distributed systems and in-memory computing to provide fast and efficient model training and deployment
.
The platform is popular among R and Python communities and can be accessed from R, Python, and Flow
.
H2O.ai, the company behind H2O, also offers other AI and machine learning platforms, such as H2O Driverless AI, which is an automatic machine learning platform that empowers data scientists to work on projects faster using automation, accomplishing tasks in minutes rather than months
.

Some examples of machine learning algorithms supported by H2O
H2O supported machine learning algorithms
H2O supervised learning algorithms
H2O unsupervised learning algorithms

Supervised learning algorithms
Unsupervised learning algorithms

H2O supervised learning algorithms list
H2O unsupervised learning algorithms list
References:
Lecture
H2O supports a wide range of machine learning algorithms for both supervised and unsupervised learning. Based on your interest in supervised and unsupervised learning algorithms, here are some examples:

Supervised Learning Algorithms (See Lab Code Examples using each of these):

Generalized Linear Models (GLM) - linear regression, logistic regression, etc.
Gradient Boosting Machines (GBM)
Distributed Random Forest (DRF)
Deep Learning (Neural Networks)
Naïve Bayes
Cox Proportional Hazards (CoxPH)
Stacked Ensembles

Unsupervised Learning Algorithms

K-means Clustering
Principal Components Analysis (PCA)
Word2Vec
Anomaly Detection using Stacked Autoencoders
These algorithms can be used for various tasks such as classification, regression, clustering, dimensionality reduction, and anomaly detection. H2O also offers an industry-leading AutoML functionality that automatically runs through all the algorithms and their hyperparameters to produce a leaderboard of the best models
.

How does H2O's implementation of gradient boosting differ from other libraries
References:
H2O gradient boosting implementation differences
H2O vs XGBoost gradient boosting
H2O vs LightGBM gradient boosting
H2O vs scikit-learn gradient boosting
Resources
Lecture
H2O's implementation of Gradient Boosting Machines (GBM) differs from other libraries in several ways:
Distributed and parallelized computation: H2O's GBM is designed to work efficiently on distributed systems and in-memory computing, allowing it to scale linearly and handle large datasets
.
Integration with H2O platform: H2O's GBM is integrated with the H2O platform, which provides a user-friendly web interface, support for R and Python, and seamless integration with other H2O algorithms and tools
.
Handling of categorical variables: H2O's GBM has an improved ability to train on categorical variables using the nbins_cats parameter, which allows for better handling of high cardinality categorical features
.
MOJO support: H2O's GBM supports MOJO (Model Object, Optimized) format, which is a compact, portable binary format for model deployment
.
While H2O's GBM shares some similarities with other libraries like XGBoost and LightGBM, such as the use of gradient boosting techniques and support for various loss functions, the differences mentioned above make H2O's GBM a unique and powerful tool for certain use cases, especially when working with large datasets and distributed systems
.

Lecture: Productionizing (meaning putting this into commercial use) H2O in Python for Building Machine Learning Models across Industry Verticals.
I. Introduction to H2O.ai and Python
H2O.ai is an open-source machine learning platform that provides a comprehensive and scalable solution for building machine learning models. With the ability to interface with Python, it offers a familiar environment for many data scientists, making it easier to build, validate, and deploy models.
H2O's Python library, h2o-py, provides an API for H2O's algorithms and features, allowing Python users to leverage H2O's capabilities directly from their Python scripts. It includes various algorithms for the things we need to do to build an LLM:
classification
regression
clustering
anomaly detection: fraud detection in credit card spending patterns, for example.
Making it an excellent choice for a variety of industry verticals and business domains.
II. Importance of Productionizing AI/ML Models: Examples from the Healthcare and Retail Industries
Once a model has been trained and validated, using H20, the next crucial step is to deploy it in a real-world environment, a process known as productionizing, that is: putting it in customers’ hands so they can start using it.
For instance, in healthcare, predictive models can help diagnose diseases or predict patient readmissions.
However, these models only generate value when integrated into the healthcare IT systems, where they can analyze real-time patient data and provide actionable insights to physicians.

Similarly, in the retail sector, ML models can analyze consumer behavior to make product recommendations or forecast sales. The ideal goal in retail is to have “JIT” Just in time stocking. Save the costs and lose of money by carrying a large standing inventory.
These models need to be integrated into the company's existing Supply Chains systems to analyze live transactional data from the cash registers that record sales, and provide real-time insights.
See this PowerPoint for some business background on how LLMs are used to process Big Data to drive customer insights:
Despite the critical nature of productionizing models, it's often a complex task due to issues like scalability, performance, robustness, monitoring, and interoperability.
This is where H2O and its Python module provide tremendous assistance.
III. H2O's POJOs and MOJOs: Python Examples from the Financial and Telecommunication Industries
H2O addresses the complexities of model deployment by enabling models to be exported as POJOs (Plain Old Java Object) and MOJOs (Model Object, Optimized: A MOJO is a Java Class with a certain specific data format).
image.png
Both formats are deployable in any Java-enabled environment and can be used from Python.
POJOs (simple Java Objects, not MOJOs): In the context of H2O, a POJO is a Java representation of a trained model.
Think about a MOJO as being a Database file, where the fields of the Database are the data elements being Modeled.
You can use it anywhere you can compile Java code.
Example: A financial institution uses machine learning for credit scoring.
The institution trains a Gradient Boosting Machine (GBM) model using H2O in Python.
The trained model can be exported as a POJO and integrated into the bank's loan processing system. Each loan application can then be scored in real-time to determine creditworthiness.
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator

# Initialize H2O cluster
h2o.init()

# Load data
data = h2o.import_file("loan_data.csv")

# Specify model parameters
gbm = H2OGradientBoostingEstimator()

# Train model
gbm.train(x=features, y=target, training_frame=data)

# Download the POJO
gbm.download_pojo(path=".", get_genmodel_jar=True)

MOJOs: The MOJO is a file which is one instantiation of the ML OPS MODEL. It is an optimized, portable model format that can represent models of any size and doesn't require a code compilation step.
Example: A telecommunications company uses H2O in Python to predict customer churn. Churn is change / reduction / increase in the number of customers. The model, a Deep Learning model, can be exported as a MOJO and integrated directly into the company's IT system, such as a customer management system. Customers can be scored for churn risk in real-time, enabling proactive customer retention strategies.
pythonCopy code
from h2o.estimators.deeplearning import H2ODeepLearningEstimator

# Load data
data = h2o.import_file("churn_data.csv")

# Specify model parameters
dl = H2ODeepLearningEstimator()

# Train model
dl.train(x=features, y=target, training_frame=data)

# Save the model as MOJO
dl.save_mojo(path=".")

The choice between POJO and MOJO will depend on your specific use case: the use case is the business process you are addressing.
Regardless, H2O's Python library provides an efficient way to transition your models from the training phase to generating real-world value in production environments.

Lecture: Develop the concepts of what MOJO is with examples to PYTHON and examples to AI ML Industry verticals

IV. A Deeper Dive into MOJOs with Python and Industry Vertical Examples
The MOJO Model is the Output of using MOJO. This is a file that is created on your File System.
MOJOs (Model Object, Optimized) are an advanced feature of H2O that simplifies the deployment of machine learning models.
These objects represent a compiled and optimized version of a trained machine learning model that H2O supports.
The idea behind MOJOs is to provide a way to take models, trained in H2O, and deploy them in a production setting while maintaining high performance and portability.
The key advantage of a MOJO over a POJO (Plain Old Java Object) is that it is designed to be compact, efficient, and deployable in any environment with Java runtime.
This means MOJOs are not just suitable for real-time predictions, but also for batch scoring and even for edge computing.
The “Edge” in this context is the point of interface between the User and the Data or Processing system they are interacting with. Edge Computing means that we factor our IT designs to try to do the processing as close to the requesting user as possible.
Let's expand this concept with Python code and industry-specific examples.
Example 1: Predictive Maintenance in the Manufacturing Industry
In the manufacturing sector, companies use machine learning for predictive maintenance. This involves training models to predict when equipment is likely to fail, allowing timely maintenance and preventing costly downtime.
Consider a company that trains a Random Forest model using H2O in Python to predict equipment failures based on sensor data.
pythonCopy code
from h2o.estimators.random_forest import H2ORandomForestEstimator

# Load sensor data
data = h2o.import_file("sensor_data.csv")

# Specify model parameters
rf = H2ORandomForestEstimator()

# Train model
rf.train(x=features, y=target, training_frame=data)

# Save the model as MOJO
rf.save_mojo(path=".")

The trained model can be exported as a MOJO and integrated directly into the company's equipment monitoring systems. Sensor data can be analyzed in real-time, providing predictive insights on potential equipment failures and allowing for proactive maintenance.
Example 2: Personalized Recommendations in the E-commerce Industry
In the e-commerce industry, personalized recommendations are a key application of machine learning. Businesses train models to analyze customer behavior and make product recommendations.
Let's say an e-commerce company trains a collaborative filtering model using H2O in Python to make these recommendations.
pythonCopy code
from h2o.estimators.glrm import H2OGeneralizedLowRankEstimator

# Load customer behavior data
data = h2o.import_file("behavior_data.csv")

# Specify model parameters
glrm = H2OGeneralizedLowRankEstimator(k=50)

# Train model
glrm.train(x=features, y=target, training_frame=data)

# Save the model as MOJO
glrm.save_mojo(path=".")

The trained model can be exported as a MOJO file on the file system, and consumed/read by other programs.
The Platform is the LLM that you build.
As users interact with the platform, they can be provided with real-time, personalized recommendations, enhancing user experience and driving additional sales.
The utility of MOJOs extends to all AI/ML industry verticals and represents one of the many ways H2O streamlines the process of building and deploying robust, scalable machine learning models.
By leveraging the versatility of MOJOs, organizations can realize the full value of their AI/ML investments, whether they are predicting healthcare outcomes, detecting financial fraud, optimizing supply chain logistics, or delivering personalized buying recommendations.
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