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Lab Workbook: Creating a simple MVP proof of concept AI model using H2O.ai

Here's a detailed step-by-step lab worksheet for creating a simple MVP proof of concept AI model using H2O.ai

Lab Worksheet: Building a Simple AI Model with H2O.ai

Objective: Create a basic machine learning model for predicting iris flower species using the H2O.ai platform.

Introduction to H2O.ai

H2O.ai is a leading open-source machine learning and artificial intelligence platform that has gained significant traction in the data science community.

Founded in 2011, H2O.ai provides a comprehensive suite of tools and technologies designed to democratize AI and make it accessible to businesses and individuals across various industries.

What is H2O?

H2O is an open-source, distributed machine learning platform that offers a wide range of algorithms and tools for data preprocessing, model building, evaluation, and deployment.

It's designed to be scalable, fast, and user-friendly, allowing data scientists, researchers, and businesses to quickly develop and implement AI solutions.

Key features of H2O include:

Support for various programming languages (R, Python, Java, Scala)

A web-based interface called Flow for interactive model building

AutoML capabilities for automated model selection and hyperparameter tuning

Distributed computing for handling large datasets

Integration with popular data science ecosystems like Spark and Hadoop

How H2O Helps Us

H2O.ai helps data practitioners and organizations in several ways:

Accessibility: It provides an easy-to-use platform for both beginners and experienced data scientists.

Efficiency: Its distributed computing capabilities allow for faster processing of large datasets.

Automation: AutoML features reduce the time and expertise required for model selection and tuning.

Flexibility: Support for multiple programming languages and integrations with other tools make it adaptable to various workflows.

Transparency: As an open-source platform, it allows for community contributions and scrutiny.

The Organization Behind H2O

H2O.ai is the company behind the H2O platform. Founded by Sri Ambati and Cliff Click, the company's mission is to democratize AI for everyone.

They aim to make AI more accessible, faster, and transparent while fostering a community of users and contributors.

Sponsorship and Funding

H2O.ai operates on a business model that combines open-source offerings with enterprise solutions. The core H2O platform is open-source and freely available, sponsored by the company and supported by the community. This approach allows for widespread adoption and community-driven development.

The company generates revenue through:

Enterprise editions with additional features and support

Consulting and training services

Cloud-based offerings

H2O.ai has received significant funding from venture capital firms and strategic investors.

As of 2021, the company had raised over $250 million from investors including Goldman Sachs, Nvidia, and Wells Fargo.

Mission and Vision

The mission of H2O.ai is to democratize AI by making it accessible, affordable, and adaptable for businesses of all sizes. They envision a future where AI can be leveraged to solve complex problems across industries, from healthcare and finance to retail and manufacturing.

By providing both open-source and enterprise solutions, H2O.ai aims to foster innovation in the AI community while also offering robust, scalable solutions for businesses. Their commitment to transparency, community engagement, and continuous improvement has made H2O a popular choice among data scientists and organizations looking to harness the power of AI.

In conclusion, H2O.ai represents a powerful confluence of open-source philosophy, cutting-edge AI technology, and business acumen.

By making advanced machine learning tools accessible to a wide audience, H2O.ai is playing a crucial role in advancing the field of AI and its practical applications across various sectors.

Prerequisites:

A computer with internet access

Basic understanding of Python (helpful but not required)

Step 1: Set up the H2O.ai environment

Go to

https://www.h2o.ai/⁠

and click on "Get Started"

Once logged in, navigate to the H2O-3 section (this is their open-source machine learning platform)

Step 2: Launch H2O-3 in the cloud

Click on "Launch H2O-3"

Choose the "H2O-3 on AWS" option (it's free for small workloads)

Wait for your instance to start (this may take a few minutes)

Step 3: Access the H2O Flow interface

Once your instance is ready, click on the provided link to open H2O Flow

You should see a notebook-style interface in your browser

Step 4: Import the Iris dataset

In the H2O Flow interface, type the following command in a new cell:

Copy

importFiles ["https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris.csv"]

Press Ctrl+Enter to run the cell

You should see a confirmation that the file was imported

Step 5: Parse the dataset

In a new cell, type:

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parseSetup ["https://s3.amazonaws.com/h2o-public-test-data/smalldata/iris/iris.csv"]

Run the cell

In the output, find the "frame_id" and copy it (it should look like "iris.hex")

Step 6: Create a dataframe

In a new cell, type (replace FRAME_ID with the id you copied):

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parseFiles

source_frames: ["FRAME_ID"]

destination_frame: "iris.hex"

parse_type: "CSV"

separator: 44

number_columns: 5

single_quotes: false

column_names: ["sepal_len","sepal_wid","petal_len","petal_wid","class"]

column_types: ["Numeric","Numeric","Numeric","Numeric","Enum"]

delete_on_done: true

check_header: 1

chunk_size: 4194304

Run the cell

Step 7: Split the data

In a new cell, type:

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splitFrame "iris.hex", ["train.hex","test.hex"], [0.8], 123456

Run the cell

Step 8: Train a model

In a new cell, type:

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buildModel

model_id: "iris_model"

training_frame: "train.hex"

validation_frame: "test.hex"

response_column: "class"

ignored_columns: []

algorithm: "gbm"

Run the cell

Wait for the model to train (this may take a few moments)

Step 9: Evaluate the model

In a new cell, type:

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getModel "iris_model"

Run the cell

Scroll through the output to see various metrics about your model's performance

Step 10: Make predictions

In a new cell, type:

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predict model: "iris_model", frame: "test.hex", predictions_frame: "predictions"

Run the cell

Step 11: View predictions

In a new cell, type:

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getFrameData "predictions"

Run the cell

You should see a table with the predicted iris species for each sample in the test set

Congratulations! You've just created a simple MVP AI model using H2O.ai. This model predicts iris species based on sepal and petal measurements.

Next steps:

Try modifying the model parameters to improve performance

Experiment with different algorithms (e.g., "deeplearning" instead of "gbm")

Import your own dataset and create a model for your specific use case

Remember, this is a basic introduction to H2O.ai. The platform offers many more advanced features for data preprocessing, model tuning, and deployment. As you become more comfortable with the basics, explore these additional capabilities to enhance your AI development skills.

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