Building and Deploying Machine Learning Models with H2O Driverless AI

Table of Contents

1. Introduction
2. Importing Data Set
3. Splitting Data Set
4. Visualizing Data Set
5. Creating an Experiment
6. Fine-tuning an Experiment
7. Experiment Settings
8. Model Building and Testing
9. Model Interpretation
10. Model Deployment
11. Conclusion

Introduction

H2O Driverless AI is a powerful tool for building and deploying machine learning models. In this article, we will explore the various features of H2O Driverless AI and how to use them to build and deploy models. We will cover topics such as importing data sets, splitting data sets, visualizing data sets, creating experiments, fine-tuning experiments, experiment settings, model building and testing, model interpretation, and model deployment.

Importing Data Set

The first step in building a machine learning model is to import a data set. H2O Driverless AI supports importing data sets from a variety of sources. Once the data set is imported, we can do a number of things with it. We can intelligently split the data set into training and testing sets, Visualize the data set, and Create experiments.

Splitting Data Set

Splitting a data set into training and testing sets is an important step in building a machine learning model. H2O Driverless AI allows us to split the data set into training and testing sets on the fly. We can specify the names of the two data sets and the percentage of the split that we want to use. This is useful if we did not already have a test and train split ahead of time.

Visualizing Data Set

Visualizing a data set is an important step in understanding the data and identifying Patterns. H2O Driverless AI allows us to automatically visualize the data set. It will generate interesting plots Based on the data sets provided automatically. We can navigate through the various plots via the carousel. We can also download any plot for later use.

Creating an Experiment

Creating an experiment is the next step in building a machine learning model. H2O Driverless AI allows us to create an experiment by simply clicking predict. We will need a trained model and optionally a tested model for our data. We'll be using a target of default payment next month, trying to determine if a customer is going to default on their payment on the following month based on their payment history.

Fine-tuning an Experiment

Fine-tuning an experiment is an important step in building a machine learning model. H2O Driverless AI allows us to fine-tune an experiment by adjusting the knobs that dictate how we prioritize accuracy, time, and interpretability. Changes to the dials will be reflected in the panel on the left-HAND side. We can also perform fine-tuning of an experiment from the export settings.

Experiment Settings

Experiment settings are important in building a machine learning model. H2O Driverless AI allows us to set certain values such as whether or not to build the Python scoring pipeline, whether or not to build a Java scoring pipeline, and whether or not to require certain algorithms or turn them off.

Model Building and Testing

Model building and testing is the heart of building a machine learning model. H2O Driverless AI builds and tests models in real-time. We can see real-time metrics of the model being created. Different charts are available such as a seeker procession versus recall lift and gains chart KS chart. We can also see our actual resource consumption such as CPU usage, memory usage, and GPU usage if the machine has GPUs.

Model Interpretation

Model interpretation is an important step in understanding the machine learning model. H2O Driverless AI allows us to interpret the model by looking at feature importance and sharply explanations for the final model generated by Driverless AI or the circuit models which are proxies for the final models created by Driverless AI. We can also download any necessary resources from the resources tab, including the Python client so that we can Interact with Driverless AI with Python.

Model Deployment

Model deployment is the final step in building a machine learning model. H2O Driverless AI allows us to create automatic one-click deployments to compute platforms like AWS lambda ec2 local, drast server, etc based on our experiments. We just have to provide the proper credentials.

Conclusion

In conclusion, H2O Driverless AI is a powerful tool for building and deploying machine learning models. We have covered various topics such as importing data sets, splitting data sets, visualizing data sets, creating experiments, fine-tuning experiments, experiment settings, model building and testing, model interpretation, and model deployment. With H2O Driverless AI, we can build and deploy machine learning models quickly and easily.

Highlights

• H2O Driverless AI is a powerful tool for building and deploying machine learning models.
• H2O Driverless AI supports importing data sets from a variety of sources.
• H2O Driverless AI allows us to split the data set into training and testing sets on the fly.
• H2O Driverless AI allows us to automatically visualize the data set.
• H2O Driverless AI builds and tests models in real-time.
• H2O Driverless AI allows us to interpret the model by looking at feature importance and sharply explanations for the final model generated by Driverless AI or the circuit models which are proxies for the final models created by Driverless AI.
• H2O Driverless AI allows us to create automatic one-click deployments to compute platforms like AWS lambda ec2 local, drast server, etc based on our experiments.

FAQ

Q: What is H2O Driverless AI? A: H2O Driverless AI is a powerful tool for building and deploying machine learning models.

Q: What sources does H2O Driverless AI support for importing data sets? A: H2O Driverless AI supports importing data sets from a variety of sources.

Q: Can H2O Driverless AI split a data set into training and testing sets on the fly? A: Yes, H2O Driverless AI allows us to split the data set into training and testing sets on the fly.

Q: Can H2O Driverless AI automatically visualize a data set? A: Yes, H2O Driverless AI allows us to automatically visualize the data set.

Q: Can H2O Driverless AI build and test models in real-time? A: Yes, H2O Driverless AI builds and tests models in real-time.

Q: Can H2O Driverless AI interpret the model? A: Yes, H2O Driverless AI allows us to interpret the model by looking at feature importance and sharply explanations for the final model generated by Driverless AI or the circuit models which are proxies for the final models created by Driverless AI.

Q: Can H2O Driverless AI create automatic one-click deployments to compute platforms? A: Yes, H2O Driverless AI allows us to create automatic one-click deployments to compute platforms like AWS lambda ec2 local, drast server, etc based on our experiments.