PySpark - Sparkling Water - Module 6.0
Scalable Machine Learning with H2O and Sparkling Water
1. Introduction to Distributed Machine Learning
We’ll begin by exploring the core concepts behind distributed machine learning. This includes a look at how H2O and Sparkling Water fit into this space. You’ll understand H2O’s role in scaling ML models and how it integrates with Spark for large-scale data processing.
2. Dataset Selection
To test the power of distributed computing, ideally we’ll work with a sizable dataset, but since we are sitting on Community edition of DataBricks we will skip using large datasets.
We will load some dataset using H2O, and apply various data preprocessing techniques, exploring both traditional pandas/sklearn methods and H2O’s distributed capabilities.
3. Module Breakdown
a. Preprocessing:
Begin by loading and exploring the dataset using H2O. From there, handle data cleaning, feature engineering, and scaling. Experiment with both local methods and H2O-specific techniques to compare workflows.
b. Model Building (Local):
Train machine learning models like Random Forests or Gradient Boosting Machines using H2O locally. Track performance metrics like accuracy and F1-score, along with the time it takes to train each model.
c. Distributed Computing with Sparkling Water:
Next, you’ll set up a Spark cluster, either locally or in the cloud, to train the same models on Sparkling Water. Compare the performance of your distributed models against those trained on your local machine, with an emphasis on training time and model accuracy.
d. Hyperparameter Tuning:
Apply H2O’s grid search to tune hyperparameters for your models. Evaluate how well the tuning process scales when done in a distributed setting via Sparkling Water.
e. Model Evaluation:
Assess the performance of your models based on accuracy, precision, and training time. You’ll analyze how distributed computing impacts both the quality and speed of training, especially when working with larger datasets.
f. Visualization & Reporting:
You’ll present your results through visualizations, comparing model performance between local and distributed setups. This will culminate in a final report summarizing your findings and reflecting on the advantages and challenges of distributed ML.
Bonus: AutoML and Cloud Deployment
For those who want to dive deeper, you can experiment with H2O’s AutoML functionality, comparing its performance in both local and distributed environments. Additionally, deploying your Spark and H2O cluster on cloud platforms like Azure or GCP will give you a real-world glimpse into scalable machine learning.
What You’ll Learn
By the end of this project, you’ll have a practical understanding of distributed machine learning. You’ll not only develop scalable ML models using H2O and Sparkling Water, but also understand when to transition from local training to a distributed cluster for better efficiency.