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This is the code repository for Practical Machine Learning on Databricks, published by Packt.
Seamlessly transition ML models and MLOps on Databricks
Unleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform.
This book covers the following exciting features:
- Transition smoothly from DIY setups to databricks
- Master AutoML for quick ML experiment setup
- Automate model retraining and deployment
- Leverage databricks feature store for data prep
- Use MLflow for effective experiment tracking
- Gain practical insights for scalable ML solutions
- Find out how to handle model drifts in production environments
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
iris = load_iris()
X = iris.data # Features
y = iris.target # Labels
Following is what you need for this book: This book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.
With the following software and hardware list you can run all code files present in the book (Chapter 1-10).
| Chapter | Software required | OS required |
|---|---|---|
| 1-10 | Databricks Runtime | Windows and Mac OS |
| 1-10 | Python proficiency (3.x) | Windows and Mac OS |
| 1-10 | Statistics and ML basics | Windows and Mac OS |
| 1-10 | Spark knowledge (3.0 or above) | Windows and Mac OS |
| 1-10 | Delta Lake features (optional) | Windows and Mac OS |
Debu Sinha is an experienced data science and engineering leader with deep expertise in software engineering and solutions architecture. With over 10 years in the industry, Debu has a proven track record in designing scalable software applications and big data, and machine learning systems. As lead ML specialist on the Specialist Solutions Architect team at Databricks, Debu focuses on AI/ML use cases in the cloud and serves as an expert on LLMs, ML, and MLOps. With prior experience as a start-up co-founder, Debu has demonstrated skills in team-building, scaling, and delivering impactful software solutions. An established thought leader, Debu has received multiple awards and regularly speaks at industry events.