Machine Learning Practitioner

DATB-MLS : Machine Learning at Scale

Course Duration

2 Hours

Delivery

ILT/VILT

Overview

Overview Course Curriculum Training Schedule Exam & Certification FAQs

In this course, you will gain theoretical and practical knowledge of Apache Spark’s architecture and its application to machine learning workloads within Databricks. You will learn when to use Spark for data preparation, model training, and deployment, while also gaining hands-on experience with Spark ML and pandas APIs on Spark. This course will introduce you to advanced concepts like hyperparameter tuning and scaling Optuna with Spark. This course will use features and concepts introduced in the associate course such as MLflow and Unity Catalog for comprehensive model packaging and governance.

What You'll Learn

Machine Learning Development with Spark
Distributed Model Tuning on Databricks
Deploying Machine Learning Models with Spark
Pandas on Spark

Who Should Attend

This course is designed for professionals who:

Are working as data scientists, machine learning engineers or advanced analytics practitioners looking to scale machine-learning workloads using the Databricks lakehouse platform and Apache Spark.
Are responsible for preparing large-scale datasets, training and deploying models in production, and want to use Spark ML, Pandas API on Spark, and tools such as MLflow and Optuna for hyperparameter tuning at scale.
Have a basic understanding of Python and machine-learning concepts (such as classification, regression, metrics like F1-score) and now wish to extend these skills to distributed ML pipelines and scale-out environments.
Are part of teams tasked with operationalizing ML workflows—i.e., scaling from experimentation to production, enabling repeatable, governed, high-volume model training and inference in the enterprise.
Want to deepen their platform-specific expertise in Databricks ML workflows and integrate Spark-based processing into their machine-learning lifecycle.

Prerequisites

The content was developed for participants with these skills/knowledge/abilities:

A beginner-level understanding of Python.
Basic understanding of DS/ML concepts (e.g. classification and regression models), common model metrics (e.g. F1-score), and Python libraries (e.g. scikit-learn and XGBoost).

Learning Journey

Coming Soon...

1.Machine Learning Development with Spark

A Brief Overview of Spark Architecture for Machine Learning
Introduction to Spark ML for Model Development
Model Tracking and Packaging with MLflow and Unity Catalog on Databricks
Model Development with Spark

2.Distributed Model Tuning on Databricks

Overview of Hyperparameter Tuning
Scalable HPO Frameworks on Databricks
Optuna and Hyperopt with Spark ML
HPO with Ray Tune

3.Deploying Machine Learning Models with Spark

Deployment with Spark
Inference with Spark
Model Deployment with Spark
Optimization Strategies with Spark and Delta Lake
Model Deployment with Spark

4.Pandas on Spark

Scaling with Pandas APIs
Pandas UDFs and Function APIs
Pandas APIs

Frequently Asked Questions (FAQs)

None

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