Databricks Certified Machine Learning Professional Practice

About this course

Instructor holds Databricks Data Engineer Professional, Databricks Machine Learning Professional, Snowflake advanced certifications, and AWS Solutions Architect Professional.**Exam Domain Weighting**Model Development: 44%  MLOps: 44%  Model Deployment: 12%## 1. Model Development 44%This domain is the core foundation of the certification. It focuses on the ability to design and develop enterprise-scale machine learning solutions, using native Databricks capabilities to build scalable, reusable, and traceable end-to-end ML development workflows. It fully covers the official focus areas: scalable Spark ML pipelines, distributed training and hyperparameter tuning, advanced MLflow capabilities, and automated Feature Store pipelines.### 1.1 Build Distributed and Scalable End-to-End ML Pipelines with Spark MLAdvanced use of core Spark ML components, including Transformer, Estimator, Pipeline, and PipelineModel, for enterprise-level encapsulation and reuse.Development and integration of custom Spark ML Transformers and Estimators to support customized feature processing and model inference requirements.Distributed feature engineering with PySpark and Spark ML for large-scale structured and unstructured data, including distributed encoding, standardization, missing value imputation, feature selection, dimensionality reduction, and distributed sampling for imbalanced datasets.Deep integration between ML pipelines and Delta Lake, enabling ACID guarantees, version management, and incremental reads for feature data, training data, and test data.Pipeline reuse, versioning, and cross-team sharing for standardized enterprise ML development.Integration of Spark ML pipelines with Structured Streaming to support streaming feature processing and standardized incremental model training workflows.

Course objectives

• prepare effectively for the Databricks Certified Machine Learning Professional exam
• understand the key components of MLOps and model deployment
• develop best practices for scalable ML development in enterprise settings