2024, Master the Databricks Machine Learning Professional exam with targeted practice and expert insights. | CertShield
What you will learn
Students pursuing the Databricks Certified Machine Learning Professional certification will gain in-depth knowledge and skills in the following areas:
1. Experimentation with MLflow
Experiment Logging & Tracking: Systematically log hyperparameters, metrics, code, and artifacts for each experiment using MLflow
Advanced Features: Understand how to use features like model signatures, input examples, and MLflow workflows for more comprehensive experiment tracking.
2. Model Lifecycle Management
Model Registry: Learn to manage the lifecycle of models (development, staging, production) seamlessly using the MLflow model registry.
Automation: Set up CI/CD (Continuous Integration/Continuous Delivery) workflows to automate model testing, validation, and deployment.
Streaming for ML: Understand how to integrate Structured Streaming for real-time or near-real-time data pipelines within your machine learning projects.
3. Batch and Real-Time Model Deployment
Inference Strategies: Deploy models using various options Databricks provides for batch predictions, scheduled jobs, or real-time inference.
MLflow Model Serving: Utilize MLflow’s features for model serving, providing REST endpoints for accessing your machine learning models.
4. Solution and Data Monitoring
Detecting Data Drift: Learn how to set up data drift detection mechanisms to alert you when the distribution of your data changes significantly, impacting model
Building Monitoring Strategies: Develop a comprehensive monitoring approach to track the health of your models, data pipelines, and the overall machine learning
English
language