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2024, Master the Databricks Machine Learning Professional exam with targeted practice and expert insights. | CertShield

What you will learn

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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