Databricks ML Pro Certification: Exam Success Practice Tests

Databricks ML Pro Certification: Exam Success Practice Tests, available at $44.99, has an average rating of 3.5, 2 quizzes, based on 1 reviews, and has 802 subscribers.

You will learn about 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 This course is ideal for individuals who are Data Scientists/ML Engineers: The most likely audience. Assumes a foundation in ML and wants to specialize in Databricks-based tools and processes. or Data Engineers and Analysts: If the course emphasizes data preparation and integration with ML pipelines within Databricks, it would be valuable for those working with data. or IT Professionals: If the course provides a high-level overview of Databricks ML concepts for project planning and management, even those with less hands-on ML experience could benefit. or Databricks Users: For those already using Databricks, the course could be a focused way to enhance their knowledge of its machine learning-specific features. It is particularly useful for Data Scientists/ML Engineers: The most likely audience. Assumes a foundation in ML and wants to specialize in Databricks-based tools and processes. or Data Engineers and Analysts: If the course emphasizes data preparation and integration with ML pipelines within Databricks, it would be valuable for those working with data. or IT Professionals: If the course provides a high-level overview of Databricks ML concepts for project planning and management, even those with less hands-on ML experience could benefit. or Databricks Users: For those already using Databricks, the course could be a focused way to enhance their knowledge of its machine learning-specific features.

Enroll now: Databricks ML Pro Certification: Exam Success Practice Tests

Summary

Title: Databricks ML Pro Certification: Exam Success Practice Tests

Price: $44.99

Average Rating: 3.5

Number of Quizzes: 2

Number of Published Quizzes: 2

Number of Curriculum Items: 2

Number of Published Curriculum Objects: 2

Number of Practice Tests: 2

Number of Published Practice Tests: 2

Original Price: ₹7,900

Quality Status: approved

Status: Live

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

Who Should Attend

• Data Scientists/ML Engineers: The most likely audience. Assumes a foundation in ML and wants to specialize in Databricks-based tools and processes.
• Data Engineers and Analysts: If the course emphasizes data preparation and integration with ML pipelines within Databricks, it would be valuable for those working with data.
• IT Professionals: If the course provides a high-level overview of Databricks ML concepts for project planning and management, even those with less hands-on ML experience could benefit.
• Databricks Users: For those already using Databricks, the course could be a focused way to enhance their knowledge of its machine learning-specific features.

Target Audiences

• Data Scientists/ML Engineers: The most likely audience. Assumes a foundation in ML and wants to specialize in Databricks-based tools and processes.
• Data Engineers and Analysts: If the course emphasizes data preparation and integration with ML pipelines within Databricks, it would be valuable for those working with data.
• IT Professionals: If the course provides a high-level overview of Databricks ML concepts for project planning and management, even those with less hands-on ML experience could benefit.
• Databricks Users: For those already using Databricks, the course could be a focused way to enhance their knowledge of its machine learning-specific features.