Google Google Cloud Certified Professional-Machine-Learning-Engineer

Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Pipeline Automation & Orchestration

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

Design pipeline. Considerations include:

• Google Cloud serving options
• Organization and tracking experiments and pipeline runs
• Track and audit metadata
• Identification of components, parameters, triggers, and compute needs
• Hybrid or multi-cloud strategies
• Performing data validation
• Storing data and generated artifacts
• Hooking into model and dataset versioning
• Implement serving pipeline
• Implement training pipeline
• Testing for target performance
• Hooking models into existing CI/CD deployment system
• Orchestration framework
• Constructing and testing of parameterized pipeline definition in SDK
• Model binary options
• A/B and canary testing
• Decoupling components with Cloud Build
• Setup of trigger and pipeline schedule
• Tuning compute performance
• Use CI/CD to test and deploy models
• Model/dataset lineage

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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

How to book the Professional Machine Learning Engineer - Google

To apply for the Professional Machine Learning Engineer - Google, You have to follow these steps:

• Step 1: Go to the Google Official Site
• Step 2: Read the instruction carefully
• Step 3: Follow the given steps
• Step 4: Apply for the Professional Machine Learning Engineer Exam

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Problem Framing

The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

• Define ML problem
• Defining problem type (classification, regression, clustering, etc.)
• Defining business problems
• Assessing ML solution readiness
• Determination of when a model is deemed unsuccessful
• Defining the input (features) and predicted output format
• Managing incorrect results
• Defining output use
• Define business success criteria
• Identify risks to feasibility and implementation of ML solution. Considerations include:
• Assessing data readiness
• Key results
• Identifying nonML solutions
• Assessing and communicating business impact
• Aligning with Google AI principles and practices (e.g. different biases)
• Success metrics
• Identifying data sources
• Defining outcome of model predictions