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:

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

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Understanding functional and technical aspects of Professional Machine Learning Engineer - Google ML Model Development

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

• Training a model as a job in different environments
• Choice of framework and model
• Distributed training
• Transfer learning
• Model performance against baselines, simpler models, and across the time dimension
• Hardware accelerators
• Overfitting
• Retraining/redeployment evaluation
• Model generalization
• Model explainability on Cloud AI Platform
• Tracking metrics during training
• Build a model
• Scale model training and serving
• Modeling techniques given interpretability requirements
• Scalable model analysis (e.g. Cloud Storage output files, Dataflow, BigQuery, Google Data Studio)
• Unit tests for model training and serving
• Productionizing

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

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