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How much Professional Machine Learning Engineer - Google Cost
The cost of the Professional Machine Learning Engineer - Google is $200. For more information related to exam price, please visit the official website Google Website as the cost of exams may be subjected to vary county-wise.
Reference: https://cloud.google.com/certification/guides/machine-learning-engineer
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:
- Assessing data readiness
- Managing incorrect results
- Defining business problems
- Determination of when a model is deemed unsuccessful
- Defining output use
- Identifying nonML solutions
- Key results
- Define ML problem
- Defining the input (features) and predicted output format
- Defining problem type (classification, regression, clustering, etc.)
- Define business success criteria
- Aligning with Google AI principles and practices (e.g. different biases)
- Defining outcome of model predictions
- Identify risks to feasibility and implementation of ML solution. Considerations include:
- Success metrics
- Identifying data sources
- Assessing ML solution readiness
- Assessing and communicating business impact
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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Architecting low-code ML solutions | - AutoML capabilities and implementation - Leveraging pre-built ML models as a service (e.g., Vision AI, Speech-to-Text, Recommendations AI) - Implementing BigQuery ML for basic models |
| Topic 2: Monitoring ML solutions | - Logging and alerting (Cloud Monitoring) - Performance monitoring and drift detection - Model retraining strategies |
| Topic 3: Scaling prototypes into ML models | - Frameworks (TensorFlow, PyTorch, JAX, Scikit-learn) - Training at scale (Distributed training, TPUs) - Hyperparameter tuning |
| Topic 4: Collaborating within and across teams to manage data and models | - Collaboration between Data Scientists, Data Engineers, and ML Engineers - Version control and reproducibility (e.g., DVC, MLOps) - Data management and governance |
| Topic 5: Automating and orchestrating ML pipelines | - CI/CD for ML systems - Triggering and scheduling pipelines - Vertex AI Pipelines (Kubeflow Pipelines) |
| Topic 6: Serving and scaling models | - Batch prediction - Hardware accelerators (GPU/TPU) in serving - Model optimization (Quantization, Distillation) - Online prediction (Vertex AI Prediction) |

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