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Professional-Machine-Learning-Engineer pdf
  • Exam Code: Professional-Machine-Learning-Engineer
  • Exam Name: Google Professional Machine Learning Engineer
  • Q & A: 412 Questions and Answers
  • PDF Price: $59.99
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  • Exam Code: Professional-Machine-Learning-Engineer
  • Exam Name: Google Professional Machine Learning Engineer
  • Q & A: 412 Questions and Answers
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  • Exam Code: Professional-Machine-Learning-Engineer
  • Exam Name: Google Professional Machine Learning Engineer
  • Q & A: 412 Questions and Answers
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The following will be discussed in Google Professional-Machine-Learning-Engineer exam dumps:

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

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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.

The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

  • 87% of Google Cloud certified individuals are more confident about their cloud skills
  • Professional Cloud Architect was the highest paying certification of 2020 and 2019
  • More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work

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

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Career Bonuses

The Google Professional Machine Learning Engineer certification proves that the successful candidates possess sufficient knowledge and skills to design and create scalable solutions for optimal performance. Some of the job roles that these individuals can consider include a Data Engineer, a Senior Data Engineer, a Machine Learning Engineer, a Technical Solutions Engineer, a Software Engineer, and a Cloud Infrastructure Engineer, among others. The median salary that the certificate holders can count on is around $140,000 per annum.

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

SectionWeightObjectives
Topic 1: Collaborate to manage data and models16%- Address data privacy, compliance, and governance
- Manage datasets and features in Vertex AI
- Organize and prepare enterprise data
  • 1. Use Cloud Storage, BigQuery, Spanner, Cloud SQL, and data processing tools
    • 2. Work with structured, unstructured, and semi-structured data
      Topic 2: Scale prototypes into AI models18%- Design and run experiments
      - Select appropriate model architectures and frameworks
      - Optimize model performance and generalization
      - Work with foundation models and generative AI techniques
      Topic 3: Train and deploy models20%- Use Vertex AI deployment features and infrastructure
      - Implement generative AI deployment patterns
      - Deploy models for online, batch, and streaming prediction
      - Configure training jobs and environments
      Topic 4: Automate and orchestrate ML pipelines18%- Automate retraining and model updates
      - Implement CI/CD for ML systems
      - Use Vertex AI Pipelines, TFX, and other orchestration tools
      - Design end-to-end ML workflows
      Topic 5: Architect low-code AI solutions12%- Identify use cases for low-code/no-code AI tools
      - Design solutions using Vertex AI Studio, Model Garden, and Agent Builder
      - Apply responsible AI principles to low-code designs
      Topic 6: Monitor and optimize AI solutions16%- Troubleshoot and maintain production systems
      - Monitor data quality and pipeline health
      - Optimize cost, latency, and resource usage
      - Monitor model performance, fairness, and drift

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