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GCP-ML: Professional Machine Learning Engineer

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Find out more about GCP-ML: Professional Machine Learning Engineer

The Professional Machine Learning Engineer (GCP-ML) course equips professionals to design, build, and deploy machine learning models on Google Cloud. Participants learn to preprocess data, select algorithms, train and evaluate models, and implement scalable ML pipelines using Vertex AI and TensorFlow. The course also covers model deployment, monitoring, optimization, and responsible AI practices. Through hands-on labs and real-world projects, learners gain practical experience while preparing for the Google Cloud Professional Machine Learning Engineer certification, advancing careers in AI and data science. 

Duration
35 hours
Fees
$399.00
Goal
Driven Approach
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About GCP-ML: Professional Machine Learning Engineer

  • Learn to design, build, and deploy machine learning models effectively.

  • Understand data preprocessing, feature engineering, and data cleaning techniques.

  • Train and evaluate models using TensorFlow and Google Cloud tools.

  • Implement scalable ML pipelines using Vertex AI and Cloud services.

  • Apply supervised, unsupervised, and reinforcement learning algorithms practically.

  • Deploy models with monitoring, logging, and performance optimization strategies.

  • Ensure responsible AI practices, fairness, explainability, and ethical considerations.

  • Gain hands-on experience through labs, projects, and real-world scenarios.

  • Prepare thoroughly for Google Cloud Professional Machine Learning Engineer exam.

  • Advance careers in AI, data science, and cloud engineering roles.

Course Contents

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  • Overview of machine learning concepts and Google Cloud ML services
  • Cloud ML workflow and shared responsibility model
  • Introduction to Vertex AI
  • Data collection, cleaning, and preprocessing
  • Feature selection, transformation, and scaling techniques
  • Handling missing data and categorical features
  • Selecting appropriate ML algorithms (supervised, unsupervised, reinforcement learning)
  • Training, validation, and evaluation strategies
  • Hyperparameter tuning and model optimization
  • Building ML pipelines using Vertex AI Pipelines
  • Automating workflows for training and deployment
  • Integration with BigQuery, Cloud Storage, and Dataflow
  • Deploying models on Vertex AI endpoints
  • Batch prediction and online prediction
  • Monitoring deployed models and performance metrics
  • CI/CD for ML models
  • Model versioning, rollback, and testing
  • Logging, monitoring, and alerting for ML pipelines
  • Explainable AI, fairness, and bias detection
  • Security and compliance for ML models
  • Ethical AI practices and regulatory considerations
  • Practical exercises on data preprocessing, modeling, and deployment
  • Building end-to-end ML solutions on Google Cloud
  • Case studies and real-world problem-solving
  • Exam format and topic coverage
  • Practice questions and mock tests
  • Tips and strategies for certification success

Eligibility of the course

  •  Bachelor’s degree or diploma in Computer Science, IT, or related fields (recommended)
  •  Minimum 1–2 years of experience in machine learning, data science, or cloud technologies
  •  Familiarity with Python programming and ML libraries such as TensorFlow or scikit-learn
  •  Understanding of data preprocessing, feature engineering, and model evaluation techniques
  •  Basic knowledge of Google Cloud Platform services like BigQuery, Cloud Storage, and Vertex AI
  •  Experience with training, deploying, and monitoring ML models is beneficial but not mandatory
  •  Suitable for ML engineers, data scientists, AI specialists, and cloud professionals


Key takeaways

  • Comprehensive Study material
  • Access to Practice tests
  • Access to LMS course content
  • Access to course assignments
  • Industry-relevant case-studies


FAQs

Asked Questions & Answer

 It validates your ability to design, build, and deploy ML models on Google Cloud. 

 Ideal for machine learning engineers, data scientists, AI specialists, and cloud professionals. 

 Basic knowledge of Python, ML concepts, and familiarity with Google Cloud services is recommended. 

 Through live online classes .

 Yes, it includes mock exams, quizzes, and performance feedback to prepare for the certification. 

 Multiple-choice and multiple-select questions, typically 2 hours in duration. 

 Yes, a course completion certificate plus guidance for the official Google Cloud exam. 

 Roles include ML Engineer, AI Specialist, Data Scientist, and Cloud AI Consultant. 

 access to  labs, and updated learning resources. 

 Registration is done via the Google Cloud certification portal with our guidance. 

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