Professional Machine Learning Engineer Training Logo

Professional Machine Learning Engineer Training

Live Online & Classroom Enterprise Training

Designs, builds, and operationalizes ML solutions on Google Cloud—using Vertex AI for model development/deployment, BigQuery for feature/analytics, and Dataflow/Dataproc for pipelines. Focuses on end-to-end MLOps (CI/CD, monitoring, governance), scalable serving, and responsible AI to turn data into production-grade, business-ready models.

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What is Professional Machine Learning Engineer Training about?

This learning path is designed to equip learners with the skills necessary to design, build, deploy, and maintain machine learning (ML) systems on Google Cloud. It covers the entire ML lifecycle, including data preparation, model development, deployment, monitoring, and optimization, with a focus on MLOps practices using Vertex AI.

What are the objectives of Professional Machine Learning Engineer Training ?

  • Understand the end-to-end machine learning lifecycle on Google Cloud.
  • Gain proficiency in using Vertex AI for model development and deployment.
  • Implement MLOps practices to manage and monitor ML models in production.
  • Develop skills in data preparation and feature engineering for ML applications.
  • Prepare for the Professional Machine Learning Engineer certification exam

Who is Professional Machine Learning Engineer Training for?

  • Aspiring machine learning engineers seeking to build expertise in cloud-based ML solutions.
  • Data scientists and analysts looking to transition into ML engineering roles.
  • Professionals aiming to enhance their skills in deploying and managing ML models at scale.
  • Individuals preparing for the Google Cloud Professional Machine Learning Engineer certification.
  • Teams responsible for implementing and maintaining ML systems within their organizations.

What are the prerequisites for Professional Machine Learning Engineer Training?

  • Basic understanding of machine learning concepts and algorithms.
  • Proficiency in programming languages such as Python.
  • Familiarity with SQL and data manipulation techniques.
  • Experience with cloud computing platforms, preferably Google Cloud.
  • Knowledge of version control systems like Git.

Available Training Modes

Live Online Training

10 Days

Self-Paced Training

100 Hours

Course Outline Expand All

Expand All

  • Overview of PMLE exam domains
  • Identifying knowledge gaps
  • Creating a personalized study plan
  • Recommended resources and practice materials
  • Introduction to Google Cloud Console
  • Navigating projects, resources, and IAM roles
  • Managing permissions and APIs
  • Hands-on exercises with basic Google Cloud features
  • Overview of AI/ML offerings on Google Cloud
  • Building predictive and generative AI projects
  • Technologies, products, and tools in the data-to-AI lifecycle
  • Hands-on exercises with AI/ML tools
  • Cleaning data with Dataprep by Trifacta
  • Running data pipelines in Dataflow
  • Creating clusters and running Apache Spark jobs in Dataproc
  • Preparing data for ML APIs
  • Introduction to Vertex AI Notebooks
  • Using Jupyter notebooks for ML workflows
  • Data preparation, model training, and evaluation
  • Deploying models using Vertex AI
  • Creating and evaluating ML models with BigQuery ML
  • Making data predictions using SQL
  • Integrating BigQuery ML with other Google Cloud services
  • Hands-on exercises with BigQuery ML
  • Building data transformation pipelines to BigQuery
  • Using Dataprep by Trifacta for data preparation
  • Leveraging Cloud Storage and Dataflow for ETL processes
  • Engineering data for predictive modeling
  • Importance of feature engineering in ML models
  • Using Vertex AI Feature Store for feature management
  • Techniques for improving model accuracy
  • Hands-on labs on feature engineering
  • Building ML models with TensorFlow and Keras
  • Improving model accuracy through tuning
  • Writing ML models for scaled use
  • Deploying models on Google Cloud
  • Implementing static, dynamic, and continuous training
  • Handling static and dynamic inference
  • Managing batch and online processing
  • TensorFlow abstraction levels and distributed training
  • Introduction to MLOps tools and best practices
  • Deploying, evaluating, and monitoring ML systems
  • Automation of ML systems in production
  • Managing ML workflows on Google Cloud
  • Managing features in Vertex AI
  • Versioning and monitoring features
  • Best practices for feature management
  • Hands-on exercises with Vertex AI Feature Store
  • Understanding generative AI concepts
  • Use cases and applications of generative AI
  • Tools and frameworks for developing generative AI models
  • Hands-on labs on generative AI
  • Overview of large language models (LLMs)
  • Use cases and applications of LLMs
  • Prompt tuning to enhance LLM performance
  • Tools for developing LLMs on Google Cloud
  • Challenges in deploying and managing generative AI models
  • MLOps practices for generative AI
  • Using Vertex AI for generative AI workflows
  • Best practices for managing generative AI models
  • Evaluating generative and predictive AI models
  • Techniques for model evaluation
  • Ensuring reliable, accurate, and high-performing results
  • Tools and best practices for model evaluation
  • Using Vertex AI platform for model training and deployment
  • AutoML and custom training services
  • Model evaluation, tuning, and deployment
  • Hands-on labs on building and deploying ML solutions
  • Building generative AI applications on Google Cloud
  • Integrating LLMs into applications
  • Enhancing user experiences with generative AI
  • Tools and frameworks for developing generative AI apps
  • Concepts of responsible AI and AI principles
  • Identifying fairness and bias in AI models
  • Techniques to mitigate bias in AI/ML practices
  • Implementing responsible AI practices using Google Cloud tools
  • Importance of AI interpretability and transparency
  • Methods for achieving interpretability in AI models
  • Tools for enhancing transparency in AI systems
  • Best practices for interpretable AI
  • AI privacy and safety considerations
  • Implementing privacy-preserving techniques in AI models
  • Tools and practices for ensuring AI safety
  • Responsible AI development on Google Cloud
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Who is the instructor for this training?

The trainer for this Professional Machine Learning Engineer Training has extensive experience in this domain, including years of experience training & mentoring professionals.

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Professional Machine Learning Engineer Training - Certification & Exam

After completing this course, you can take the below certifications: Google Cloud Professional Machine Learning Certification

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