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DataCamp

Professional Machine Learning Engineer with Google Cloud

via DataCamp Path

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Overview

A Machine Learning Engineer designs, builds, and productionizes ML systems to solve business challenges. This certification learning path provides the advanced knowledge and practical skills required for this role, preparing you to successfully operate and maintain ML systems on Google Cloud. Through a curated collection of on-demand courses, labs, and skill badges, you will gain real-world, applied experience with Google Cloud technologies. This path focuses on the essential skills for the ML Engineer role, from designing and building ML systems to optimizing and maintaining them in production. Upon completion, you will be equipped with the skills validated by the Professional Machine Learning Engineer certification. Take the next step in your professional journey and demonstrate your expertise by preparing for the Google Cloud Professional Machine Learning Engineer exam.

Syllabus

  • Build a Certification Study Guide: ACE Exam Prep
    • Learn how to use NotebookLM to create a personalized study guide for the Professional Machine Learning Engineer certification exam (PMLE).
  • Introduction to AI and Machine Learning on Google Cloud
    • This course introduces Google Cloud's AI and machine learning (ML) capabilities, with a focus on developing both generative and predictive AI projects.
  • Production Machine Learning Systems
    • Learn how to implement the various flavors of ML: static, dynamic, and continuous training; static and dynamic inference; and batch and online processing.
  • Machine Learning Operations (MLOps): Getting Started
    • Learn MLOps tools and best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
  • Machine Learning Operations (MLOps) with Vertex AI: Manage Features
    • Learn best practices for deploying, evaluating, monitoring and operating production ML systems on Google Cloud.
  • Machine Learning Operations (MLOps) for Generative AI
    • Uncover the unique challenges faced by MLOps teams when deploying and managing Generative AI models, and explore how Vertex AI empowers AI teams.
  • Machine Learning Operations (MLOps) with Vertex AI: Model Evaluation
    • Gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks.
  • Create Generative AI Apps on Google Cloud
    • Learn about Gen AI applications and how you can use prompt design and retrieval augmented generation (RAG) to build powerful applications using LLMs.
  • Responsible AI for Developers: Fairness & Bias
    • This course covers techniques to practically identify fairness and bias and mitigate bias in AI/ML practices.
  • Responsible AI for Developers: Interpretability & Transparency
    • It discusses the importance of AI transparency for developers and engineers.
  • Responsible AI for Developers: Privacy & Safety
    • It explores practical methods and tools to implement AI privacy and safety recommended practices.

Taught by

Google Cloud

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