Google Cloud Machine Learning Engineer Course

Google Cloud Machine Learning Engineer course is formulated in such a way that it enables learners to acquire the advanced skills that they can use to create, train, optimize, and deploy machine learning models on Google Cloud. The basic services included in this comprehensive program are Vertex AI, BigQuery ML, AutoML, and TensorFlow, which allow participants to build scalable machine learning pipelines and build them into the real world. The Leaners will have practical experience in working with data, engineering features, model testing, and practices of MLOps so that they could effectively handle end-to-end ML processes.

Other key concepts in clouds such as distributed training, hyperparameter tuning, model monitoring, and responsible AI implementation are also covered in the course. With hands-on labs and case studies, participants gain the knowledge and skills necessary to apply enterprise-level ML solutions as they acquire the best practices suggested by Google Cloud.

Upon the completion of this training, the learners will be in a good position to work on the Google Cloud Machine Learning Engineer position and hence be able to participate in the decision-making process of any organization that is data-driven. This program is offered by SSDN Technologies which has been known to be the best Training Company to offer industry-relevant, instructor-led learning programs that enable professionals to upgrade their careers with confidence. 
 


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Learning Options for You

  • Live Training (Duration : 80 Hours)
  • Per Participant

Fee: On Request

Course Prerequisites

  • Strong understanding of Python and ML fundamentals
  • Familiarity with data preprocessing, model training, and evaluation
  • Basic knowledge of cloud computing
  • Optional: Experience with TensorFlow, PyTorch, or Scikit-learn
  • Optional: Prior exposure to GCP services such as BigQuery or Compute Engine

Learning Objectives

This course provides comprehensive training to become a Google Cloud Machine Learning Engineer. Learners explore Vertex AI for end-to-end model development, including data ingestion, feature engineering using Vertex AI Feature Store, model training, hyperparameter tuning, deployment, and continuous monitoring. The program emphasizes MLOps best practices, automated pipelines with Vertex AI Pipelines, metadata tracking, versioning, and responsible AI principles. Participants gain hands-on experience building scalable, secure, and efficient ML systems while preparing for the Google Cloud ML Engineer certification.

Target Audience

  • Aspiring and professional Machine Learning Engineers
  • Data Scientists and Data Engineers working with ML pipelines
  • AI/ML developers scaling models on cloud environments
  • Cloud engineers preparing for Google’s ML Engineer certification
  • Professionals automating ML workflows with MLOps
  • Organizations deploying production-grade ML systems on GCP

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