Google Cloud Professional Machine Learning Engineer Certification Logo

Google Cloud Professional Machine Learning Engineer Certification

Powered By

Google Cloud Platform Logo

The Google Cloud Professional Machine Learning Engineer certification validates your ability to design, build, and productionize machine learning (ML) models using Google Cloud technologies. This certification emphasizes practical application, scalability, and responsible AI practices.

ATP_Authorized Logo

Powered By

Google Cloud Platform Logo
COURSE BROCHURE DOWNLOAD PDF
168

Buy Certification

Looking for a private batch ?

REQUEST A CALLBACK

Need help finding the right training?

Your Message



What is Google Cloud Professional Machine Learning Engineer Certification Training about?

This certification validates your ability to design, build, and productionize machine learning models on Google Cloud. It covers ML problem framing, data preparation, feature engineering, model training, tuning, deployment, and monitoring using Google Cloud’s AI and ML tools. The role demands solid ML knowledge plus cloud architecture skills to deliver scalable, reliable ML solutions that meet business needs.

Exam Format

Exam Code: Professional Machine Learning Engineer

Duration: 2 hours

Question Types:

  • Multiple-choice and multiple-select questions
  • Scenario-based and case study questions

Passing Score:  70%

What are the objectives of Google Cloud Professional Machine Learning Engineer Certification Training ?

  • Design and Build ML Models: Create machine learning models using Google Cloud technologies, ensuring they are scalable and performant.
  • Automate and Orchestrate ML Pipelines: Implement end-to-end ML pipelines using tools like Vertex AI, TensorFlow, and Kubeflow.
  • Serve and Scale ML Models: Deploy ML models to production environments, ensuring they can handle varying loads and are accessible for inference.
  • Monitor and Optimize ML Solutions: Continuously monitor model performance and implement strategies to improve accuracy and efficiency.
  • Implement Responsible AI Practices: Ensure that AI solutions are ethical, transparent, and comply with relevant regulations.

Who is Google Cloud Professional Machine Learning Engineer Certification Training for?

  • ML Engineers and Data Scientists experienced with ML concepts
  • Cloud professionals looking to specialize in ML solutions on Google Cloud
  • Developers transitioning to ML engineering roles

What are the prerequisites for Google Cloud Professional Machine Learning Engineer Certification Training?

  • 3+ years of industry experience, including 1 or more years designing and managing solutions using Google Cloud
  • Proficiency in Python and SQL
  • Familiarity with machine learning frameworks like TensorFlow and scikit-learn
  • Experience with data processing tools such as BigQuery and Dataflow
  • Understanding of MLOps principles
  • Prepare for the exam by completing this course: Professional Cloud Machine Learning Engineer


Course Logo

Google Cloud Professional Machine Learning Engineer Certification Training - Certification & Exam

The certification course for GCP Professional Machine Learning Engineer covers essential topics to ensure comprehensive understanding and proficiency:

Section 1: Architecting low-code AI solutions 

  • Developing ML models by using BigQuery ML
  • Building AI solutions by using ML APIs or foundation models
  • Training models by using AutoML

Section 2: Collaborating within and across teams to manage data and models 

  • Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Hadoop)
  • Model prototyping using Jupyter notebooks
  • Tracking and running ML experiments

Section 3: Scaling prototypes into ML models 

  • Building models
  • Training models
  • Choosing appropriate hardware for training

Section 4: Serving and scaling models 

  • Serving models
  • Scaling online model serving

Section 5: Automating and orchestrating ML pipelines 

  • Developing end-to-end ML pipelines
  • Automating model retraining
  • Tracking and auditing metadata

Section 6: Monitoring AI solutions 

  • Identifying risks to AI solutions
  • Monitoring, testing, and troubleshooting AI solutions


Unique Benefits of Certification

  • Validation of Expertise: Recognizes advanced proficiency in cloud security on Google Cloud Platform.
  • Career Advancement: Enhances job prospects and opens opportunities for roles in cloud security engineering.
  • Industry Recognition: Enhances professional credibility and marketability within the cloud computing industry.
  • Access to GCP Resources: Exclusive access to Google Cloud tools, resources, and communities.