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The benefit of obtaining the Professional Machine Learning Engineer - Google Certification

  • Professional Cloud Architect was the highest paying certification of 2020 and 2019
  • More than 1 in 4 of Google Cloud certified individuals took on more responsibility or leadership roles at work
  • 87% of Google Cloud certified individuals are more confident about their cloud skills

The Google Professional Machine Learning Engineer certification is developed to validate the ability of the specialists to design, build, and productionize the Machine Learning models to solve business challenges with the help of Google Cloud technologies as well as their knowledge of the proven Machine Learning models & techniques. Specifically, this certificate equips the candidates with an understanding of all the aspects related to data pipeline interaction, model architecture, as well as metrics interpretation. It also provides the target individuals with the comprehension of the basic concepts of application development, data engineering, infrastructure management, and data governance. To get certified, the individuals need to take one qualifying exam.

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Google Professional-Machine-Learning-Engineer Exam Syllabus Topics:

TopicDetails
Topic 1
  • Architecting low-code AI solutions: This section of the exam measures the skills of Google Machine Learning Engineers and covers developing machine learning models using BigQuery ML. It includes selecting appropriate models for business problems, such as linear and binary classification, regression, time series, matrix factorization, boosted trees, and autoencoders. Additionally, it involves feature engineering or selection and generating predictions using BigQuery ML.
Topic 2
  • Automating and orchestrating ML pipelines: This topic focuses on developing end-to-end ML pipelines, automation of model retraining, and lastly tracking and auditing metadata.
Topic 3
  • Serving and scaling models: This section deals with Batch and online inference, using frameworks such as XGBoost, and managing features using VertexAI.
Topic 4
  • Scaling prototypes into ML models: This topic covers building and training models. It also focuses on opting for suitable hardware for training.

Reference: https://cloud.google.com/certification/guides/machine-learning-engineer

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Topics of Professional Machine Learning Engineer - Google

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  • ML Pipeline Automation & Orchestration
  • Data Preparation and Processing
  • ML Solution Monitoring, Optimization, and Maintenance
  • ML Model Development
  • ML Solution Architecture
  • ML Problem Framing

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