Professional Machine Learning Engineer
- Offered byGoogle Cloud
Professional Machine Learning Engineer at Google Cloud Overview
Duration | 2 hours |
Mode of learning | Online |
Official Website | Go to Website  |
Credential | Certificate |
Professional Machine Learning Engineer at Google Cloud Highlights
- Earn a Certificate after completion of the course
Professional Machine Learning Engineer at Google Cloud Course details
- The Professional Machine Learning Engineer course assesses aspirants ability to Frame ML problems, Develop ML models and Architect ML solutions
- The aspirants will be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation
Professional Machine Learning Engineer at Google Cloud Curriculum
Framing ML problems
Choosing the best solution (ML vs. non-ML, custom vs. pre-packaged [e.g., Auto ML, Vision API]) based on the business requirements
Defining how the model output should be used to solve the business problem
Architecting ML solutions
Choosing appropriate ML services for the use case (e.g., Cloud Build, Kubeflow)
Component types (e.g., data collection, data management)
Designing data preparation and processing systems
Visualization
Statistical fundamentals at scale
Developing ML models
Choice of framework and model
Modeling techniques given interpretability requirements
Automating and orchestrating ML pipelines
Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
Orchestration framework (e.g., Kubeflow Pipelines/Vertex AI Pipelines, Cloud Composer/Apache Airflow)
Monitoring, optimizing, and maintaining ML solutions
Performance and business quality of ML model predictions
Logging strategies
Other courses offered by Google Cloud
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionProfessional Machine Learning Engineer