Udemy – Google Certified Professional Machine Learning Engineer 2023-6

Udemy – Google Certified Professional Machine Learning Engineer 2023-6 Downloadly IRSpace

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Apr 17, 2025 - 13:24 Updated: Apr 21, 2025 - 06:18

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Udemy – Google Certified Professional Machine Learning Engineer 2023-6

Google Certified Professional Machine Learning Engineer course:

Solve business challenges into ML use cases
Choose the optimal solution (ML vs. non-ML, custom vs. prepackaged)
Define how the output of the model should solve the business problem
Identify data sources (available vs. ideal)
Definition of ML problems (type of problem, result of predictions, input and output formats)
Define business success metrics (ML metrics alignment, key results)
Identify risks of ML solutions (business impact assessment, ML solution readiness, data readiness)
Design reliable, scalable and accessible ML solutions
Select the appropriate ML services and components
Designing data discovery/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and servicing strategies
Evaluate Google Cloud hardware options (CPU, GPU, TPU, edge devices)
Design architectures that meet security concerns across departments
Data exploration (visualization, statistical foundations, data quality, data limitations)
Creating data pipelines (organizing and optimizing data sets, handling missing and outlier data, preventing data leakage)
Creating input features (ensuring consistency of data preprocessing, encoding structured data, managing feature selection, handling class imbalances, using transformations)
Building models (framework selection, interpretability, transfer learning, data augmentation, semi-supervised learning, overfitting/overfitting management)
Train models (receiving types of files, managing educational environments, setting meta-parameters, tracking educational criteria)
Testing models (perform unit tests, compare model performance, use Vertex AI for model explainability)
Scale model training and servicing (training distribution, scale prediction service)
Designing and implementing training pipelines (identifying components, managing orchestration frameworks, designing hybrid or multi-cloud strategies, using TFX components)
Implementation of servicing pipelines (management of servicing options, target performance testing, scheduling configuration)
Metadata tracking and auditing (organizing and tracking experiments, managing model/dataset versioning, understanding model/dataset ancestry)
Monitoring and troubleshooting ML solutions (performance measurement, enrollment strategies, establishing continuous evaluation criteria)
Performance tuning for training and service in production (input pipeline optimization, use of simplification techniques)

What you will learn in the Google Certified Professional Machine Learning Engineer course

Framing ML problems
ML architecture solutions
Design of data preparation and processing systems
Development of ML models
Automation and synchronization of ML pipelines
Monitor, optimize and maintain ML solutions

This course is suitable for people who

Anyone who wants to get Google Cloud Machine Learning Professional Engineer

Google Certified Professional Machine Learning Engineer course specifications

Publisher: Udemy
Lecturer: Deepak Dubey
Training level: beginner to advanced
Training duration: 12 hours and 16 minutes
Number of courses: 48

Google Certified Professional Machine Learning Engineer course topics on 7/2023

Google Certified Professional Machine Learning Engineer

Prerequisites for the Google Certified Professional Machine Learning Engineer course

Some prior experience with Google Cloud and Machine Learning will help. Also if you are already certified with Google Professional Data Engineer that will help you greatly.