Google Machine Learning Engineer - Professional Training

• How to deal with a machine learning engineering role perfectly?
• How to pass Google Cloud Professional Machine Learning Engineer certification exam?
• How to design, build, and productionalize ML models to solve business challenges?
• How to understand the purpose of the Professional Machine Learning Engineer certification?
• How to use Google Cloud technologies in the organization?

• Recorded Videos After Training
• Digital Learning Material
• Course Completion Certificate
• 24x7 After Training Support

• This Google Machine Learning Engineer- Professional Training is ideal for the IT professionals who are interested in learning the features and functionalities of Google Cloud Security.

• A basic level of familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.

• Multisoft Systems will provide you with a training completion certificate after completing this Google Machine Learning Engineer - Professional Training.

Module 1: Translating business challenges into ML use cases

• Choosing the best solution (ML vs. non-ML, custom vs. pre-packaged 
• Defining how the model output should be used to solve the business problem
• Deciding how incorrect results should be handled
• Identifying data sources 

Module 2: Defining ML problems

• Problem type 
• Outcome of model predictions
• Input (features) and predicted output format

Module 3: Defining business success criteria

• Alignment of ML success metrics to the business problem
• Key results
• Determining when a model is deemed unsuccessful

Module 4: Identifying risks to feasibility of ML solutions

• Assessing and communicating business impact
• Assessing ML solution readiness
• Assessing data readiness and potential limitations
• Aligning with Google's Responsible AI practices 

Module 5: Designing reliable, scalable, and highly available ML solutions

• Choosing appropriate ML services for the use case 
• Component types 
• Exploration/analysis
• Feature engineering
• Logging/management
• Automation
• Orchestration
• Monitoring
• Serving

Module 6: Choosing appropriate Google Cloud hardware components

• Evaluation of compute and accelerator options 

Module 7: Designing architecture that complies with security concerns across sectors/industries

• Building secure ML systems 
• Privacy implications of data usage and/or collection 

Module 8: Exploring data (EDA)

• Visualization
• Statistical fundamentals at scale
• Evaluation of data quality and feasibility
• Establishing data constraints 

Module 9: Building data pipelines

• Organizing and optimizing training datasets
• Data validation
• Handling missing data
• Handling outliers
• Data leakage

Module 10: Creating input features (feature engineering)

• Ensuring consistent data pre-processing between training and serving
• Encoding structured data types
• Feature selection
• Class imbalance
• Feature crosses
• Transformations (TensorFlow Transform)

Module 11: Building models

• Choice of framework and model
• Modeling techniques given interpretability requirements
• Transfer learning
• Data augmentation
• Semi-supervised learning
• Model generalization and strategies to handle overfitting and underfitting

Module 12: Training models

• Ingestion of various file types into training 
• Training a model as a job in different environments
• Hyperparameter tuning
• Tracking metrics during training
• Retraining/redeployment evaluation

Module 13: Testing models

• Unit tests for model training and serving
• Model performance against baselines, simpler models, and across the time dimension
• Model explainability on AI Platform

Module 14: Scaling model training and serving

• Distributed training
• Scaling prediction service 

Module 15: Designing and implementing training pipelines

• Identification of components, parameters, triggers, and compute needs 
• Orchestration framework 
• Hybrid or multicloud strategies
• System design with TFX components/Kubeflow DSL

Module 16: Implementing serving pipelines

• Serving (online, batch, caching)
• Google Cloud serving options
• Testing for target performance
• Configuring trigger and pipeline schedules

Module 17: Tracking and auditing metadata

• Organizing and tracking experiments and pipeline runs
• Hooking into model and dataset versioning
• Model/dataset lineage

Module 18: Monitoring and troubleshooting ML solutions

• Performance and business quality of ML model predictions
• Logging strategies
• Establishing continuous evaluation metrics 
• Understanding Google Cloud permissions model
• Identification of appropriate retraining policy
• Common training and serving errors (TensorFlow)
• ML model failure and resulting biases

Module 19: Tuning performance of ML solutions for training and serving in production

• Optimization and simplification of input pipeline for training
• Simplification techniques