Google Machine Learning Engineer - Professional Training

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Course Highlights

  • Instructor-led Online Training
  • Project Based Learning
  • Certified & Experienced Trainers
  • Course Completion Certificate
  • Lifetime e-Learning Access
  • 24x7 After Training Support

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Google Machine Learning Engineer - Professional Training Course Overview

We, Multisoft Systems, are offering an easy solution to the aspirants of the Google Cloud Professional Machine Learning Engineer certification exam. To become a Machine Learning Engineer, you need a basic level of familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.

Pass the Google Cloud Professional Machine Learning Engineer certification exam by earning Google Machine Learning Engineer - Professional Training! Multisoft Systems is offering Google Machine Learning Engineer – Professional Training. In its successful completion, you will also learn the techniques of dealing with a machine learning engineering role perfectly, using Google Cloud technologies in the organization, understanding the purpose of the Professional Machine Learning Engineer certification, and building ML models to solve business challenges.

Our trainers will also help you in aligning with Google's Responsible AI practices, assessing data readiness and potential limitations, and determining when a model is deemed unsuccessful. To become a Machine Learning Engineer, you need a basic level of familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. You will become a master of training, deploying, monitoring, scheduling, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.

Google Machine Learning Engineer Course Objective
  • 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?
Google Machine Learning Engineer Online Training
  • Recorded Videos After Training
  • Digital Learning Material
  • Course Completion Certificate
  • 24x7 After Training Support
Target Audience
  • 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.
Google Machine Learning Engineer Course Prerequisites
  • A basic level of familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance.
Google Machine Learning Engineer Course Certification
  • Multisoft Systems will provide you with a training completion certificate after completing this Google Machine Learning Engineer - Professional Training.

Google Machine Learning Engineer - Professional Training Course Content

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

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Free Google Machine Learning Engineer Training Assessment

This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :

  • User-friendly interface for easy navigation
  • Secure login and authentication measures to protect data
  • Automated scoring and grading to save time
  • Time limits and countdown timers to manage duration.
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Google Machine Learning Engineer Corporate Training

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Certification Training Achievements: Recognizing Professional Expertise

Multisoft Systems is the “one-top learning platform” for everyone. Get trained with certified industry experts and receive a globally-recognized training certificate. Some Multisoft Training Certificate Features :

  • Globally recognized certificate
  • Course ID & Course Name
  • Certificate with Date of Issuance
  • Name and Digital Signature of the Awardee
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Google Machine Learning Engineer - Professional Training FAQ's

Ensure you have 3+ years of hands-on experience with Google Cloud products and solutions

  • Get exam overview
  • Review the sample questions
  • Schedule an exam

  • You should have a bachelor's degree or equivalent practical experience
  • 3 years of software development experience, or 1 year with a relevant advanced degree
  • Experience in applied machine learning or artificial intelligence

Yes, at Multisoft, you will get the opportunity to attend classes on weekdays and weekends for this Google Machine Learning Engineer Training.

  • Fast-Track Training
  • Own Schedule Training
  • One-On-One Training
  • Project Based Training 
  • Corporate Training

Yes, we provide recorded videos along with lifetime e-learning access to all our learners. Also, you will get a globally accepted course completion certificate after you have successfully completed this Google Machine Learning Engineer. 

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