Course Overview of Vertex AI for Machine Learning Practitioners

This practical, hands-on course is designed for engineers and data scientists familiar with ML models who want to become proficient in using Vertex AI for custom model workflows.

After completing Vertex AI for Machine Learning Practitioners, students will be able to

  • Understand key Vertex AI components and how they support ML workflows.
  • Configure and launch Custom Training and Hyperparameter Tuning Jobs.
  • Organize and version models using the Vertex AI Model Registry.
  • Deploy models for online predictions with Vertex AI Endpoints.
  • Orchestrate end-to-end ML workflows using Vertex AI Pipelines.
  • Set up monitoring for deployed models.

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Key Features of Vertex AI for Machine Learning Practitioners

  • Expert-Led Practical Training- One-day instructor-led session designed specifically for ML practitioners. 

  • Heavy Hands-On Focus- Includes 3 lectures supported by 5 comprehensive hands-on labs. 

  • Full ML Lifecycle Coverage- Covers everything from containerized training and tuning to model versioning, deployment, and monitoring. 

  • Workflow Orchestration- Learn to use Kubeflow and Vertex AI Pipelines for increased efficiency and scalability. 

  • Production Monitoring- Specialized focus on understanding feature drift, skew, and monitoring models in production. 

Who should Attend Vertex AI for Machine Learning Practitioners?

  • Machine Learning Engineers and Data Scientists.

Prerequisites of Vertex AI for Machine Learning Practitioners

  • Experience building and training custom ML models.
  • Familiarity with Docker.

    • Why choose CloudThat as your training partner for Vertex AI for Machine Learning Practitioners

    • Specialized GCP Focus- CloudThat specializes in cloud technologies, offering focused and specialized training programs. We are Authorized Trainers for the Google Cloud Platform. This specialization ensures in-depth coverage of GCP services, use cases, best practices, and hands-on experience tailored specifically for GCP. 
    • Industry-Recognized Trainers- CloudThat has a strong pool of industry-recognized trainers certified by GCP. These trainers bring real-world experience and practical insights into the training sessions, comprehensively understanding how GCP is applied in different industries and scenarios. 
    • Hands-On Learning Approach- CloudThat emphasizes a hands-on learning approach. Learners can access practical labs, real-world projects, and case studies that simulate actual GCP environments. This approach allows learners to apply theoretical knowledge in practical scenarios, enhancing their understanding and skill set. 
    • Customized Learning Paths- CloudThat understands that learners have different levels of expertise and varied learning objectives. We offer customized learning paths, catering to beginners, intermediate learners, and professionals seeking advanced GCP skills. 
    • Interactive Learning Experience- CloudThat's training programs are designed to be interactive and engaging. We utilize various teaching methodologies like live sessions, group discussions, quizzes, and mentorship to keep learners engaged and motivated throughout the course. 
    • Placement Assistance and Career Support- CloudThat often provides placement assistance and career support services. This includes resume building, interview preparation, and connecting learners with job opportunities through our network of industry partners and companies looking for GCP-certified professionals. 
    • Continuous Learning and Updates-  CloudThat ensures that our course content is regularly updated to reflect the latest trends, updates, and best practices within the GCP ecosystem. This commitment to keeping the content current enables learners to stay ahead in their GCP knowledge. 
    • Positive Reviews and Testimonials- Reviews and testimonials from past learners can strongly indicate the quality of training provided. You can Check feedback and reviews about our GCP courses that can provide potential learners with insights into the effectiveness and value of the training. 

    Learning Objectives of Vertex AI for Machine Learning Practitioners

    • Understand Vertex AI Components- Gain a comprehensive understanding of the key components within Vertex AI and how they collaborate to support custom ML workflows.  
    • Optimize Model Performance- Learn to configure and launch Vertex AI Custom Training and Hyperparameter Tuning Jobs to achieve optimal model results.  
    • Manage Model Versions- Utilize the Vertex AI Model Registry to organize and version models for simplified tracking and access.  
    • Deploy for Predictions- Configure serving clusters and deploy models to Vertex AI Endpoints to facilitate online predictions.  
    • Orchestrate Workflows- Operationalize end-to-end ML workflows using Vertex AI Pipelines to improve scalability and efficiency.  
    • Implement Monitoring: Configure and set up proactive monitoring for models that have been deployed to production.  
    • Master Specific Tools: Develop an understanding of containerized training applications, Kubeflow, and pre-built vs. lightweight Python components.  
    • Detect Model Issues: Understand and identify technical issues such as feature drift and skew in deployed models.  

    Course Outline for Vertex AI for Machine Learning Practitioners Download Course Outline

    Lecture Content

    • Packaging machine learning source code into standard OCI Containerized Training Applications
    • Managing training execution resources using Vertex AI Custom Training jobs
    • Optimizing model parameters programmatically with Hyperparameter Tuning Jobs
    • Tracking, organizing, and versioning production-ready assets within the Vertex AI Model Registry
    • Architecting real-time low-latency serving infrastructure via Online Deployment to Vertex AI Endpoints

    Learning Objectives

    • Design and build custom Docker containers for isolated, reproducible machine learning training scripts
    • Configure automated hyperparameter trials to find optimal model configurations at scale
    • Version, manage, and transition machine learning models across production lifecycles in a central registry
    • Provision scalable, high-performance web endpoints to serve real-time model predictions securely

    Lab Content

    • Containerizing and Executing Custom Model Training Jobs on Vertex AI
    • Hyperparameter Tuning and Deploying Models to Vertex AI Endpoints

    Lecture Content

    • Core orchestration principles and architectural layout of Kubeflow Pipelines (KFP)
    • Building modular, reusable workflows using Google Cloud pre-built components vs. custom lightweight Python components
    • Abstracting data steps: Data ingestion, data transformation, model training, and evaluation nodes
    • Compiling pipeline syntax, tracking artifact metadata, and executing managed pipelines on the Vertex AI platform

    Learning Objectives

    • Understand how Kubeflow pipelines decouple and automate sequential steps in a machine learning lifecycle
    • Formulate production-grade pipelines by wiring together lightweight Python-defined tasks and pre-built Google Cloud components
    • Compile, schedule, and execute automated machine learning workflows within the managed Vertex AI Pipelines environment

    Lab Content

    • Building and Compiling Machine Learning Workflows with Kubeflow and Vertex AI Pipelines

    Lecture Content

    • Production operational vulnerabilities: Understanding Data Skew (training-to-serving delta) and Feature Drift (time-series baseline shifts)
    • Configuring statistical measurement thresholds to catch anomalous data pattern mutations
    • Setting up Vertex AI Model Monitoring parameters specifically for models deployed to active Vertex AI Endpoints
    • Automating telemetry feedback logs, performance tracking tables, and real-time system alerts

    Learning Objectives

    • Explain the mathematical and conceptual differences between feature drift and training-serving skew
    • Implement automated real-time monitoring jobs on active prediction endpoints to identify drop-offs in model reliability
    • Configure alert metrics to notify engineering teams when production server feature values violate baseline constraints

    Lab Content

    • Configuring Feature Drift, Data Skew Detection, and Live Model Monitoring on Vertex AI Endpoints

    Certification Details of Vertex AI for Machine Learning Practitioners

      CloudThat Course Completion Certificate will be awarded to all learners who complete the training.

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    Course ID: 28969

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    FAQs for Vertex AI for Machine Learning Practitioners

    This is for engineers and data scientists already familiar with machine learning who want to specialize in Google Cloud's Vertex AI. 

    This course specifically targets "custom model workflows" rather than entry-level automated tools.

    You will use Vertex AI Custom Training, Model Registry, Endpoints, and Pipelines. 

    Yes, the course is highly practical with 5 hands-on labs. 

    Enquire Now