Technologies / Artificial Intelligence

MLOps and AI Model Lifecycle Management on Google Vertex AI

This training focuses on practical aspects of deploying and managing machine learning models in production environments using the Vertex AI platform. The program covers ML pipeline automation, model monitoring, and MLOps best practices in the Google Cloud ecosystem. Sessions are conducted in workshop format, during which participants build complete MLOps systems from development phase to production deployment. The training serves as a natural extension of competencies for those familiar with Kubeflow and introduces the managed Google Cloud environment.

Required Participant Preparation

Solid knowledge of Python language and machine learning libraries
Experience in training and evaluating machine learning models
Basic knowledge of DevOps concepts and CI/CD practices
Understanding of Docker container architecture and orchestration
Knowledge of working with cloud platforms and APIs

Benefits

Gain practical skills in implementing MLOps in Google Cloud environment
Learn to automate machine learning pipelines using Vertex AI Pipelines
Master techniques for effective model management and versioning in production environment
Learn methods for monitoring model quality and responding to data changes
Be able to optimize costs and performance of large-scale ML processes
Understand MLOps integration with DevOps and CI/CD practices
Gain knowledge about managed alternatives to open-source solutions like Kubeflow
Prepare for ML Engineer role in organizations using Google Cloud

Who is this training for?

Machine Learning Engineers (ML Engineers) deploying models to production

Data Scientists interested in model operationalization

DevOps Engineers expanding competencies to AI/ML area

Cloud architects designing scalable machine learning solutions

Automation specialists in data-driven organizations

AI project managers responsible for production deployments

Graduates of "Kubeflow on Google Cloud Platform" training seeking advanced solutions

IT consultants specializing in machine learning platforms

Training program

MLOps definition: goals, principles, key components and benefits
Challenges related to deploying ML models in production environment
Overview of Vertex AI platform as unified MLOps environment
Comparing Vertex AI with open-source Kubeflow: managed vs. self-hosted solutions
Vertex AI architecture and integration with other Google Cloud services
Data Preparation and Feature Engineering with Vertex AI
Vertex AI Managed Datasets for managing training datasets
Vertex AI Feature Store: creating, versioning and sharing features

Integration with BigQuery and Dataflow for large-scale data processing
Automating data preparation pipelines
Training Models at Scale with Vertex AI Training

Types of training jobs: custom jobs, hyperparameter tuning, training pipelines
Using predefined containers and building custom Docker images
Configuring and running distributed training
Vertex AI Experiments and TensorBoard for experiment tracking
Optimizing training costs and resource management

Vertex AI Model Registry: central repository and model versioning
Storing model metadata and quality assessment
Deploying models to Vertex AI Endpoints for online predictions
Implementing batch predictions for large datasets
Deployment strategies: blue/green, canary, shadow deployment
ML Pipeline Automation with Vertex AI Pipelines
Building ML pipelines using Kubeflow Pipelines SDK
Vertex AI SDK for Python for defining pipeline components
Managing artifacts, parameters and dependencies between components
Orchestration, scheduling and monitoring pipeline execution
Vertex ML Metadata for automatic data and model lineage tracking

Vertex AI Model Monitoring: detecting data drift and concept drift
Training-serving skew and prediction drift: identification and mitigation
Configuring alerts and creating monitoring dashboards
Vertex AI Explainable AI for understanding predictions and diagnosing issues
Automatic model retraining in response to data changes

Integrating MLOps pipelines with CI/CD practices (Continuous Integration/Delivery)
Cost management strategies in MLOps projects on Vertex AI
Security aspects: protecting code, data and models in pipelines
Ray on Vertex AI for scaling AI and Python applications
Best practices for organizing MLOps teams and processes

Delivery Methods

Online

Convenience of participating from anywhere
Interactive live sessions with trainer
Materials available for 30 days
No travel costs

On-site

Direct contact with trainer and group
Intensive hands-on workshops
Networking with other participants
Full focus on learning

Training Topics

Frequently asked questions

What are the prerequisites for this training?

The MLOps and AI Model Lifecycle Management on Google Vertex AI training does not require specialized prior knowledge. Basic IT knowledge is sufficient.

What is the format and duration of this training?

The training lasts 3 days and is available in online and on-site format. Sessions run from 9:00 AM to 4:00 PM. We can also customize the schedule to fit your team's needs.

Who is this training designed for?

This training is designed for: Machine Learning Engineers (ML Engineers) deploying models to production; Data Scientists interested in model operationalization; DevOps Engineers expanding competencies to AI/ML area.