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Date: Aug 18, 2026 – Aug 19, 2026   Location:
 

Deploying and Scaling Generative AI Applications

Deploy and scale generative AI in production across specialized hardware and complex data flows. GenAI deployment foundations cover the deployment lifecycle, architecture options including serverless...

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$2,000USD
Duration 2 days
Course Code GAI-2401
Available Formats Classroom, Virtual
Next Class: Aug 18, 2026

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Overview

Course Description

Deploy and scale generative AI in production across specialized hardware and complex data flows. GenAI deployment foundations cover the deployment lifecycle, architecture options including serverless and microservices, key challenges of latency, cost, and scalability, model-serving solutions including Hugging Face, TensorFlow Serving, and TorchServe, and MLOps for GenAI. Model packaging, containerization, and deployment strategies cover serializing and exporting models, Docker containerization, optimized image builds, dependency and environment management, cloud platforms including SageMaker, Vertex AI, and Azure ML, on-premise and hybrid deployments, edge deployment, Kubernetes orchestration, and serverless GenAI deployments. Monitoring, scaling, optimization, security, and reliability cover GenAI monitoring fundamentals, horizontal and vertical scaling, load balancing and auto-scaling, inference optimization through quantization, pruning, and distillation, caching, cost optimization, API endpoint security, input validation, error handling and failover, GDPR and CCPA compliance, and security audits. Hands-on labs produce a containerized GenAI model, a Kubernetes deployment, an auto-scaled application, and a basic security audit. The course is designed for DevOps engineers and software developers with practical deployment experience.

Skills Gained

By the end of this course, participants will be able to:

  • Configure efficient deployment strategies that reduce operational cost
  • Apply scaling techniques (horizontal, vertical, auto-scaling) to GenAI applications
  • Establish data privacy compliance through proper deployment configuration
  • Improve reliability and uptime of AI services through redundancy and failover
  • Secure sensitive data and prevent unauthorized access in GenAI deployments

Who Can Benefit

This course is designed for:

  • DevOps
  • Software Developers

Prerequisites

Participants should enter this course with:

  • Practical experience deploying applications
  • GAI-1001 or equivalent

Organizational Objectives

This course assists organizations to:

  • Reduce deployment cost through inference optimization and caching strategies
  • Lower operational risk through monitoring, redundancy, and tested failover patterns
  • Build a shared deployment discipline across DevOps and engineering teams
  • Establish security audit cadence appropriate to AI workloads

Software

All attendees must have a modern web browser and an Internet connection.

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Course Details

Course Details

Introduction to Generative AI Deployment

By the end of this module, you will be able to recognize the deployment lifecycle for generative AI, choose between deployment architectures, identify the key challenges (latency, cost, scalability), compare model-serving solutions, and apply MLOps practices to GenAI.

  • Understanding the Deployment Lifecycle for Generative AI
  • Deployment Architectures for GenAI (Serverless, Microservices, etc.)
  • Key Challenges in Deploying Generative AI Models (Latency, Cost, Scalability)
  • Comparing and Contrasting Model Serving Solutions (Hugging Face, TensorFlow Serving, TorchServe)
  • Introduction to MLOps for Generative AI
  • Hands-on Lab: Evaluate deployment options for a sample GenAI application and stand up a basic MLOps pipeline for a text-generation model.

Model Packaging and Containerization

By the end of this module, you will be able to serialize and export GenAI models, containerize them with Docker, build optimized images for inference, manage dependencies and environments, and package models for specific frameworks and hardware.

  • Serializing and Exporting GenAI Models (ONNX, PMML)
  • Containerization with Docker
  • Building Optimized Docker Images for Generative AI
  • Managing Dependencies and Environments
  • Packaging Models for Specific Frameworks and Hardware
  • Hands-on Lab: Containerize a pre-trained GenAI model and optimize the Docker image for faster inference.

Deployment Strategies and Infrastructure

By the end of this module, you will be able to deploy generative AI to cloud platforms (SageMaker, Vertex AI, Azure ML), choose between on-premise and hybrid options, deploy to edge for low-latency, orchestrate with Kubernetes, and run serverless GenAI deployments.

  • Cloud-Based Deployment (AWS SageMaker, Google Vertex AI, Azure ML)
  • On-Premise and Hybrid Deployments
  • Edge Deployment for Low-Latency Applications
  • Kubernetes for Orchestrating GenAI Deployments
  • Serverless Deployments with AWS Lambda or Azure Functions
  • Hands-on Lab: Deploy a GenAI model to a cloud platform and to Kubernetes, comparing the operational tradeoffs.

Basics of Generative AI Monitoring

By the end of this module, you will be able to distinguish ongoing monitoring from offline evaluation, choose key monitoring metrics for an LLM application in production, instrument an alert and log pipeline, and verify the monitoring system end-to-end.

  • Differences Between Evaluation and Monitoring
  • Identifying Key Monitoring Metrics
  • Understanding the Monitoring Workflow
  • Alerts, Logs, and Monitoring Verification
  • Setting Up a Monitoring System
  • Hands-on Lab: Stand up a monitoring system for one GenAI application with key metrics, alert thresholds, and verification.

Scaling and Optimizing Generative AI Deployments

By the end of this module, you will be able to apply horizontal and vertical scaling to GenAI, configure load balancing and auto-scaling, optimize inference through quantization and pruning, apply caching for latency, and tune cost.

  • Horizontal and Vertical Scaling Strategies
  • Load Balancing and Auto-Scaling for GenAI Applications
  • Optimizing Model Inference for Performance (Quantization, Pruning, Distillation)
  • Caching Strategies for Improved Latency
  • Cost Optimization Techniques for GenAI Deployments
  • Hands-on Lab: Implement auto-scaling for a GenAI application and apply model-optimization techniques to reduce inference latency.

Security and Reliability in Generative AI Deployments

By the end of this module, you will be able to secure GenAI API endpoints, validate and sanitize inputs, implement robust error handling and failover, apply data-privacy compliance controls, and conduct security audits and penetration tests.

  • Securing API Endpoints and Access Control
  • Input Validation and Sanitization to Prevent Attacks
  • Implementing Robust Error Handling and Failover Mechanisms
  • Ensuring Data Privacy and Compliance (GDPR, CCPA)
  • Regular Security Audits and Penetration Testing
  • Hands-on Lab: Implement API authentication and authorization, then conduct a basic security audit of one GenAI deployment.

Schedule

2 options available

FAQ

Does the course schedule include a Lunchbreak?

Classes typically include a 1-hour lunch break around midday. However, the exact break times and duration can vary depending on the specific class. Your instructor will provide detailed information at the start of the course.

What languages are used to deliver training?

Most courses are conducted in English, unless otherwise specified. Some courses will have the word "FRENCH" marked in red beside the scheduled date(s) indicating the language of instruction.

What does GTR stand for?

GTR stands for Guaranteed to Run; if you see a course with this status, it means this event is confirmed to run. View our GTR page to see our full list of Guaranteed to Run courses.

Does Ascendient Learning deliver group training?

Yes, we provide training for groups, individuals and private on sites. View our group training page for more information.

What does vendor-authorized training mean?

As a vendor-authorized training partner, we offer a curriculum that our partners have vetted. We use the same course materials and facilitate the same labs as our vendor-delivered training. These courses are considered the gold standard and, as such, are priced accordingly.

Is the training too basic, or will you go deep into technology?

It depends on your requirements, your role in your company, and your depth of knowledge. The good news about many of our learning paths, you can start from the fundamentals to highly specialized training.

How up-to-date are your courses and support materials?

We continuously work with our vendors to evaluate and refresh course material to reflect the latest training courses and best practices.

Are your instructors seasoned trainers who have deep knowledge of the training topic?

Ascendient Learning instructors have an average of 27 years of practical IT experience and have also served as consultants for an average of 15 years. To stay current, instructors spend at least 25 percent of their time learning new, emerging technologies and courses.

Do you provide hands-on training and exercises in an actual lab environment?

Lab access is dependent on the vendor and the type of training you sign up for. However, many of our top vendors will provide lab access to students to test and practice. The course description will specify lab access.

Will you customize the training for our company’s specific needs and goals?

We will work with you to identify training needs and areas of growth.  We offer a variety of training methods, such as private group training, on-site of your choice, and virtually. We provide courses and certifications that are aligned with your business goals.

How do I get started with certification?

Getting started on a certification pathway depends on your goals and the vendor you choose to get certified in. Many vendors offer entry-level IT certification to advanced IT certification that can boost your career. To get access to certification vouchers and discounts, please contact info@ascendientlearning.com.

Will I get access to content after I complete a course?

You will get access to the PDF of course books and guides, but access to the recording and slides will depend on the vendor and type of training you receive.

How do I request a W9 for Ascendient Learning?

View our filing status and how to request a W9.

Reviews

Instructor knew her stuff. Long time in the industry. Course was easy to follow and very informative.

You get detailed labs to guide you through the technical material giving you a hands on method of learning otherwise difficult material.

This course gave me a clearer understanding of the AWS cloud architecture.

ExitCertified provided great learning material and the instructor was great.

Very good material, the instructor was clear explaining the topics, and the labs were easy to follow it.