Integration and Deployment of GenAI Models

Name: Integration and Deployment of GenAI Models
Rating: 4.1 (39 reviews)

Master API Integration, Docker Containerization, Kubernetes & Cloud Deployment for Production-Ready GenAI Applications

Created bySkool of AI, Meta Brains

Last updated 5/2025

English

What you'll learn

Deploy generative AI models in real-world applications with ease and efficiency.
Integrate APIs to enhance AI capability within custom applications.
Develop interactive applications using frameworks like Streamlit.
Containerize AI solutions using Docker for seamless deployment.
Master Kubernetes to scale and manage AI-based applications effectively.
Optimize generative AI workflows for performance and accuracy.
Build production-ready AI systems with robust development tools.
Understand advanced AI deployment strategies and best practices.

Course content

9 sections • 41 lectures • 2h 56m total length

Overview of Generative AI and Its Applications3:54
Types of Generative Models5:05
Challenges in Deploying Generative AI Systems5:15

GenAI Integration Options4:32
GenAI Deployment Options6:01
Evaluate GenAI deployment options across cloud, on-prem, and hybrid environments, including edge, containerized, serverless, API-based, and hosted platforms, with a focus on scalability, latency, and privacy and compliance requirements.
Selecting a Model Architecture4:16
Choose the right model architecture by weighing llms versus slms, assessing training data, benchmarks, cost, and scalability, with domain-specific examples from finance, legal, manufacturing, and healthcare.
Course Materials0:05

Setting Up the Environment4:00
Create a Streamlit Application for Text Summarization5:25
Build a Streamlit app that uses Cohere’s v2 chat endpoint to summarize text, with a serverless deployment, a Python environment, and an interactive text input and result display.
Run the App Locally for Testing5:25
Build a local Streamlit app for text summarization using the Cohere v2 chat endpoint, with a virtual environment and optional Streamlit Cloud deployment for serverless testing.
Selecting a Deployment Option2:47
Deploy the Application3:15

Setting Up the Environment4:32
Building the application7:39
Handling User Questions3:29
Building the Streamlit Interface4:23
Testing the application locally4:23
Deploying the application5:21
Create a docker image from a dockerfile and requirements.txt, run with streamlit, and deploy with flyctl for Gemini-based multi-PDF conversations.
Recap of Key Concepts and Applications3:25
Explore deploying generative AI across apps such as pdf chat with Google Gemini, speech-to-text with Assembly AI, and text summarizer via Cohere API. Highlight scalability, privacy, and cloud options.

Requirements

Basic understanding of Python programming concepts.
Familiarity with APIs and basic web development is helpful but not mandatory.
A computer with internet access and Python installed.
No prior AI or Machine Learning experience required—ideal for beginners!

Description

The gap between creating GenAI models and deploying them into production is where most AI initiatives fail. While countless courses teach you how to build models, few prepare you for the critical engineering challenges that determine whether your AI will reach users or remain trapped in development. This comprehensive course bridges that gap, transforming you from an AI enthusiast into a complete GenAI engineer.

Designed for developers and engineers ready to move beyond theory, this hands-on course guides you through the entire deployment lifecycle of generative AI systems. You'll build real-world applications using today's most powerful AI platforms while mastering the infrastructure that makes them reliable, scalable, and production-ready.

Through practical, project-based learning, you'll:

Master API Integration - Build seamless connections between GenAI models and applications using FastAPI, enabling powerful AI features with clean, efficient interfaces
Containerize AI Systems - Package complete GenAI applications with Docker to ensure consistent performance across any environment
Orchestrate with Kubernetes - Scale your GenAI deployments effectively, managing resources and ensuring reliability even under demanding workloads
Deploy Production Applications - Create and deploy four complete GenAI applications including a text summarizer, AI chatbot, audio transcription service, and an intelligent PDF chat system
Work with Leading AI Platforms - Gain hands-on experience with Cohere, Amazon Bedrock, AssemblyAI, and Google Gemini, building valuable skills with industry-standard tools

Each section builds toward complete, working applications that demonstrate real-world deployment patterns. You'll create deployable solutions for text summarization, conversational AI, audio processing, and document intelligence - covering the most in-demand GenAI capabilities in today's market.

By course completion, you'll have both the technical knowledge and portfolio projects to confidently implement GenAI solutions in production environments, skillsets that command premium compensation in today's AI-driven job market.

Don't let your AI models remain theoretical exercises. Enroll now and master the engineering skills that transform promising models into production-ready systems delivering real value.

The difference between AI enthusiasts and AI engineers is deployment — and this course will put you firmly in the latter category.

Who this course is for:

Developers and engineers looking to integrate AI into their applications.
Beginners curious about deploying AI without prior experience.
Data scientists aiming to scale their AI models into production.
Tech enthusiasts exploring generative AI tools and applications.
Entrepreneurs developing AI-powered solutions for businesses.
Professionals wanting to learn Docker, Kubernetes, and AI deployment.

Integration and Deployment of GenAI Models

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Course content

Introduction to Generative AI3 lectures • 14min