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

English [Auto],

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
Explore how generative AI uses prompts, NLP, and visual encoding to create new content, and examine applications in creative content, productivity, and personalization at scale.
Types of Generative Models5:05
Explore types of generative models, from GANs and VAEs to NeRF, diffusion models, and transformers, highlighting architectures, trade-offs, applications, and selection criteria for GenAI deployments.
Challenges in Deploying Generative AI Systems5:15
Identify deployment challenges of generative ai, including output quality, data management, legacy integration, latency, cost, and explore composite ai solutions such as rag retrieval, vector databases, and augmented generation.

GenAI Integration Options4:32
Explore cloud-based apis, no-code platforms, custom application integration, and edge deployments to embed generative ai in apps, with pragmatic deployment considerations and real-world use cases.
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

Overview of APIs4:27
Explore core API concepts, types (REST, SOAP, RPC, WebSocket), benefits, and architectures, and learn how FastAPI enables building robust APIs for generative AI models.
Create the FastAPI App4:35
Create a fast API app that calls OpenAI models using an API key, set up a Python project, install the OpenAI package, and test content generation with a haiku.
Designing APIs for AI Model Interaction4:03
Design APIs for ai by building a fast api that serves a generative ai model via OpenAI's GPT-4 mini, with a post endpoint for generate and pydantic validation.
Frontend for Displaying Application5:21
Learn to build a Streamlit front end that displays a FastAPI backend, configure CORS middleware, run backend and front end concurrently, and generate content via an API endpoint.

Introduction to Docker and Dockerfile4:20
Explore docker and containerization, understanding how containers create self-contained, portable, and efficient environments. Learn docker concepts: images, containers, daemon, docker hub, base and official images, and dockerfile.
Setting up the environment3:02
Install Docker Desktop to build images and run containers locally. Review a sample Dockerfile showing base image, working directory, dependencies, copy, run, expose, and uvicorn.
Creating and Running a Container for the Generative AI Model3:05
Create and run a docker container for a streamlit app by writing a dockerfile with a base image, dependencies, and requirements.txt, then build and run to expose port 8501.

Kubernetes Basics for AI Workloads3:15
Discover how Kubernetes manages containerized workloads for AI tasks, enabling scalable training and data processing with GPU and TPU support, automated resource management, and cross-platform deployment.
Creating Kubernetes Deployments for Generative AI Models3:11
Deploy generative AI models on Kubernetes by creating deployment and service configurations, setting two replicas, port 8501, and exposing the Streamlit app via a load balancer.
Applying the deployment configuration and pod scaling4:12
Apply deployment configuration and scale pods in a local Kubernetes cluster by using kubectl with Docker Desktop Kubernetes, port forwarding, and deploying the content generator Docker image from Docker Hub.

Setting Up the Environment4:00
Set up a development environment and build a text summarization tool using the Cohere API, including creating a project folder, obtaining an API key, and running a Python script.
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
Explore selecting a deployment option for GenAI apps by contrasting serverless Streamlit Cloud with local testing. Learn to build and test a Cohere-based text summarization app before deployment.
Deploy the Application3:15
Explore deployment options for GenAI apps across Azure, AWS, Google Cloud, Heroku, Streamlit, and Python Anywhere, with a focus on Streamlit for simple data science deployments.

Set Up AWS Account and Permissions6:15
Set up an AWS account and permissions, install and configure the AWS CLI, create an IAM admin group with access keys, and enable Bedrock model access for an AI chatbot.
Set Up AWS Account and Permissions5:39
Set up an AWS account and permissions to build an AWS Bedrock chatbot with LangChain, boto3, and a Streamlit front end; configure prompts and model ID for GenAI deployment.
Build the Streamlit Front End4:29
Build a Streamlit front end to power a Bedrock-based chatbot, enabling model selection (Anthropic cloud v2/v3), user input, and live responses from AWS Bedrock integration.
Testing the Application Locally3:22
Test the bedrock chatbot locally using the anthropic cloud model, after setting up a Python virtual environment and installing langchain, streamlit, and boto3, then run app.py.
Selecting the Deployment Option3:50
Explore multiple deployment options for chatbot apps, including AWS EC2, Streamlit Cloud, Heroku, Azure, and Render, with free plans, pricing, and easy Git integration.
Deploy the Chat Application3:50
Deploy a chatbot using AWS EC2, Streamlit Cloud, Heroku, Azure, and Render, focusing on free options and scalability. Connect Render to a GitHub repo and deploy a Docker-based app.

Setting Up the Environment3:08
Explore setting up speech-to-text with Assembly AI, the industry's most accurate ASR model, offering live streaming with under 600 ms latency and 90% accuracy, plus Python integration.
Designing the Application4:59
Design a speech-to-text application using assemblyai, transcribing online or local audio files via an api key, and display results with a simple streamlit interface.
Building the User Interface4:43
Build a Streamlit front end to upload mp3 audio and transcribe with assembly ai. Use an environment variable for the API key and test locally with a requirements file.
Test the application locally3:31
Test the speech-to-text app locally with Streamlit, install dependencies, set the AssemblyAI API key, upload MP3s for transcription, and review deployment options like Render and Streamlit Cloud.
Choosing the Deployment Option5:12
Learn to deploy a web app on fly.io using docker and flyctl. Create a dockerfile for Streamlit, build a docker image, and set environment variables to enable a successful deployment.
Deploying the Application4:51
Deploy and monitor an audio transcription app built with assembly AI on fly.io using flightctl, configure secrets and environment variables, set billing, and review live logs and deployment metrics.

Setting Up the Environment4:32
Set up the environment for a multi-pdf generative AI workflow using Google Gemini, PyPDF2, LangChain, and phis for embeddings and fast retrieval, with Streamlit for the user interface.
Building the application7:39
Develop a pdf chat app that extracts text from pdfs, splits it into chunks, builds a vector store with Google embeddings, and answers with a no-guess prompt via Gemini Pro.
Handling User Questions3:29
Learn to handle user questions by embedding queries, loading a local index, performing similarity search, and using a conversational chain to generate and display responses in Streamlit.
Building the Streamlit Interface4:23
Build a Streamlit interface to upload PDFs, extract text, split into chunks, and store embeddings for a vector store; use a conversational chain to answer questions from PDFs with AI.
Testing the application locally4:23
Test the application locally by configuring the dot env with the Google API key, creating a virtual environment, installing dependencies, and running Streamlit to extract PDF text and answer questions.
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