LLMOps Masterclass 2026 - Generative AI - MLOps - AIOps

Generative ai reshapes daily life by autonomously generating content from natural language prompts, powering art, design, personalized experiences, and improved natural language understanding in chatbots and interfaces.

Explore generative AI as a digital brain that mimics human intelligence, enabling automation, data analysis, personalized experiences, and advances in NLP, healthcare, and autonomous systems.

Define and compare levels of AI, from narrow or weak AI to artificial general intelligence, limited self-aware AI, full self-aware AI, and super intelligent AI, with examples and ethical considerations.

Explore artificial intelligence, from narrow AI to AGI, including generative AI, and learn machine learning, deep learning, reinforcement learning, NLP, computer vision, expert systems, robotics, and autonomous systems.

Explore generative AI's autonomous content creation across images, text, and music, mimicking human creativity and evolving with data to drive innovation in diverse domains, including healthcare and drug discovery.

Generative models generate new data by learning the joint distribution, while discriminative models learn feature-target patterns for classification and define decision boundaries.

Explore real world applications of generative AI, including OpenAI ChatGPT, Google Gemini, and GPT-3, with text generation and machine translation.

Explore why generative AI captivates global attention through creativity, realism, and cross-industry applications, from GPT and StyleGAN outputs to prompt engineering concepts.

Master prompt engineering by crafting precise prompts, providing relevant context and examples to steer generative AI outputs and optimize model performance.

Explore the architecture of a prompt, including instruction, context, structured examples, and system messages, and learn how precise prompts and organized examples boost large language model performance and task alignment.

Apply clear, specific prompts with relevant context and examples to guide language models, using template-based prompts and iterative tuning to customize, experiment, and refine outputs.

Explore what large language models are, how prompts generate human-like text, and how increasing parameters enhances pattern understanding in training workflows, including data collection, tokenization, and deployment.

Collect a diverse internet text corpus, tokenize, select a transformer such as GPT or BERT, pre-train unsupervised, fine-tune on downstream tasks, evaluate and tune hyperparameters, deploy, and enable continuous learning.

Explore how enterprises deploy large language models for customer support, content generation, data analysis, automated translation, knowledge management, compliance, training and education, and R&D to drive efficiency.

Master the prerequisites to build an AI assistant with chatgpt by creating an openai account, enabling billing, and generating an api key to power your project.

Explore the OpenAI playground to interact with ChatGPT, customize behavior with your data, and build assistants using the assistance API, chat API, code interpreter, retrieval, and function calling.

Create a personalized assistant using the OpenAI playground’s assistants API, configure a name and instruction, select GPT-4 Turbo, and enable functions, code interpreter, and retrieval for file-based answers.

Learn to build AI-driven assistants with the assistant API, enabling tools like retrieval, code interpreter, and function calling, integrated via threads in a Python Colab workflow.

master fastapi to package ai apps for production, building python-based apis with get and post endpoints and uvicorn deployment, and explore docs for testing.

Package an AI app with FastAPI and OpenAI, storing API key and assistant ID in config.py, enabling modular blue-green deployment and a /response post endpoint with pedantic input validation.

Test a fast API application using Postman to send a POST request with a JSON body to a local URL, and review the model's response.

Create a requirements.txt file listing essential dependencies like OpenAI, FastAPI, and Uvicorn to set up a virtual environment and install them with pip install -r requirements.txt.

Discover how Docker packages LM applications into containers with images, Dockerfile, Docker Compose, Docker Hub, and container orchestration with Kubernetes and Docker Swarm, enabling consistent development to production and CI/CD.

Install Docker Desktop on your Mac or Windows by downloading the installer from docker.com. Follow the guidance to enable WSL 2 on Windows, then launch and initialize Docker Desktop.

Explore Docker fundamentals by pulling images from Docker Hub, running containers, mapping ports, and building a custom image with a Dockerfile, then push to Docker Hub.

Create a docker file with a python 3.10 base, copy files, install requirements, and run main.py to build and push the image to docker hub for the LM ops chatbot.

Explore how Kubernetes acts as an application orchestrator for containerized cloud native microservices. Enable deployment, dynamic scaling, self-healing, and zero-downtime rolling updates with rollbacks, configs, and secrets.

Explore the architecture of Kubernetes, from the control plane and worker nodes to the API server, scheduler, etcd, kubelet, and how pods and containers run in a cluster.

Learn how Kubernetes runs anywhere—from laptops to cloud servers and Raspberry Pi clusters—and install it on your laptop via Docker Desktop, enabling a single-node cluster, and kubectl verification.

Deploy a containerized app on Kubernetes by wrapping it in a pod defined in a YAML file; kubectl apply -f pod.yaml runs it, with a single pod per node.

Define and deploy a Kubernetes service that exposes pods via a stable front end, using a yaml file, selectors, and a node port to route localhost traffic to port 80.

Create and manage a Kubernetes deployment to encapsulate pods, enabling self-healing, scaling, and rolling updates. Use deployment.yaml to run five replicas and observe the deployment controller reconcile the state.

Demonstrate manual scaling of a Kubernetes deployment by adjusting replicas from five to ten and back to two, using deployment objects and kubectl commands to observe scaling and self-healing.

Explore performing rolling updates in Kubernetes by applying a deployment YAML that uses a rolling update strategy with min ready seconds, max surge, and max unavailable to avoid downtime.

Explore how Kubernetes config maps store configuration data as key-value pairs to decouple config from code, enabling environment-specific, flexible deployments and easy management with kubectl or YAML.

Explore creating and using Kubernetes config maps with an imperative workflow, injecting config values into pods as environment variables, and preparing for secrets integration.

Learn how Kubernetes secrets securely store sensitive data like passwords, API keys, and certificates, enabling pods to access them as environment variables or mounted volumes.

Design and write a small independent microservice, package it as a container, and define its configuration for deployment to Kubernetes. Wrap pods and orchestrate with deployments, daemonsets, and cron jobs.

Orchestrate containers with Kubernetes by creating a secret for the OpenAI API key, injecting it as an environment variable, and deploying a secured ChatGPT app.

Explore how GitHub actions automate builds, tests, and deployments via customizable workflows triggered by repository events, using YAML-defined jobs, steps, actions, and docker-based runners.

Create your first GitHub actions workflow by adding a .github/workflows yaml file to your repository, then push and observe the workflow run on GitHub.

Understand how a GitHub actions YAML file defines and triggers a workflow, with naming and a run name, on push, using an Ubuntu runner and the checkout action v4.

Create a fresh GitHub Actions yaml file that triggers on push to the main branch, defines a workflow with ubuntu latest, and demonstrates environment variable usage.

Configure a GitHub actions workflow from marketplace to build a Docker image, publish to Google Container Registry, and deploy to Google Kubernetes Engine, using deploy.yaml, customization.yaml, and service.yaml, with authentication.

Connect to Google Cloud, configure the Cloud CLI, and create a GKE cluster to deploy the ChatGPT application, guided by the course repository with step-by-step instructions.

Set up the Google Cloud CLI and authenticate with gcloud. Configure your project ID and enable the Kubernetes API to access cloud resources.

Create a Google Kubernetes Engine cluster with the gcloud CLI by setting the cluster name, node count (three) and zone, then verify with kubectl and manage the cluster context.

Deploy a chatgpt-based application on a GKE cluster using deploy.yaml, create a kubernetes secret for the API key, apply the configuration, and validate access via a load balancer.

Specify linux/amd64 in your Dockerfile and build with docker buildx to ensure the image runs on Kubernetes clusters, push to Docker Hub, update deploy.yaml, and enable ci/cd with GitHub actions.