MLOps & LLM Deployment in Noida

About this course

This intermediate-level programme delivers a structured, instructor-led journey through modern MLOps and LLM deployment practices. Learners work through live sessions reinforced by hands-on labs in every module, building real systems with the actual tools used in production engineering teams. The course spans experiment tracking, containerisation, Kubernetes orchestration, LLM integration, and automated CI/CD, culminating in an end-to-end capstone that takes a model from code commit through to a fully monitored, production-grade deployment on a Kubernetes cluster.

What is MLOps & LLM Deployment?

MLOps is the discipline of applying software engineering and operations principles to the full machine learning lifecycle — from experiment tracking and model versioning through containerised serving, orchestration, and continuous monitoring. LLM Deployment extends this discipline to large language model applications: wrapping OpenAI API calls and LangChain pipelines in FastAPI services, managing prompt engineering patterns, controlling token budgets, and streaming responses at scale. Together they close the gap between a notebook prototype and a reliable production system. Tools like MLflow, Docker, Kubernetes, Helm, Prometheus, Grafana, and Evidently AI form the operational backbone, while LangChain and Hugging Face Hub handle the LLM layer. The result is software that can be versioned, rolled back, autoscaled, and observed exactly like any other production service.

Why learn MLOps & LLM Deployment now

Organisations are moving fast to ship LLM-powered products alongside traditional ML models, and the bottleneck is rarely the model itself — it is the engineering infrastructure needed to deploy and sustain it reliably. Teams that can instrument MLflow tracking pipelines, containerise FastAPI inference services with Docker, orchestrate them on Kubernetes, detect data and concept drift with Evidently AI, and wire the whole flow through GitHub Actions CI/CD are in short supply. Practitioners who combine classical MLOps rigour with hands-on LLM integration skills — LangChain, OpenAI API, Hugging Face endpoints, RAG pipelines — command attention across startups and enterprises alike. Closing that gap now, while the toolchain is still consolidating, positions engineers at the centre of production AI delivery.

Who this course is for

• Data Scientists moving to production — Practitioners who can train models but struggle to deploy, version, and monitor them reliably in live environments.
• Software engineers entering ML infrastructure — Backend developers comfortable with Python and REST APIs who want to specialise in ML platform and LLM serving engineering.
• MLOps engineers upskilling on LLMs — Engineers already running traditional pipelines who need to add LangChain, OpenAI API, and RAG deployment patterns to their toolkit.
• AI application developers — Builders creating LLM-powered features who need FastAPI, Docker, and Kubernetes skills to ship and sustain those features in production.
• DevOps engineers pivoting to ML workloads — Infrastructure specialists familiar with containers and CI/CD who want to apply those skills specifically to ML and LLM deployment pipelines.
• Technical leads overseeing AI delivery — Engineering leads who need fluency across MLflow, Kubernetes, Evidently AI, and GitHub Actions to guide and review production ML systems.

What you’ll be able to build

By the end you will have built — not just studied — the core systems of the field:

• MLflow-instrumented training pipeline — A fully tracked scikit-learn training workflow logging parameters, metrics, and artefacts to an MLflow tracking server, with models registered and staged in the Model Registry.
• Multi-step LangChain application — A working LangChain application chaining PromptTemplates, LLM calls via the OpenAI API, and a retrieval-augmented generation pipeline, with cost and token budget controls in place.
• FastAPI LLM inference service — A production-structured FastAPI service wrapping a LangChain pipeline, with Pydantic request validation, async streaming endpoints, error handling, and full API versioning.
• Containerised ML service with Docker — A production-grade Docker image for the FastAPI LLM service using multi-stage builds, optimised layers, and Docker Compose local orchestration, pushed to a container registry.
• Kubernetes deployment with Helm — A complete Kubernetes deployment of the containerised LLM service, including manifests, ConfigMaps, Secrets, horizontal pod autoscaling, and a Helm chart for repeatable releases with rolling updates and rollback.
• Prometheus and Grafana observability stack — A live monitoring setup instrumenting the FastAPI service with Prometheus metrics and Grafana dashboards, including alerting rules and trace logging for ML-specific signals.
• End-to-end CI/CD capstone pipeline — A GitHub Actions workflow that runs on every code commit, executes tests, rebuilds the Docker image, deploys to Kubernetes, and triggers Evidently AI drift detection on the live service.

Career paths & salary

Completing this programme prepares learners for roles where the ability to ship and sustain ML and LLM systems in production is the core requirement. MLOps Engineer and ML Platform Engineer positions are among the most actively recruited in the AI space, with organisations building dedicated teams to own model lifecycle, infrastructure, and reliability. AI/ML Engineers who can move fluidly between LangChain application development and Kubernetes orchestration are valued across both product and platform teams. LLM Application Developer roles are growing rapidly as companies productionise generative AI features. Machine Learning Infrastructure Engineers and AI Solutions Engineers who bring hands-on fluency with the full stack — MLflow, FastAPI, Docker, Kubernetes, Prometheus, Grafana, Evidently AI, and GitHub Actions — are consistently sought after. Data Scientists with a production focus find that MLOps skills substantially broaden the scope and seniority of roles available to them.