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DevOps for AI Track 2026

MLOps & LLMOps
Architect Production CI/CD for AI, Kubernetes Scaling & LLM Observability.
Get Placed in Top MNCs.

Master MLOps & LLMOps with MLflow, Kubeflow, Docker, Kubernetes, vLLM, Triton, DVC, Airflow, and Langfuse. 1-on-1 Mock Interview SessionsDedicated Support.

4.9/5 Rating (1.8k+ Reviews)
90+ Hours Live Hands-on
16+ Ops Tools Modern Stack
Production Capstone Labs
1-on-1 Mock Interview SessionsDedicated Support Support
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MLOps & LLMOps Certification Course Student Working on AI Projects
Docker
Docker
Kubernetes
Kubernetes
MLflow
MLflow
Kubeflow
Kubeflow
Airflow
Airflow
vLLM
vLLM
AWS
AWS
Git
Git
🖥️Live Interactive Coding
👤AI Principal Mentors
📋Enterprise Capstones
💼Career Mentorship Assurance

Start Your Career in MLOps & LLMOps

Get personalized curriculum & free 1-on-1 counseling.

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◆ Enterprise-Aligned Curriculum

Complete MLOps & LLMOps Curriculum — Foundations to Enterprise Scale

Engineered in collaboration with principal engineers from leading product and Fortune 500 AI teams.

01

ML Lifecycle, Reproducibility & Data Version Control (DVC)

⏱ In-Depth Module
⚡
  • The MLOps Maturity Model: From Manual Scripts to Automated CI/CD
  • Data Version Control (DVC): Tracking Gigabytes of Datasets with Git Integration
  • Remote Storage Configuration: S3, GCS, Azure Blob with DVC
→
02

Experiment Tracking & Model Registry with MLflow

⏱ In-Depth Module
⚡
  • MLflow Architecture: Tracking Server, Artifact Store & Backend DB (PostgreSQL)
  • Logging Hyperparameters, Loss Curves, Precision-Recall & Model Artifacts
  • MLflow Autologging with PyTorch, Scikit-Learn, and XGBoost
→
03

Containerization & Kubernetes for Machine Learning

⏱ In-Depth Module
⚡
  • Dockerfiles for ML: Multi-Stage Builds, CUDA Base Images, Minimizing Image Sizes
  • FastAPI Model Serving Containers with Gunicorn & Uvicorn Workers
  • Kubernetes Foundations: Pods, Deployments, Services, ConfigMaps, Secrets
→
04

LLMOps: High-Throughput Serving with vLLM & Triton

⏱ In-Depth Module
⚡
  • Inference Challenges: Memory Bandwidth vs Compute Bound, KV-Cache Inflation
  • vLLM Architecture: PagedAttention, Tensor Parallelism & Continuous Batching
  • Triton Inference Server: Multi-Model Concurrency, Dynamic Batching & Ensembles
→

Detailed Module-by-Module Breakdown

Click each module below to explore technical topics, coding labs, and tools covered.

Module 01

ML Lifecycle, Reproducibility & Data Version Control (DVC)

14 Hours

Establish rigorous engineering hygiene for Machine Learning code, data, and model artifacts.

Core Topics & Competencies:

  • ✔ The MLOps Maturity Model: From Manual Scripts to Automated CI/CD
  • ✔ Data Version Control (DVC): Tracking Gigabytes of Datasets with Git Integration
  • ✔ Remote Storage Configuration: S3, GCS, Azure Blob with DVC
  • ✔ DVC Pipelines: Reproducible DAGs with dvc.yaml and Parameter Tracking
  • ✔ Setting up Production Python Environments with Poetry, Conda & Pre-Commit Hooks
💻 Hands-on Capstone Lab:

Building a Multi-Stage Data Ingestion & Preprocessing DVC Pipeline synced with AWS S3.

DVCGitAWS S3PoetryPython 3.11
Module 02

Experiment Tracking & Model Registry with MLflow

16 Hours

Track thousands of model training runs, parameters, metrics, and manage model lifecycle states.

Core Topics & Competencies:

  • ✔ MLflow Architecture: Tracking Server, Artifact Store & Backend DB (PostgreSQL)
  • ✔ Logging Hyperparameters, Loss Curves, Precision-Recall & Model Artifacts
  • ✔ MLflow Autologging with PyTorch, Scikit-Learn, and XGBoost
  • ✔ MLflow Model Registry: Staging, Production, and Archived State Transitions
  • ✔ Model Packaging: MLflow Models, PyFunc Flavors & Conda/Docker Environments
💻 Hands-on Capstone Lab:

Self-Hosted Remote MLflow Tracking Server with PostgreSQL and S3 Artifact Storage.

MLflowPostgreSQLAWS S3Scikit-LearnDocker
Module 03

Containerization & Kubernetes for Machine Learning

16 Hours

Package models into microservices and deploy scalable clusters with Docker and Kubernetes.

Core Topics & Competencies:

  • ✔ Dockerfiles for ML: Multi-Stage Builds, CUDA Base Images, Minimizing Image Sizes
  • ✔ FastAPI Model Serving Containers with Gunicorn & Uvicorn Workers
  • ✔ Kubernetes Foundations: Pods, Deployments, Services, ConfigMaps, Secrets
  • ✔ NVIDIA GPU Operator for Kubernetes: Enabling GPU Acceleration in Clusters
  • ✔ Horizontal Pod Autoscaling (HPA) based on Request Volume and GPU Utilization
💻 Hands-on Capstone Lab:

Deploying a GPU-Accelerated Object Detection API on Kubernetes with HPA and Ingress.

DockerKubernetesNVIDIA Container ToolkitFastAPIHelm
Module 04

Pipeline Orchestration with Kubeflow & Apache Airflow

16 Hours

Automate end-to-end continuous training, model validation, and automated deployment pipelines.

Core Topics & Competencies:

  • ✔ Kubeflow Pipelines (KFP): Building Containerized Components & Pipeline DSL
  • ✔ Passing Artifacts, Datasets, and Metrics Between Pipeline Steps
  • ✔ Apache Airflow for Data-to-ML Scheduled Orchestration
  • ✔ Automated Model Evaluation Gates: Promoting Models only if Metrics Exceed Production Baseline
  • ✔ Continuous Training (CT) Triggers: Scheduling vs Data Drift Triggers
💻 Hands-on Capstone Lab:

Automated Continuous Training (CT) Pipeline on Kubeflow with Metric Verification Gate.

KubeflowAirflowKubernetesPython DSL
Module 05

LLMOps: High-Throughput Serving with vLLM & Triton

14 Hours

Scale generative AI foundation models with state-of-the-art inference engines.

Core Topics & Competencies:

  • ✔ Inference Challenges: Memory Bandwidth vs Compute Bound, KV-Cache Inflation
  • ✔ vLLM Architecture: PagedAttention, Tensor Parallelism & Continuous Batching
  • ✔ Triton Inference Server: Multi-Model Concurrency, Dynamic Batching & Ensembles
  • ✔ Quantized Model Serving: AWQ, FP8, INT4 Inference for Maximum Throughput
  • ✔ API Gateway Layer: Rate Limiting, Streaming SSE Responses & Load Balancing
💻 Hands-on Capstone Lab:

High-Concurrency vLLM Serving Cluster delivering 100+ tokens/sec across multi-GPU nodes.

vLLMTriton Inference ServerNVIDIA CUDAFastAPINGINX
Module 06

LLM Observability, Prompt Management & Cost Governance

14 Hours

Monitor LLM token usage, latency, prompt drift, and trace multi-step reasoning chains in production.

Core Topics & Competencies:

  • ✔ LLMOps Observability: Langfuse, Arize Phoenix & OpenTelemetry Tracing
  • ✔ Prompt Versioning, Testing & Staging in Production Gateways
  • ✔ Semantic Prompt Caching with Redis to cut API Costs by 40-70%
  • ✔ Detecting Data Drift, Concept Drift & Model Performance Degradation
  • ✔ CI/CD for AI using GitHub Actions: Automated Linting, Unit Testing & Docker Registry Push
💻 Hands-on Capstone Lab:

Enterprise LLM Gateway with Redis Semantic Caching, Token Throttling & Langfuse Telemetry.

LangfuseRedisGitHub ActionsPrometheusGrafana

Tools & Frameworks You Will Master

Gain hands-on proficiency in the exact modern tech stack used across Fortune 500 tech teams.

Docker
Docker
Kubernetes
Kubernetes
MLflow
MLflow
Kubeflow
Kubeflow
Airflow
Airflow
vLLM
vLLM
AWS
AWS
Git
Git
LAUNCHPAD PRO

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Live Project Work
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Completion Certificate
Explore LaunchPad Pro → See What You'll Work On →
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Hands-on • Mentor-led

Already trained. Now build proof of your skills.

Turn learning into practical experience you can discuss with confidence.

  • Work on live enterprise projects
  • Build a real-world portfolio
  • Use Agile workflows & Jira
  • Get mentor feedback
  • Practice with mock interviews
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Estimated Salary Range Based on Market Data
₹ 11.5 LPA – 20.0 LPA
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Frequently Asked Questions

Everything you need to know about the MLOps & LLMOps Certification Course, batches, prerequisites & Career Mentorship assurance.

What is the difference between MLOps and LLMOps?

MLOps focuses on traditional machine learning pipelines: tabular data ingestion, model training, feature stores, experiment tracking, and batch/real-time inference. LLMOps specializes in foundation model operations: prompt versioning, continuous KV-cache batching (vLLM), token cost monitoring, vector database scaling, and RAG observability.

Do I need DevOps experience to join this course?

Basic knowledge of Linux commands and Python is sufficient. We teach Docker, Kubernetes, Helm, CI/CD, and cloud infrastructure from scratch with hands-on labs.

Which cloud platforms are supported in the labs?

All tools taught (Docker, Kubernetes, MLflow, vLLM, Airflow) are cloud-agnostic open-source standards. You will practice deployment patterns that work seamlessly across AWS, Google Cloud, Azure, or private on-premise GPU clusters.

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