AI Receptionist
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. 100% placement assurance.

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90+ Hours Live Hands-on
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Start Your Career in MLOps & LLMOps

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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
โšก
  • 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
โšก
  • 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
โšก
  • 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
โšก
  • 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:

  • โœ” The MLOps Maturity Model: From Manual Scripts to Automated CI/CD
  • โœ” Data Version Control (DVC): Tracking Gigabytes of Datasets with Git Integration
  • โœ” Remote Storage Configuration: S3, GCS, Azure Blob with DVC
  • โœ” DVC Pipelines: Reproducible DAGs with dvc.yaml and Parameter Tracking
  • โœ” Setting up Production Python Environments with Poetry, Conda & Pre-Commit Hooks
๐Ÿ’ป 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:

  • โœ” MLflow Architecture: Tracking Server, Artifact Store & Backend DB (PostgreSQL)
  • โœ” Logging Hyperparameters, Loss Curves, Precision-Recall & Model Artifacts
  • โœ” MLflow Autologging with PyTorch, Scikit-Learn, and XGBoost
  • โœ” MLflow Model Registry: Staging, Production, and Archived State Transitions
  • โœ” Model Packaging: MLflow Models, PyFunc Flavors & Conda/Docker Environments
๐Ÿ’ป 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:

  • โœ” Dockerfiles for ML: Multi-Stage Builds, CUDA Base Images, Minimizing Image Sizes
  • โœ” FastAPI Model Serving Containers with Gunicorn & Uvicorn Workers
  • โœ” Kubernetes Foundations: Pods, Deployments, Services, ConfigMaps, Secrets
  • โœ” NVIDIA GPU Operator for Kubernetes: Enabling GPU Acceleration in Clusters
  • โœ” Horizontal Pod Autoscaling (HPA) based on Request Volume and GPU Utilization
๐Ÿ’ป 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:

  • โœ” Kubeflow Pipelines (KFP): Building Containerized Components & Pipeline DSL
  • โœ” Passing Artifacts, Datasets, and Metrics Between Pipeline Steps
  • โœ” Apache Airflow for Data-to-ML Scheduled Orchestration
  • โœ” Automated Model Evaluation Gates: Promoting Models only if Metrics Exceed Production Baseline
  • โœ” Continuous Training (CT) Triggers: Scheduling vs Data Drift Triggers
๐Ÿ’ป 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:

  • โœ” Inference Challenges: Memory Bandwidth vs Compute Bound, KV-Cache Inflation
  • โœ” vLLM Architecture: PagedAttention, Tensor Parallelism & Continuous Batching
  • โœ” Triton Inference Server: Multi-Model Concurrency, Dynamic Batching & Ensembles
  • โœ” Quantized Model Serving: AWQ, FP8, INT4 Inference for Maximum Throughput
  • โœ” API Gateway Layer: Rate Limiting, Streaming SSE Responses & Load Balancing
๐Ÿ’ป 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:

  • โœ” LLMOps Observability: Langfuse, Arize Phoenix & OpenTelemetry Tracing
  • โœ” Prompt Versioning, Testing & Staging in Production Gateways
  • โœ” Semantic Prompt Caching with Redis to cut API Costs by 40-70%
  • โœ” Detecting Data Drift, Concept Drift & Model Performance Degradation
  • โœ” CI/CD for AI using GitHub Actions: Automated Linting, Unit Testing & Docker Registry Push
๐Ÿ’ป 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
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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
Designed for job-focused learners
48,500+
Successful Learners
500+
Hiring Partners
โ‚น12.5 LPA
Highest Package
140%
Average Career Growth
100%
Interview Guarantee

Career & Salary Calculator

Explore verified 2026 compensation benchmarks and market demand curves across India's top tech hubs.

๐Ÿ’ฐ Estimated Salary Range
Market Data 2026
₹ 8.5 LPA – 16.5 LPA
Frontier AI Specialist | 1-3 Years | Delhi NCR
๐Ÿ“ˆEntry Level
₹ 7.5 – 9.5 LPA
๐Ÿ’ผMid Level
₹ 11.0 – 16.5 LPA
โญLead Architect
₹ 25.0+ LPA

Based on verified 2026 hiring data from Fortune 500 and Top MNC tech recruiters.

Salary Curve by Experience
High-Paying Frontier Track
7.5L
Fresher
12.5L
1 - 3 Yrs
18.5L
3 - 5 Yrs
28.0L
5 - 8 Yrs
42.0L+
8+ Yrs

Frequently Asked Questions

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