What is MLOps and why is it important?

MLOps (Machine Learning Operations) bridges the gap between data science and production engineering. It covers model versioning, CI/CD pipelines, monitoring and scalable deployment — skills that make you industry-ready.

What tools are taught in the MLOps course at AlgoAcademy?

You will learn Docker, FastAPI, Apache Airflow, MLflow, Grafana, Prometheus, AWS SageMaker and GitHub Actions for end-to-end ML pipeline automation.

What is the duration of the MLOps course in Chennai?

The Applied AI and MLOps course is 3 months long with live expert-led sessions and real deployment projects.

Who should take the MLOps and AI deployment course?

This course is ideal for data scientists, ML engineers and software developers who want to learn how to deploy, monitor and maintain AI models in production environments.

What projects will I complete in the MLOps course?

You will build an end-to-end ML pipeline with FastAPI, create a model monitoring dashboard with Grafana, deploy a model on AWS and automate retraining workflows with Airflow.

⚙️ MLOps Track

MLOps Course in Chennai — Deploy & Scale AI Systems in Production

Bridge the gap between data science and engineering. Build automated ML pipelines, CI/CD for models, and monitoring systems that keep AI reliable.

View Syllabus ↓

8 Modulescovered

6+ Projectsbuilt

Docker + Kubernetestools

MLOps Engineeroutcome

📚 Course Syllabus

MLOps Skills You'll Master

Every module is outcome-driven — you build something real at the end of each one.

ML lifecycleTraining vs servingData driftModel decaySLOsMonitoring

Understand why 87% of ML models never reach production

Map the full ML deployment lifecycle

Define SLOs for ML systems

Think like an ML engineer, not just a data scientist

PickleJoblibONNXMLflowModel versioningArtifacts

Serialize and load ML models reliably

Track model versions with MLflow

Export models to framework-agnostic ONNX format

Package any ML model for reproducible deployment

FastAPIPydanticAsync requestsInput validationOpenAPI docsLoad testing

Build production-grade REST APIs for ML models

Validate and sanitize model inputs

Document APIs automatically with OpenAPI

Expose any ML model as a scalable REST API

DockerfileDocker ComposeMulti-stage buildsVolumesNetworksRegistry

Containerize ML applications for any environment

Build optimized Docker images for inference

Orchestrate multi-service ML systems

Run ML models identically in dev, staging, and production

GitHub ActionsAutomated testingModel validationData validationDeployment gatesRollback

Automate model training and evaluation pipelines

Gate deployments on model quality thresholds

Implement rollback strategies for bad models

Automatically test and deploy ML models on every code push

DAGsTask schedulingData ingestion pipelinesRetraining triggersMonitoring hooks

Build scheduled retraining pipelines

Trigger retraining on data drift detection

Monitor pipeline health and failures

Build automated pipelines that keep models fresh

Data drift detectionPrediction driftGrafanaPrometheusAlertingEvidently AI

Detect when model performance degrades

Set up real-time alerting for model failures

Build dashboards for model KPIs

Monitor model health and catch problems before users do

AWS SageMakerGCP Vertex AIS3/GCSLambda functionsAuto-scalingCost optimization

Deploy models to managed cloud ML platforms

Set up auto-scaling for inference workloads

Optimize cloud costs for ML serving

Deploy production ML systems on major cloud platforms

🛠 Tech Stack

MLOps Tools & Platforms

Industry-standard tools you'll use throughout this program — exactly what employers want.

🐳

Docker

Containerization

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FastAPI

Model APIs

📊

MLflow

Experiment tracking

☁️

AWS/GCP

Cloud deploy

🔄

Airflow

Pipeline orchestration

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Grafana

Monitoring dashboards

🔒

GitHub Actions

CI/CD

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ONNX

Model optimization

💻 Real Projects

Production AI Pipelines You'll Build

Portfolio-ready projects that demonstrate your skills to recruiters from day one.

PROJECT 01

ML Model REST API

Wrap a trained ML model as a FastAPI service with full input validation, logging, and OpenAPI docs.

FastAPIScikit-learnPydanticDocker

✅ Production-ready ML API with 100ms latency

PROJECT 02

Automated Retraining Pipeline

Airflow DAG that monitors data drift, triggers retraining, and deploys if quality improves.

AirflowMLflowEvidentlyDocker

✅ Self-healing ML pipeline running 24/7

PROJECT 03

Model Monitoring Dashboard

Grafana + Prometheus dashboard tracking prediction drift, latency, and error rates.

GrafanaPrometheusFastAPIPython

✅ Real-time model health monitoring system

PROJECT 04

Cloud ML Deployment

Deploy a containerized ML service to AWS with auto-scaling and load balancing.

DockerAWS ECSS3CloudWatch

✅ Scalable cloud ML service handling 10K+ requests/day

🎯 Transformation

MLOps Engineer Roles You'll Land

By the end of this program, you won't just have knowledge — you'll have career-changing proof.

⚙️

MLOps Engineer

₹15L–₹40L at product companies

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Production Mindset

Build systems that actually stay running

☁️

Cloud Proficient

Deploy on AWS, GCP and Azure

🔄

Automation Expert

Build self-maintaining ML pipelines

🏢 Placement Network

Our Students Work Here

Frequently Asked Questions

About the MLOps & AI Deployment Course in Chennai

The MLOps course is 3 months long covering Docker, FastAPI, Airflow, MLflow, Grafana, AWS and full ML pipeline orchestration and monitoring.

You will learn Docker, FastAPI, Apache Airflow, MLflow, Grafana, Prometheus, AWS EC2 and S3 to build and deploy production-grade AI pipelines.

You should have basic Python and foundational ML knowledge. The course then teaches Docker, FastAPI, Airflow and cloud deployment from the ground up.

MLOps engineers in Chennai earn ₹10–20 LPA. With hands-on Docker, Airflow and cloud deployment skills, senior roles can offer ₹25–35 LPA.

You will build a full ML inference API with FastAPI, an automated retraining pipeline with Airflow, a model registry with MLflow and a real-time monitoring dashboard.