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RS Trainings

Data Science, AI & Analytics

Best MLOps with AI Training in Hyderabad

A hands-on MLOps with AI course in Hyderabad covering ML pipelines, MLflow, Docker, Kubernetes, CI/CD, model monitoring and LLMOps on the cloud. Learn live from working ML engineers at our Madhapur classroom or through MLOps with AI online training in Hyderabad.

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Training modes
Live online · Classroom (Madhapur) · Corporate
Curriculum
25 modules, 325 topics
Before you enroll
Free demo session with the trainer
Missed a class?
Recorded sessions and other live batches

What is MLOps with AI?

MLOps (Machine Learning Operations) is how teams take AI and machine learning models from notebooks to reliable production systems. As the best MLOps with AI training in Hyderabad, our course teaches you to version data and models, automate training and deployment, and monitor models in production, including generative AI and LLM applications.

Where MLOps with AI is used

01

Deploying ML models

Packaging models as APIs with Docker and FastAPI and running them on Kubernetes or managed cloud services.

02

Automated ML pipelines

Retraining and releasing models automatically with CI/CD, pipelines and experiment tracking.

03

Model monitoring

Tracking accuracy, data drift and performance so models keep working after go-live.

04

LLM and GenAI operations

Running large language model apps in production with evaluation, prompt versioning and cost control.

Platforms and tools you will work on

  • Python
  • MLflow
  • DVC
  • Docker
  • Kubernetes
  • Kubeflow
  • GitHub Actions
  • FastAPI
  • AWS SageMaker / Azure ML
  • Prometheus and Grafana

Why now

Why learn MLOps with AI in 2026

AI needs production skills

Most AI projects fail to reach production; companies need engineers who can deploy and run models reliably.

Combines DevOps and AI

MLOps is one of the best-paid specialisations because it blends software, cloud and machine learning skills.

GenAI is driving demand

The rise of LLM applications has created new LLMOps roles in Hyderabad and beyond.

Who this course is for

  • Data scientists who want to deploy their models
  • DevOps and cloud engineers moving into AI
  • Software engineers working on AI products
  • ML engineers who want to strengthen production skills

Before you start

  • Basic Python programming
  • Basic machine learning concepts are helpful

Tools and technologies you will use

  • Python
  • Scikit-learn
  • MLflow
  • DVC
  • Git and GitHub
  • FastAPI
  • Docker
  • Kubernetes
  • GitHub Actions
  • Jenkins
  • Apache Airflow
  • Kubeflow
  • Amazon SageMaker
  • Azure Machine Learning
  • Prometheus and Grafana
  • OpenTelemetry
  • Vector databases

What you will be able to do

  • Prepare data and build ML models with Scikit-learn
  • Track experiments and register models with MLflow
  • Version data and models with Git and DVC
  • Package models as FastAPI services and run them in Docker
  • Deploy models on Kubernetes, AWS SageMaker and Azure ML
  • Automate CI/CD and continuous training pipelines
  • Monitor models for drift and build observability dashboards
  • Apply governance, security and responsible AI practices
  • Run and monitor LLM and RAG applications in production

MLOps with AI training institute in Hyderabad

RS Trainings is a MLOps with AI training institute in Hyderabad located at Nukala Residency, Madhapur, Hi-Tech City. Our trainers are working ML and MLOps engineers who deploy AI models for real clients, so every class is based on current industry practice.

You can join classroom batches at our MLOps with AI training center in Hyderabad or attend live MLOps with AI online training in Hyderabad from anywhere. Both modes include the same live sessions, hands-on labs, class recordings and trainer support.

Classroom training

  • Weekday and weekend batches in Madhapur
  • Hands-on lab practice
  • Small batches with personal attention
  • Real-time project work

Online training

  • Live, interactive sessions with the trainer
  • Recordings of every class
  • Doubt-clearing sessions
  • Same projects as classroom batches

Whether you are starting out or moving up in your career, our MLOps with AI course in Hyderabad gives you practical skills, real projects and interview preparation. Book a free demo to meet the trainer before you join.

MLOps with AI curriculum

25 modules taught with live labs and real-time examples. Almost every module ends with a hands-on exercise, and the course finishes with an end-to-end MLOps project.

1 Introduction to AI, ML & MLOps
  • What is Artificial Intelligence?
  • Machine Learning fundamentals
  • Deep Learning overview
  • Generative AI overview
  • Traditional software development vs ML development
  • What is MLOps?
  • Why MLOps is required
  • ML lifecycle
  • Challenges in production ML
  • MLOps maturity levels
  • Roles and responsibilities in an ML team
  • Hands-on: Build a simple ML model and understand the complete ML lifecycle.
2 Python for AI/ML Engineering
  • Python fundamentals
  • Variables and data types
  • Conditions and loops
  • Functions
  • Lists, tuples, sets and dictionaries
  • File handling
  • Exception handling
  • Modules and packages
  • Virtual environments
  • pip, Jupyter Notebook
  • VS Code, Git basics
  • Hands-on: Create a Python-based ML project structure.
3 Data Engineering for ML
  • Structured and unstructured data
  • CSV, JSON and Parquet
  • Data collection
  • Data ingestion
  • Data validation
  • Data cleaning
  • Missing values
  • Duplicate records
  • Outlier detection
  • Feature transformation
  • Data splitting
  • Training/validation/test datasets
  • Data pipelines
  • Batch vs streaming data
  • Hands-on: Create a data-preparation pipeline for an ML project.
4 Machine Learning Fundamentals
  • Supervised learning
  • Unsupervised learning
  • Regression
  • Classification
  • Clustering
  • Feature engineering
  • Model training
  • Model validation
  • Model evaluation
  • Overfitting
  • Underfitting
  • Bias and variance
  • Hyperparameters
  • Hyperparameter tuning
  • Hands-on: Build a classification and regression model.
5 ML Model Development with Scikit-learn
  • Scikit-learn architecture
  • Preprocessing
  • Pipelines
  • Transformers
  • Estimators
  • Model persistence
  • Cross-validation
  • GridSearchCV
  • RandomizedSearchCV
  • Evaluation metrics
  • Confusion matrix
  • Precision, recall and F1-score
  • ROC-AUC
  • Regression metrics
  • Hands-on: Develop an end-to-end Scikit-learn ML project.
6 Experiment Tracking
  • Why experiment tracking is required
  • Tracking parameters
  • Tracking metrics
  • Tracking datasets
  • Tracking models
  • Experiment comparison
  • Model versioning
  • MLflow introduction, MLflow Tracking, MLflow Projects
  • MLflow Models
  • MLflow Model Registry
  • Hands-on: Track multiple ML experiments using MLflow.
7 Data & Model Versioning
  • Why versioning matters in ML
  • Git
  • GitHub
  • Git branching
  • Git tags
  • Dataset versioning
  • DVC introduction
  • DVC repositories
  • Data pipelines
  • Dataset versions
  • Model versions
  • Reproducibility
  • Hands-on: Version an ML dataset and model using Git + DVC.
8 ML Project Structure & Reproducibility
  • Production-ready ML project structure
  • Configuration management
  • Environment management
  • Dependency management
  • requirements.txt
  • pyproject.toml
  • Configuration files
  • Secrets management
  • Reproducible training
  • Logging
  • Testing ML code
  • Unit testing
  • Integration testing
  • Hands-on: Convert a notebook-based ML project into a production-style Python project.
9 Model Packaging
  • What is model packaging?
  • Serialization
  • Pickle
  • Joblib
  • Model artifacts
  • Dependency packaging
  • REST API concepts
  • FastAPI introduction
  • Creating prediction APIs
  • Request/response models
  • API validation
  • API testing
  • Hands-on: Package an ML model and expose it through a FastAPI endpoint.
10 Docker for ML
  • Containers and images
  • Docker architecture
  • Dockerfile
  • Docker images
  • Docker containers
  • Docker Hub
  • Environment variables
  • Volumes
  • Networks
  • Containerizing ML APIs
  • Containerizing ML inference services
  • Docker Compose
  • Hands-on: Dockerize the ML prediction API.
11 CI/CD for Machine Learning
  • What is CI/CD?
  • CI vs CD
  • ML CI/CD challenges
  • Automated testing
  • Automated model validation
  • Build pipelines
  • Deployment pipelines
  • GitHub Actions
  • Jenkins overview
  • Pipeline triggers
  • Secrets
  • Artifacts
  • Docker image build and push
  • Hands-on: Build a CI pipeline for an ML application using GitHub Actions.
12 Continuous Training (CT)
  • Why models need retraining
  • Training pipelines
  • Automated retraining
  • Data changes
  • Model performance degradation
  • Retraining triggers
  • Scheduled training
  • Event-based training
  • Model validation before deployment
  • Model approval process
  • Training → validation → registry → deployment
  • Hands-on: Create an automated model retraining workflow.
13 Model Deployment
  • Model deployment strategies
  • Batch prediction
  • Real-time prediction
  • Online inference
  • REST API deployment
  • Shadow deployment
  • Canary deployment
  • Blue-green deployment
  • A/B testing
  • Rollback strategies
  • Hands-on: Deploy an ML model as a production-style API.
14 Kubernetes for MLOps
  • Kubernetes fundamentals
  • Cluster architecture
  • Pods
  • Deployments
  • Services
  • ConfigMaps
  • Secrets
  • Namespaces
  • Ingress
  • Horizontal Pod Autoscaling
  • Container orchestration
  • ML inference workloads
  • Hands-on: Deploy the Dockerized ML API on Kubernetes.
15 Cloud MLOps
  • Cloud ML architecture
  • AWS ML ecosystem
  • Azure ML ecosystem
  • Cloud storage
  • Compute
  • Container registries
  • Kubernetes
  • Managed ML services
  • IAM and security
  • Model deployment
  • Cloud monitoring
  • Hands-on: Design a cloud-based MLOps architecture.
16 AWS MLOps
  • AWS S3
  • AWS ECR
  • AWS EC2
  • AWS IAM
  • Amazon SageMaker
  • SageMaker training jobs
  • SageMaker model registry
  • SageMaker endpoints
  • SageMaker pipelines
  • Model monitoring
  • CloudWatch
  • Hands-on: Build an AWS-based ML training and deployment pipeline.
17 Azure MLOps
  • Azure Storage
  • Azure Container Registry
  • Azure Machine Learning
  • Azure ML workspace
  • Compute instances
  • Compute clusters
  • Azure ML datasets
  • Azure ML models
  • Model registry
  • Endpoints
  • Azure ML pipelines
  • Azure DevOps integration
  • Application Insights
  • Hands-on: Create an Azure ML deployment pipeline.
18 Model Monitoring
  • Why model monitoring is required
  • Application monitoring
  • Infrastructure monitoring
  • Model performance monitoring
  • Data quality monitoring
  • Data drift
  • Concept drift
  • Prediction drift
  • Feature drift
  • Model accuracy monitoring
  • Latency monitoring
  • Error monitoring
  • Alerting
  • Hands-on: Monitor a deployed ML model and detect data drift.
19 ML Observability
  • What is ML observability?
  • Logs
  • Metrics
  • Traces
  • Model performance
  • Prediction monitoring
  • Feature monitoring
  • Data quality
  • Infrastructure health
  • OpenTelemetry concepts
  • Dashboards
  • Alerts
  • Hands-on: Build an ML monitoring dashboard.
20 Model Governance & Responsible AI
  • Model governance
  • Model lineage
  • Model documentation
  • Model approval
  • Auditability
  • Explainable AI
  • Bias detection
  • Fairness
  • Responsible AI
  • Security
  • Privacy
  • Compliance
  • Model risk management
  • Hands-on: Create a model card and governance checklist.
21 ML Security / DevSecMLOps
  • Security in ML pipelines
  • Secure source code
  • Secrets management
  • IAM
  • Dependency vulnerabilities
  • Container security
  • API security
  • Model security
  • Data security
  • Secure CI/CD
  • Vulnerability scanning
  • Supply-chain security
  • Hands-on: Add security checks to an ML CI/CD pipeline.
22 Generative AI MLOps / LLMOps
  • LLM lifecycle
  • LLM application architecture
  • Prompt management
  • Embeddings
  • Vector databases
  • RAG
  • Fine-tuning overview
  • LLM evaluation
  • Prompt evaluation
  • Response quality
  • Hallucination monitoring
  • Token/cost monitoring
  • LLM observability
  • Model/version management
  • Hands-on: Build and monitor a production-style RAG application.
23 ML Pipelines & Orchestration
  • What is an ML pipeline?
  • Pipeline stages
  • Data pipeline
  • Training pipeline
  • Validation pipeline
  • Deployment pipeline
  • Scheduling
  • Dependencies
  • Workflow orchestration
  • Apache Airflow
  • Kubeflow overview
  • Cloud-native ML pipelines
  • Hands-on: Build an automated ML workflow.
24 Advanced MLOps
  • Feature Stores
  • Feature engineering pipelines
  • Model registries
  • Distributed training
  • GPU workloads
  • AutoML
  • Hyperparameter optimization
  • Model ensembles
  • Edge ML deployment
  • Serverless inference
  • Batch inference at scale
  • Cost optimization
25 End-to-End MLOps Project
  • Project: Customer Churn Prediction Platform
  • Customer data, data validation and preprocessing
  • Feature engineering and ML training
  • Experiment tracking and model evaluation
  • Model registry and approval
  • Docker image and CI/CD pipeline
  • Deployment on Kubernetes / cloud as a REST API
  • Production predictions and monitoring
  • Drift detection and automatic retraining
  • Releasing a new model version

Career path after MLOps with AI

  1. Step 1

    ML Engineer / Junior MLOps Engineer

  2. Step 2

    MLOps Engineer

  3. Step 3

    Senior MLOps / ML Platform Engineer

  4. Step 4

    AI Platform Architect / Lead ML Engineer

Roles this course prepares you for

  • MLOps Engineer
  • Machine Learning Engineer
  • AI / LLMOps Engineer
  • ML Platform Engineer

These skills also prepare you for AI engineering and cloud architecture roles.

Frequently asked questions

Which is the best MLOps with AI training institute in Hyderabad?

RS Trainings in Madhapur, Hi-Tech City is a trusted MLOps with AI training institute in Hyderabad. Classes are taught live by working professionals, with real-time projects, recordings and placement support.

What is the duration of the MLOps with AI course in Hyderabad?

The MLOps with AI course in Hyderabad usually takes about 2 to 3 months, depending on weekday or weekend batches. Contact us for the current batch schedule.

Do you offer MLOps with AI online training in Hyderabad?

Yes. Our MLOps with AI online training in Hyderabad uses live, interactive sessions with the same trainer, curriculum and projects as classroom batches, plus recordings of every class.

Where is your MLOps with AI training center in Hyderabad?

Our MLOps with AI training center in Hyderabad is at #402, Nukala Residency, Madhapur, Hi-Tech City, Hyderabad 500081.

Do I need DevOps experience to learn MLOps?

No. We cover Docker, Kubernetes and CI/CD from the basics. Basic Python is enough to start.

Does the course cover LLMOps and generative AI?

Yes. A full module covers running LLM and RAG applications in production.

Will I work on real-time projects?

Yes. Most modules include a hands-on lab, and the course ends with an end-to-end project: a Customer Churn Prediction Platform covering data validation, training, experiment tracking, model registry, Docker, CI/CD, Kubernetes or cloud deployment, monitoring, drift detection and automatic retraining.

Do you provide placement assistance?

Yes. We help with resume preparation, mock interviews, interview questions and job referrals.

Can I attend a free demo?

Yes. Book a free demo session to meet the trainer and see how classes work before you enroll.

What if I miss a class?

You get the recording of every session, and you can also attend the same topic in another live batch.

Free demo class

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