Architectures - Cloud Native tools - AWS, GCP and Azure
The cost-benefit approach of each architecture and MLOps maturity
List of tools involved in each stage (MLOps tool ecosystem
Different Roles involved in MLOps ( ML Engineering + Operations )
Overview of Git
Understanding branching strategies and REPO
Standard GIT branching strategies (development, feature, bug, release, UAT)
Practising important Git commands
GitHub Action overview and working
GitHub Remote Repository
Project: Mastering Git: Commands, Branching, and Collaboration
Introduction to CI and CD
CI/CD in Machine Learning operations
Steps involved in the CI/CD implementation in ML lifecycle and workflow
A glimpse of popular Tools used in the DevOps ecosystem on the Cloud.
AWS DevOps
AWS CodePipeline
AWS CodeBuild
AWS CodeDeploy
AWS CodeCommit
Project: AWS DevOps Pipeline
GCP DevOps
Cloud Run
Cloud Build
Cloud Deploy
Artifacts Registry
Cloud Source Repositories
Project: GCP DevOps Pipeline
Azure DevOps
Azure Boards
Azure Repos
Azure Pipeline
Azure Test Plans
Azure Artifacts
Infrastructure as code (IaC) with Azure DevOps
YAML Pipeline Structure
Project: Azure DevOps Pipeline
GitHub Actions
Introduction to GitHub Actions
GitHub Actions YAML pipeline structure
GitHub Action automation & Custom Workflows
GitHub Pages
Project: GitHub Actions DevOps Pipeline
Docker Foundation
Installing Docker on Windows, macOS & Linux
Managing Containers with Docker Commands
How does it work? Docker registry - Docker Hub
Building your own Docker images
Docker Network Types
Docker Volumes
Docker Compose
Docker Swarm
Project: Deploy a Node.js app in a Docker container
Project: Deploy an ML model in a Docker container
Project: Deploy a complete end-to-end ML model with Docker Compose
Kubernetes Overview
Kubernetes Architecture
Worker Nodes
Control Plane
Virtual Network
API Server
Command line tool - kubectl
Kubernetes Resources
Pod
ConfigMap
Service
Secret
Ingress
Deployment
StatefulSet
DaemonSet
Volumes (PVC)
Minikube
Project: Deploy ML model in Kubernetes cluster
Kubernetes Deployment Strategy types
Monitoring
Liveness and Readiness Probes
Labels and Selectors
Amazon Elastic Kubernetes Service (EKS)
Project: Deploy a Kubernetes infrastructure on Amazon EKS and deploy an ML model on EKS.
What is a Model Management
What are the various activities in Model Management
Data Versioning
Code Versioning
Experiment Tracker
Model Registry
Model Monitoring
A high-level overview of the below Model Management tools
MLFlow
Project: Deploy MLFlow stack on the cloud
Project: Build, train, and deploy an ML model using MLFlow Experiments and MLFlow model registry
Data version control (DVC)
Versioning of data and models
DVC with Git workflows
Data source for DVC
Project: Version data stored in cloud storage services.
Git Large File Storage (LFS)
Introduction to Feature Stores, SageMaker Feature Store, Vertex AI Feature Store, Databricks, Tecton, Feast, Hopsworks etc.
Feast open-source feature store
Feature Store: Online Vs Offline
Project: Deploy Feast Online/Offline feature store
Online & offline feature store options
Feast Feature Store on Cloud
Monitor features programmatically
Visualizing feature drift over time
AWS SageMaker
Introduction to Amazon SageMaker
Using Amazon S3 along with SageMaker
Amazon SageMaker Notebooks
Notebook instance type, IAM Role & VPC
Build, Train & deploy ML Model using SageMaker
Endpoint & Endpoint configurations
Generate inference from deployed model
AWS SageMaker Pipelines
SageMaker Studio & SageMaker domain
SageMaker Projects
Repositories
Pipelines & Graphs
Experiments
Model groups
Endpoints
Project: Deploy an end-to-end MLOps pipeline using SageMaker Studio
GCP VertexAI
Introduction of Vertex AI
Gather, Import & label datasets
Build, Train & deploy ML Solutions
Manage your models with confidence
Using Pipelines throughout your ML workflow
Adapting to changes in data
Creating models with Vertex AI and deploying ML models using aiplatform pipelines
Project: Deploy an end-to-end MLOps pipeline using Vertex AI
Azure MLOps
Azure Machine Learning Studio
Azure MLOps
Azure ML components
Azure MLOps + DevOps
Fully automated end-to-end CI/CD ML pipelines
Project: Deploy an end-to-end MLOps V2 pipeline using Azure Machine Learning
Kubeflow Introduction
Kubeflow- Who uses it
Kubeflow features
Kubeflow Components
Kubeflow Fairing
Kubeflow Pipelines
Kubeflow use cases
Project: Deploy a Kubeflow stack and create end-to-end ML pipelines on it.
What is LLM?
MLOps for LLM’s
FMOps/LLMOps: Operationalize generative AI
LLM System Design
High-level view LLM-driven application
LLMOps Pipeline
Importance Of Model Monitoring
What are the various types of monitoring related to the model
The architecture of monitoring ecosystem in AWS/Azure/GCP
AWS Model Monitoring
Azure Model Monitoring
GCP Model Monitoring
Optimize and Manage Models at the Edge
Common Issues in ML Model Deployment
Feedback Loop Role
Project: Model & infrastructure monitoring using cloud tools
H20 MLOps
Valohai
Domino Data Lab
neptune.ai
iguazio
W&B
Post Deployment Challenges intro
Post Deployment Challenges - ML Related
Challenges when deploying machine learning to edge devices
What is Evidently?
Post Deployment - Monitoring the Drift - Evidently
Post Deployment Challenges - Software Engineering Related
Common Issues in ML Model Deployment
Project: Evidently AI for Monitoring the Data Quality and Drift
Jenkins
Understanding Jenkins CI/CD
Jenkins Plugins
Pipeline as Code
Distributed Builds
Understanding Jenkins User Interface
Integration with SCM Tools
Monitoring and Reporting
Jenkins Common Use Cases
Project: Building a Python Application with Jenkins Pipelines
Project: Build end-to-end ML pipelines using Jenkins, Docker containers, and MLflow
Apache Airflow
What is Apache Airflow?
Airflow workflow
Airflow use cases
Airflow benefits
Apache Airflow Fundamentals
Directed Acyclic Graph (DAG)
DAG run
Airflow Tasks
Airflow Operators
Airflow Hooks
Project: Building Scheduled ETL data Pipelines with Apache Airflow
Project: Building Scheduled End-to-End ML Pipelines with Apache Airflow
Google Kubernetes Engine (GKE)
Introduction to GKE
Benefits of using GKE
How does GKE work?
Limitations of using GKE
Project: Deploy a Kubernetes infrastructure on GKE and deploy an ML model
Azure Kubernetes Service (AKS)
What is AKS?
Overview of AKS
When to use AKS?
Features of AKS
Project: Deploy a Kubernetes infrastructure on AKS and deploy an ML model
Terraform
What is Terraform?
How does Terraform work?
Infrastructure as code (IaC) using Terraform
Terraform stages
Why Terraform?
Terraform Registry
Terraform configuration
Project: Build, Modify, and Destroy Docker Infrastructure for ML Model Deployment with Terraform
Project: Build, change, and destroy AWS cloud infrastructure using Terraform
Argo Workflows
Intro Argo Workflows
CI/CD with Argo Workflows on Kubernetes
Argo Architecture
Argo Workflows CRDs (Custom Resource Definitions)
Argo Templates
Argo Workflows UI
Project: Building End-to-End Scheduled ML Pipelines with Argo Workflows and Kubernetes
Tools Covered
Watch Demo
Have a question?
Send your queries to the program trainer
Meet Our POWERFUL
Trainer
Sarath Kumar
Data Scientist | Founder - Psitron Technologies | Educator & Mentor
Enabler of Applied Analytics, Data Science, Artificial Intelligence (AI), Deep Learning, Generative AI (Gen AI), and MLOps for solving real-world business challenges.
Experienced manager, mentor, and trainer with a strong focus on problem-solving and project delivery. Proactive contributor with excellent communication and interpersonal skills, adept at managing task forces and driving results. Passionate about leveraging AI technologies to empower businesses and individuals.
About Program
Hottest Job of the 21st Century
The global AI market is projected to surge from $214.6 billion in 2024 to $1.34 trillion by 2030, with a remarkable 35.7% CAGR. Driven by advancements in computational power, data availability, and government investments, AI is transforming industries like healthcare, finance, and retail, propelling businesses toward innovative, efficiency-boosting solutions.
Who Can Apply for this Course?
✓ Data Scientists
✓ Data engineers & Data Analysts
✓ Research/Applied Scientists
✓ ML engineers
✓ DevOps engineers
✓ Aspiring MLOps Professionals and Enthusiasts
✓ Machine Learning professionals who want to deploy models to production
✓ Anyone who wants to learn Docker & Kubernetes, AWS, Azure, GCP, DVC, Feast, MLFlow etc
✓ Software Engineers
✓ Individuals interested in data and AI industry
MLOps:The Future of Machine Learning
Looking to start hands-on Machine Learning Operations (MLOps) with a real-time project? As of 2025, MLOps is an essential skill for enterprise ML projects. Despite widespread adoption, only 15% of ML projects succeed in production, highlighting MLOps' role in bridging the gap between development and deployment.
The MLOps market was valued at USD 720 million in 2022 and is projected to reach USD 13.32 billion by 2030, with a CAGR of 43.5%. This rapid growth underscores the demand for MLOps expertise across industries. Mastering MLOps unlocks professional opportunities and enables organizations to turn ML initiatives into real business value.
If you are looking to get started with MLOps, you have come to the right place. Our hands-on course will teach you the skills you need to build and deploy ML models in production.
100% Hands-on - Mark Your Calendar starting from 22nd February 2025!
Why Cloud ?
Skills to Master
DevOps
Git
GitHub
AWS CodeCommit
AWS CodePipeline
AWS CodeBuild
AWS CodeDeploy
Cloud Source Repositories
Cloud Run
Cloud Build
Cloud Deploy
Argo Workflows
Terraform
Azure Boards
Azure Repos
LLMOps
FMOps
Docker
Azure Pipeline
Azure Test Plans
Azure Artifacts
Kubernetes
EKS,AKS,GKE
MLFlow
DVC
Git LFS
AWS SageMaker
Vertex AI
GCP Cloud Build
GCP Cloud Run
Feast
SageMaker Pipelines
Azure Machine learning studio
Kubeflow
Evidently
Apache Airflow
Job opportunities
MLOps is an essential practice that helps organizations manage their machine-learning projects more effectively. It combines machine learning with DevOps practices to create end-to-end pipelines for your models. MLOps is becoming increasingly important as more and more businesses are adopting machine learning to gain a competitive advantage.
The average salary for an MLOps engineer starts from ₹35.1 Lakhs according to 6figr.com.
Source:According to a Talent.com & figr.com report, the annual salary
100% Hands-on - Mark Your Calendar starting from 22nd February 2025!
Glimpse of our happy participants
Testimonials
Harsha Vardhan Reddy- MLOps engineer says I received 3 different offers for MLOps in just 3 weeks with more than 40% hike.
Nisheeth Jaiswal- Data Scientist from UK
Balasubramanian- what ISRO engineer has to say!
Dipali Matkar - MLOps Engineer- Congratulations on your new job 👏 well deserved!
Sitaram - MLOps Engineer- Congratulations on your new job 👏 well deserved!
Rahul Patil - Congratulations on your new job 👏 well deserved!
Dhirendra Kumar Singh- Participants - MLOps
Participants Feedback
100% Hands-on - Mark Your Calendar starting from 22nd February 2025!
Who is Psitron Technologies? Why should you care?
Psitron Technologies is an IoT and AI company. Our mission at Psitron is to solve global problems with innovative technologies. Psitron has responsibility for developing innovative innovations for addressing current problems in industries, especially in order to improve uptime and reduce downtime of industries and to provide affordable industry 4.0 solutions to increase productivity in industries.
Supported By
Department of science & technology
Incubated in PSG-STEP
NIDHI Entrepreneur-in-Residence
Awards /Honours
FKCCI Bangalore best Startup award 2018
Living Talent, Dubai finalist of international level innovation competition 2018
HILTI International innovation competition finelist.
