MLOps, or Machine Learning Operations, is the engineering discipline that connects data science with real-world production systems. This MLOps course by kodestree gives you a structured, end-to-end understanding of how organizations build, deploy, and maintain machine learning models at scale.
Whether you are stepping into MLOps for the first time or looking to formalize skills you have already been applying on the job, this program meets you where you are. In this MLOps online certification program, you will move through core concepts, hands-on tool work, and live project experience- everything needed to operate confidently in production ML environments where reliability, speed, and scalability are non-negotiable.
By the time you complete this online MLOps certification program, you will not just understand the theory. You will have built actual pipelines, resolved real deployment challenges, and earned a credential that validates your readiness for production ML roles.
MLOps Students Also Learn
Prerequisite
- Basic knowledge of Python programming
- Familiarity with core machine learning concepts (training, testing, evaluation)
- Basic understanding of the command line / Linux environment
- Exposure to any cloud platform – AWS, Azure, or GCP (preferred, not mandatory)
- Basic knowledge of Git and version control
- No prior MLOps experience required
Course Objectives
- Understand core MLOps principles and lifecycle.
- Build automated ML pipelines.
- Implement model versioning and experiment tracking.
- Apply CI/CD for ML workflows.
- Deploy models using Docker, Kubernetes, and cloud platforms.
- Monitor models and detect model drift.
- Build scalable and production-ready ML solutions.
- Apply MLOps practices for reliable and reproducible ML systems.
What You’ll Learn
Below are the topics you will cover in this MLOPs training program.
- Build a strong foundation in Python, core MLOps principles, and data management to develop and deploy machine learning models confidently in real production environments.
- Work hands-on with industry-leading platforms, including Amazon SageMaker, AWS, Microsoft Azure, MLflow, and Hugging Face to build end-to-end ML solutions, create automated pipelines, and develop production-ready APIs.
- Fine-tune and deploy Large Language Models (LLMs) using Hugging Face and package containerized models in the ONNX format for efficient, cross-platform deployment.
- Design and implement a complete MLOps pipeline using MLflow- covering project structuring, model versioning, registry management, and experiment tracking from start to finish.
15+ Essential Skills Covered In This Program
The following are the essential skills you will learn during this MLOps training program.
- Machine Learning Lifecycle Management
- CI/CD for Machine Learning
- Model Versioning
- Experiment Tracking
- Feature Engineering & Feature Stores
- Data Pipeline Automation
- Model Deployment (Batch, Real-time, Streaming)
- Model Monitoring & Drift Detection
- Containerization (Docker)
- Container Orchestration (Kubernetes)
- Workflow Orchestration (Airflow)
- Cloud ML Platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
- Model Governance & Reproducibility
- A/B Testing & Canary Deployments
- Python for MLOps
15+ Tools Covered in This Program
This MLOps online course gives you hands-on experience with the specific MLOps tools that appear in job descriptions for MLOps engineers in 2026:
- Version Control & Experiment Tracking: Git, DVC, MLflow
- Containerization & Orchestration: Docker, Kubernetes, Kubeflow
- CI/CD Automation: Jenkins, GitLab CI
- Cloud Platforms: Azure Machine Learning, Amazon SageMaker, Google Cloud Vertex AI
- Monitoring & Observability: Grafana, Prometheus
- ML Frameworks: TensorFlow, Keras, Scikit-learn, PyTorch
- Programming & Scripting: Python, Linux/Bash
- Emerging Stack: Kubeflow, Hugging Face for production LLM pipelines
- Feature Stores: Feast, Tecton
- LLMOps Stack: LangChain, ONNX, Vector Databases
- Governance & Explainability: SHAP, Model Cards, Policy-as-Code
MLOps Projects You Will Work On
This MLOps online course is built around real-time practical projects that take you beyond theory and isolated model experiments. Also, you will get an opportunity to work on realistic machine learning workflows where the focus is on automation, deployment, monitoring, scalability, and keeping models reliable in production.
- End-to-End ML Pipeline Project: Build a complete machine learning pipeline covering data preparation, feature engineering, model training, experiment tracking, model registration, deployment, and monitoring.
- MLflow Model Lifecycle Project: Use MLflow to track experiments, compare model runs, manage versions, register production models, and create a repeatable model lifecycle.
- CI/CD Pipeline for Machine Learning: Create an automated CI/CD workflow that validates code and data, trains models, runs tests, packages artifacts, and deploys approved models to a target environment.
- Containerized Model Deployment: Package a trained machine learning model with Docker and expose it through a production-ready API for real-time predictions.
- Kubernetes-Based ML Deployment: Deploy and manage containerized ML workloads using Kubernetes, with a focus on scalability, service management, and reliable model serving.
Career Outcomes
The following are the high-paying roles you can choose after completing the MLOps training.
- MLOps Engineer
- Machine Learning Engineer
- AI/ML Platform Engineer
- ML Infrastructure Engineer
- AI/ML DevOps Engineer
- ML Reliability Engineer
- LLMOps Engineer (emerging and high-demand in 2026)
Top Companies Actively Hiring MLOps Professionals
- Global Tech Giants: Google, Microsoft, Amazon, Meta, Tesla
- Cloud & Data Platforms: Databricks, Snowflake, Salesforce, Oracle
- AI Infrastructure & Tools: NVIDIA, Hugging Face, Weights & Biases, DataRobot
- Enterprises & Fintech: JPMorgan Chase, Capital One, PayPal, Stripe
MLOps Engineer Salary
The demand for MLOps professionals has significantly outpaced the supply of qualified talent.
| Experience Level | India (Annual Salary) | USA (Annual Salary) |
| Entry-Level (0-2 Years) | ₹8 – ₹15 LPA | $82,000 – $110,000 |
| Mid-Level (2-5 Years) | ₹15 – ₹30 LPA | $110,000 – $150,000 |
| Senior-Level (5-8 Years) | ₹30 – ₹50 LPA | $150,000 – $180,000 |
| Lead/Principal (8+ Years) | ₹50 – ₹80+ LPA | $180,000 – $250,000+ |
Why Choose kodestree To Learn MLOps?
- Experienced Trainers: Learn directly from industry veterans with hands-on experience at top organizations like Microsoft, IBM, and Google. Our trainers do not just teach concepts- they bring real production challenges and solutions into every session.
- Alumni Working at Top MNCs: Our graduates are employed at leading multinational companies across the US, UK, and India. When you join kodestree, you join a network of professionals who have already made the transition successfully.
- Always Up-to-Date Curriculum: The MLOps landscape is constantly evolving. Our course outline is continuously updated to reflect the latest tools, frameworks, and industry practices- so what you learn today is what employers are looking for right now.
- End-to-End Career Support: From resume building and LinkedIn optimization to interview preparation, including answers to the most commonly asked MLOps interview questions, and placement assistance, kodestree supports you well beyond the last session. The goal is not just certification- it is your next job offer.
- Lifetime Access to Learning Material: All session recordings, course materials, and lab resources remain accessible to you indefinitely. Revisit any module, refresh your knowledge before an interview, or explore a tool again whenever you need to.
Sujit Nair
MLOps Trainer
Sujit is a seasoned MLOps Trainer specializing in ML pipeline automation, model deployment, CI/CD for machine learning, cloud-native ML infrastructure, and production monitoring. His training approach centers on hands-on labs, real-world deployment pipelines, and industry case studies, helping learners transition from “notebook to production” with confidence.
- MLOps and ML Engineering professional with 15+ years of experience across data science, DevOps, and cloud infrastructure.
- Successfully trained and mentored 1000+ learners and working professionals across MLOps, ML engineering, and AI infrastructure domains.
- Expertise in CI/CD for ML, Model Versioning, Experiment Tracking, Feature Stores, Model Monitoring, Data Drift Detection, and end-to-end ML Pipeline Orchestration.
- Hands-on experience with leading MLOps tools, including MLflow, Kubeflow, DVC, Airflow, Docker, Kubernetes, Jenkins, TensorFlow Extended (TFX), and Prometheus/Grafana.
- Strong knowledge of deployment strategies, including Canary, Shadow, and Blue-Green deployments, along with model governance and reproducibility best practices.
- Specialized in building and scaling ML pipelines across AWS (SageMaker), Microsoft Azure (Azure ML), and Google Cloud Platform (Vertex AI).
- Helps learners prepare for industry-recognized MLOps certifications and real-world roles such as MLOps Engineer, ML Platform Engineer, and Applied ML Engineer.
