Intership Assured - MLOps Masterclass (Build, Test, Deploy, Monitor)
- Course Duration Weekdays (8 Weeks) /Weekends (16 Weeks)
- Course Mode Instructor Led Online Training
- Date & Time 05-May-2025
About The Course
This MLOps Cohort Program is a comprehensive, hands-on training designed to bridge the gap between machine learning development and real-world deployment. Spanning 70 hours of interactive learning, the program takes you from the basics of Python, Data Science, and Machine Learning to mastering modern MLOps workflows — covering everything from model building and packaging to deployment, automation, monitoring, and scaling in production environments.
The course blends theory with extensive hands-on projects, teaching you how to manage ML pipelines, build APIs, use Docker containers, automate CI/CD workflows with GitHub Actions, and implement continuous monitoring using Prometheus and Grafana. You’ll learn to track experiments, manage model versions with MLflow, and securely deploy ML applications using FastAPI, Flask, and Streamlit on cloud platforms like AWS.
This program is built for aspiring data scientists, ML engineers, and software developers who want to turn their machine learning models into scalable, production-grade solutions. By the end of this course, you\'ll be equipped with the skills and confidence to handle complete ML project lifecycles, from prototyping to production and post-deployment monitoring — a must-have skill set in today’s AI-driven industry.
Key Features
Instructor-Led, Interactive Training
Live, expert-led sessions for hands-on learningLifetime Access to Recordings
Revisit recorded classes anytime, at your convenienceAssignments & Real-Time Projects
Apply skills through practical projects after every moduleLifetime Job Assistance
Ongoing support for AI and Data Science job opportunities3 Years of Technical Support
24/7 query resolution and technical help for 3 yearsGlobally Recognized Certification
Certified by AiCouncil, backed by Microsoft & AWSHighlights
Foundational Python programming with essential libraries including NumPy, Pandas, and Matplotlib, tailored for data science and MLOps applications.
Comprehensive Exploratory Data Analysis (EDA) covering data cleaning, handling missing values, scaling, outlier detection, and correlation analysis.
Machine Learning fundamentals with hands-on projects in supervised and unsupervised learning, covering algorithms like Linear Regression, Decision Trees, KNN, K-Means, and PCA.
Model evaluation and optimization techniques including cross-validation, performance metrics, hyperparameter tuning with GridSearchCV and RandomizedSearchCV.
Introduction to MLOps principles — covering CI/CD, model packaging, version control with Git, and the full lifecycle of ML systems.
End-to-end machine learning pipeline development using Python modules, scikit-learn pipelines, modular programming practices, and code testing with Pytest.
Experiment tracking and model management with MLflow, including logging metrics, managing model versions, and deploying models locally and remotely.
Model deployment using FastAPI, Flask, and Streamlit, creating RESTful APIs and interactive web applications for real-time predictions.
Containerization of machine learning projects using Docker and Docker Compose, ensuring consistent and portable deployments across environments.
Automated CI/CD pipelines using GitHub Actions integrated with AWS EC2 for seamless, production-ready ML model deployment.
Monitoring ML models in production with Prometheus and Grafana dashboards, including infrastructure health checks, model drift detection, and alert systems.
Production security best practices covering adversarial attacks, data poisoning risks, and A/B testing for model performance validation.
Industry-focused capstone project where learners design, build, deploy, and monitor a complete MLOps pipeline on a real-world dataset.
Real-world case studies and live demonstrations, providing practical exposure to current tools, cloud environments, and deployment pipelines used by industry experts.
Course Agenda
Objective: Build Python basics with core libraries for data science.
- Topics:
- Python basics: Variables, Data Types, Operators, Control Flow
- Functions, Classes, and Modules
- Jupyter Notebooks for interactive coding
- NumPy: Arrays, operations
- Pandas: Series, DataFrames, manipulation
- Matplotlib: Basic plotting
- Exercises with NumPy, Pandas
- Visualizations with Matplotlib
- Notebook-based data exploration
Objective: Understand core data science concepts, EDA, preprocessing.
- Topics:
- What is Data Science?
- Types of Data: Structured, Unstructured, Semi-structured
- Data Preprocessing: Missing Data, Normalization, Scaling
- Exploratory Data Analysis (EDA) with Pandas, Matplotlib, Seaborn
- Titanic Dataset EDA
- Data Cleaning, Missing value handling
- Data visualization and insights
Objective: Understand machine learning fundamentals and evaluation.
- Topics:
- What is Machine Learning?
- ML Types: Supervised, Unsupervised, Reinforcement
- Regression, Classification, Clustering
- Evaluation Metrics: Accuracy, Precision, Recall, F1-Score
- Linear Regression
- K-Nearest Neighbors (KNN)
- Model evaluation with appropriate metrics
Objective: Learn common ML algorithms with real-world applications.
- Topics:
- Supervised: Linear Regression, Logistic Regression, Decision Trees, Random Forests, SVM, KNN
- Unsupervised: K-Means, Hierarchical Clustering, PCA, t-SNE
- Regression on house price data
- Classification for customer churn
- K-Means clustering
- Dimensionality reduction with PCA
Objective: Apply proper model evaluation and optimization techniques.
- Topics:
- Cross-validation, Train-Test Split
- GridSearchCV, RandomizedSearchCV
- Overfitting, Underfitting
- Cross-validation and hyperparameter tuning
- Avoid overfitting with regularization
Objective: Learn MLOps basics and integrate GIT for ML projects.
- Topics:
- What is MLOps, why it matters
- MLOps vs Traditional software engineering
- CI/CD/CM concepts in ML workflows
- GIT basics: clone, commit, push, pull, branch, merge
- Managing ML projects using GIT
- Create a GitHub repo
- Track project changes
- Explore an MLOps case study
Objective: Build modular, testable, deployable ML projects.
- Topics:
- Modular Programming concepts
- Python package management (requirements.txt, virtualenv)
- Folder hierarchy for ML projects
- Data Handling & Preprocessing Module
- sklearn pipelines: Training & Prediction
- Pytest introduction and fixtures
- ML package building (Manifest, Version file, setup.py)
- Build training & prediction pipelines
- Package ML code
- Write and run tests using Pytest
Objective: Manage ML experiments, track metrics, deploy models.
- Topics:
- MLflow tracking & logging
- MLflow projects
- MLflow models & serving
- Setting up local MySQL for model logging
- Register, log, and serve models
- Track model runs with MLflow
- Log metrics to MySQL
- Serve models via MLflow
Objective: Deploy models as APIs and interactive apps.
- Topics:
- API, REST, REST API concepts
- FastAPI crash course + Pydantic
- Flask intro + app building
- Streamlit intro + app building
- Deploy ML model with FastAPI
- Build interactive ML apps with Streamlit
- Flask-based deployment demo
Objective: Containerize ML apps for consistent deployment.
- Topics:
- Docker installation & basics
- Dockerfile creation
- Docker Hub basics
- Docker Compose intro and setup
- Deploy multiple services (FastAPI + Prometheus + Grafana)
- Containerize ML APIs
- Push to DockerHub
- Docker Compose for multi-container app
Objective: Automate ML workflows using CI/CD pipelines.
- Topics:
- GitHub Actions: YAML workflow files
- CI/CD pipelines for ML projects
- AWS EC2 integration for model deployment
- Testing CI/CD pipelines
- Configure and run CI/CD pipelines
- Test ML projects deployment on AWS
Objective: Monitor ML models in production and mitigate risks.
- Topics:
- Importance of monitoring
- Tools: Prometheus, Grafana
- Monitoring architecture
- Model Drift & A/B testing
- Security: Adversarial attacks, DDoS, Data poisoning
- Future of MLOps
- Monitor infrastructure and apps with Prometheus & Grafana
- Trigger alerts
- Simulate drift detection
- Deploy dashboards and alerts
Projects
These projects align with modules covering Python, Git, Docker, and APIs.
- Skills Covered: Python scripting, API creation, packaging ML models, Git basics.
- Objective: Package a simple machine learning model (e.g. Iris classifier) into a Flask API and deploy it locally.
- Tech Stack: Python, Flask, Pickle/Joblib, Git.
- Skills Covered: Docker basics, containerization, image building, running applications in containers.
- Objective: Containerize the Flask ML API from the previous project, build Docker images, and run the containerized app locally.
- Tech Stack: Docker, Python, Flask, Pickle/Joblib.
These projects correspond to modules covering CI/CD with GitHub Actions, MLflow, Model Versioning, and Cloud Basics.
- Skills Covered: Experiment tracking, model versioning, logging metrics, artifact storage.
- Objective: Track multiple ML model runs, compare performance, and manage model versions using MLflow locally.
- Tech Stack: Python, Scikit-learn, MLflow.
- Skills Covered: Continuous Integration, Continuous Deployment, workflow automation.
- Objective: Set up a GitHub repository for an ML project and automate code testing, Docker builds, and deployments using GitHub Actions.
- Tech Stack: Git, GitHub Actions, Docker, Python.
These projects fit within Production Deployment, Cloud Integration, and Monitoring modules.
- Skills Covered: Cloud basics, VM setup, cloud deployment, firewall configurations.
- Objective: Deploy a containerized ML API (built with FastAPI/Flask) to an AWS EC2 instance and make it publicly accessible.
- Tech Stack: AWS EC2, Docker, FastAPI/Flask, Python.
- Skills Covered: Service monitoring, metrics collection, dashboard creation, real-time alerting.
- Objective: Set up Prometheus to collect metrics from the ML API, and visualize service performance in Grafana dashboards.
- Tech Stack: Prometheus, Grafana, FastAPI/Flask, Docker.
This final project integrates EDA, ML model building, versioning, deployment, monitoring, and automation.
- Skills Covered: Data cleaning, ML model development, API deployment, Dockerization, CI/CD automation, model versioning, monitoring.
- Objective:
- Build a churn prediction model
- Track experiments with MLflow
- Create and containerize a FastAPI endpoint
- Deploy on AWS EC2
- Set up GitHub Actions for CI/CD
- Monitor with Prometheus & Grafana
- Tech Stack: Python (Scikit-learn, Pandas), MLflow, FastAPI, Docker, GitHub Actions, AWS, Prometheus, Grafana.
Certification
Career Support
We have a dedicated team which is taking care of our learners learning objectives.