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Assignment - 1 [ Total Marks - 2 5 ] M 1 : MLOps Foundations Objective: Understand the basics of MLOps and implement a simple

Assignment-1[Total Marks -25]
M1: MLOps Foundations
Objective: Understand the basics of MLOps and implement a simple CI/CD
pipeline.
Tasks:
1. Set Up a CI/CD Pipeline:
Use a CI/CD tool like GitHub Actions or GitLab CI to set up a pipeline for a
sample machine learning project.
Include stages for linting, testing, and deploying a simple machine
learning model.
2. Version Control:
Implement version control for your project using Git.
Demonstrate branching, merging, and pull requests.
Deliverables:
A report detailing the CI/CD pipeline stages.
Screenshots or logs showing successful runs of the pipeline.
A Git repository link with branches and merge history.
M2: Process and Tooling
Objective: Gain hands-on experience with popular MLOps tools and
understand the processes they support.
Tasks:
1. Experiment Tracking:
Use MLflow to track experiments for a machine learning project.
Record metrics, parameters, and results of at least three different model
training runs.
2. Data Versioning:
Use DVC (Data Version Control) to version control a dataset used in your
project.
Show how to revert to a previous version of the dataset.
Deliverables:
MLflow experiment logs with different runs and their results.
A DVC repository showing different versions of the dataset.
M3: Model Experimentation and Packaging
Objective: Train a machine learning model, perform hyperparameter tuning,
and package the model for deployment.
Tasks:
1. Hyperparameter Tuning:
Use a library like Optuna or Scikit-learns GridSearchCV to perform
hyperparameter tuning on a chosen model.
Document the tuning process and the best parameters found.
2. Model Packaging:
Package the best-performing model using tools like Docker and Flask.
Create a Dockerfile and a simple Flask application to serve the model.
Deliverables:
A report on hyperparameter tuning results.
A Dockerfile and Flask application code.
Screenshots of the model running in a Docker container.
M4: Model Deployment & Orchestration (Optional)
Objective: Deploy a machine learning model and orchestrate its operations
using Kubernetes.
Tasks:
1. Model Deployment:
Deploy the Dockerized model from M3 to a cloud platform like AWS,
Azure, or GCP.
Use a platform service like AWS ECS, Azure AKS, or Google Kubernetes
Engine (GKE).
2. Orchestration:
Set up a Kubernetes cluster.
Deploy the model using Kubernetes and create a Helm chart for
managing deployments.
Deliverables:
A link to the deployed model endpoint.
Kubernetes configuration files and Helm chart.
A report detailing the deployment and orchestration process.
M5: Final Deliverables
A zip file containing:
Code
Data
Model
A one-page summary that includes:
Description of the work completed
Justification for the choices made
A screen recording (maximum 5 minutes) that:
Explains the work done
Shows the results

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