MLOps Basics: A Complete Guide to Building, Deploying and Monitoring Machine Learning Models

Machine Learning models are powerful but building them is only half the story. The true challenge lies in deploying, scaling and maintaining these models in production environments – a process that requires collaboration between data scientists, developers and operations teams.

This is where MLOps (Machine Learning Operations) comes in. MLOps combines the principles of DevOps with machine learning workflows to automate and streamline the process from data collection to model deployment and monitoring.

MLOps Basics: A Complete Guide to Building, Deploying and Monitoring Machine Learning Models

The open-source project “MLOps-Basics” by graviraja provides an excellent, hands-on approach to learning the core concepts of MLOps. Through a 10-week structured series, it covers every essential aspect from project setup and data versioning to CI/CD, serverless deployment and prediction monitoring.

If you’re looking to understand how to take a model from experimentation to production, this guide will walk you through the key lessons from the MLOps-Basics project.

Explore Github here

Week 0: Project Setup

The journey starts with setting up a simple classification problem using the HuggingFace ecosystem. The project demonstrates how to collect and process data, define dataloaders, declare models and train them efficiently using PyTorch Lightning.

Key technologies:

HuggingFace Datasets
HuggingFace Transformers
PyTorch Lightning

This stage ensures that your machine learning model is reproducible, modular and ready for experimentation laying the foundation for the rest of the MLOps pipeline.

Week 1: Model Monitoring with Weights & Biases

Once the model is built, tracking experiments becomes crucial. The project integrates Weights & Biases (W&B) for real-time tracking of metrics, hyperparameters and performance comparisons.

Through W&B dashboards, developers can visualize model performance, plot validation losses and log datasets helping them identify overfitting, data drift or training instability early.

Key takeaways:

Log metrics and losses with W&B
Track hyperparameter experiments
Visualize results and data samples
Monitor model improvements over time

Tech stack: Weights & Biases, TorchMetrics

Week 2: Configuration Management with Hydra

Managing configurations across multiple environments is a common challenge. Hydra helps organize and override configuration files easily.

In MLOps-Basics, Hydra is used to manage training parameters, data paths and model settings making it simple to run experiments with different configurations using a single command.

Topics covered:

Basics of Hydra
Splitting and overriding configurations
Variable interpolation
Running parameter combinations automatically

Tech stack: Hydra

Week 3: Data Version Control (DVC)

Traditional version control tools like Git struggle with large datasets and model files. This is where Data Version Control (DVC) comes in.

The project integrates DVC to track datasets, store model checkpoints and sync them with remote storage like AWS S3. This ensures full reproducibility of results even months after training.

Core concepts:

Initializing DVC in a project
Configuring remote storage (e.g., S3)
Saving and versioning models
Synchronizing data across teams

Tech stack: DVC

Week 4: Model Packaging with ONNX

Model packaging allows developers to run their trained models across various platforms and frameworks.

The project uses ONNX (Open Neural Network Exchange) to convert PyTorch models into a format compatible with other environments. With ONNX Runtime, the packaged models can be deployed for high-performance inference on any device.

Covered topics:

What is ONNX and why it matters
Converting trained models to ONNX format
Running inference with ONNX Runtime
Comparing performance across environments

Tech stack: ONNX, ONNXRuntime

Week 5: Containerization with Docker

One of the biggest challenges in deployment is environment inconsistency what works on one system might fail on another. Docker solves this by packaging code, dependencies and configurations into a single portable container.

In this stage, the project shows how to:

Create a FastAPI wrapper for serving models
Build a Docker image for deployment
Use Docker Compose for multi-container setups

Once containerized, your ML app can run reliably on any cloud platform, enabling scalability, reliability and faster deployment.

Tech stack: Docker, FastAPI

Week 6: Continuous Integration and Deployment (CI/CD) with GitHub Actions

Automation is at the heart of MLOps. The CI/CD pipeline ensures that every change whether in data, model or code is automatically tested, validated and deployed.

Using GitHub Actions, the project demonstrates how to automate the entire pipeline:

Build and test your model automatically
Use Google Service Accounts for authentication
Deploy models with minimal manual intervention

This step establishes a fully automated, production-ready workflow.

Tech stack: GitHub Actions, DVC, Docker

Week 7: Container Registry with AWS ECR

To deploy Dockerized models, you need a reliable registry to store your container images. Amazon Elastic Container Registry (ECR) serves this purpose.

This part of the project explains how to:

Configure AWS S3 for remote storage
Push and pull container images from ECR
Automate image deployment with GitHub Actions

This ensures secure, scalable management of model containers in production environments.

Tech stack: AWS ECR, AWS S3, Docker

Week 8: Serverless Deployment with AWS Lambda

Serverless architectures simplify deployment by eliminating the need to manage servers manually.

Using AWS Lambda, the project deploys ML models as serverless functions. These functions automatically scale based on demand and can be triggered via API Gateway.

Developers also learn to automate deployment workflows to Lambda using GitHub Actions combining speed, scalability and cost efficiency.

Tech stack: AWS Lambda, API Gateway, Docker

Week 9: Model Monitoring with Kibana

After deployment, monitoring model predictions ensures that models remain reliable over time. The project concludes by integrating Kibana with Elasticsearch and CloudWatch for visualization and logging.

By monitoring logs and dashboards, developers can detect data drift, model decay and operational issues early helping trigger retraining or redeployment when necessary.

Tech stack: Kibana, Elasticsearch, CloudWatch

Conclusion

The MLOps-Basics project offers a practical, end-to-end roadmap for mastering the MLOps lifecycle. From model training to serverless deployment and continuous monitoring, it encapsulates everything you need to build production-ready machine learning systems.

By integrating tools like Hydra, DVC, Docker, GitHub Actions, AWS Lambda and Kibana, it demonstrates how to automate every stage of the ML workflow efficiently.

Whether you are a data scientist wanting to scale your experiments or an engineer aiming to operationalize models, MLOps-Basics provides the perfect foundation to bridge the gap between machine learning and production.

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