Building ML Pipelines using MLflow & DVC
IntermediateLevel
1844+Students Enrolled
2 Hrs Duration
4.9Average Rating
About this Course
- Learn to build reproducible ML workflows using MLflow for experiment tracking, model versioning, and evaluation—ideal for collaborative machine learning projects.
- Master the art of building and improving baseline models using BOW, TF-IDF, hyperparameter tuning, model stacking, and techniques to handle imbalanced datasets.
- Create complete ML pipelines with DVC and deploy them using Docker-based CI/CD on AWS. You’ll also build a Chrome plugin and integrate it with your deployed models.
Learning Outcomes
Design ML Pipelines
Build robust ML workflows using MLflow and version control tools.
Optimize ML Model
Improve models with BOW, TF-IDF, tuning, and stacking techniques.
Deploy ML Projects on AWS
Use DVC, Docker, and CI/CD to deploy end-to-end pipelines at scale.
Who Should Enroll
- Aspiring data scientists looking to master real-world ML workflows and deployment techniques.
- ML engineers aiming to build and design reproducible, scalable, and production-ready ML pipelines.
- Tech professionals and Engineers wanting hands-on experience with MLflow, DVC, Docker, and AWS CI/CD.
Course Curriculum
Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.
1. Project Planning & Introduction
1. Data Collection
2. Data Preprocessing & EDA
3. Setup MLFlow Server on AWS
1. Building Baseline Model
2. Improving Baseline Model - BOW, TF-IDF
3. Improving Baseline Model - Max features
4. Improving Baseline Model - Handling Imbalanced Data
5. Improving Baseline Model - Hyperparameter tuning with Multiple Model
6. Improving Baseline Model - Stacking Models
1. Building an ML Pipeline using DVC
2. Data Ingestion Component
3. Data Preprocessing Component
4. Model Building Component
5. Model Evaluation Component with MLFlow
6. Model Register Component with MLFlow
1. Flask API Implementation
2. Implementation of Chrome Plugin
1. Adding Docker
2. Deployment on AWS
Meet the instructor
Our instructor and mentors carry years of experience in data industry
Get this Course Now
With this course you’ll get
- 2 Hours
Duration
- Boktiar Ahmed Bappy
Instructor
- Intermediate
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
MLOps (Machine Learning Operations) focuses on managing ML models through their lifecycle—training, deployment, and monitoring—whereas DevOps is centered around software development and delivery pipelines.
An MLOps pipeline typically includes data ingestion, preprocessing, model training, versioning, deployment, monitoring, and retraining workflows.
DVC stores metadata for datasets and intermediate artifacts in Git-friendly files. By defining pipeline stages (data ingestion, preprocessing, training, etc.), DVC reruns only affected stages when code or data changes, maintaining a reproducible DAG of the workflow.
Each DVC stage has inputs (raw data, scripts), outputs (processed data, models), and a command. DVC tracks hashes of dependencies and outputs, so when code or data changes, only impacted stages re-run, ensuring full pipeline reproducibility.
Yes, you will receive a certificate of completion after successfully finishing the course and assessments.
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