Key Takeaways
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Gain clarity on ML workflow challenges and foundational concepts, including Level 0 and Level 1 MLOps architectures.
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Learn to deploy models using tools like Streamlit and handle real-time and batch predictions with proper monitoring.
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Identify and address key production issues such as data drift, concept drift, and architectural limitations using MLOps best practices.
Who Should Enroll?
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Ideal for students looking to bridge the gap between machine learning theory and real-world deployment through hands-on MLOps practices.h
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Perfect for data scientists and ML engineers aiming to scale their models to production and implement reliable MLOps workflows in real environments.
About the Instructor
Apoorv Vishnoi Head Training Vertical, Analytics Vidhya
Apoorv is a seasoned AI professional with over 14 years of experience. He has founded companies, worked at start-ups, and mentored start-ups at incubation cells.
Course curriculum
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1
Kick-Start Your MLOps Journey
- Course Introduction
- The Challenges in ML Workflow
- General Challenges
- Blueprint of Level 0 Architecture
- Hands-on with Level 0 Architecture Part - I
- Hands-on with Level 0 Architecture Part - II
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2
Deploying and Monitoring ML Models
- Real-time Prediction and Batch Time Prediction
- Model Deployment in Streamlit
- Understanding Data Drift and Concept Drift
- Drawback of Level 0 Architecture
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3
Overview of Level 1 MLOps
- Introduction to Cloud Platform
- ML Framework
- Blueprint of Level 1 Architecture Part I
- Blueprint of Level 1 Architecture Part II
- Best Practices for MLOps Mastery!
FAQ
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What is MLOps, and how is it different from DevOps?
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.
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What are the main components of an MLOps pipeline?
An MLOps pipeline typically includes data ingestion, preprocessing, model training, versioning, deployment, monitoring, and retraining workflows.
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What is data drift and how does it affect model performance?
Data drift refers to changes in input data over time. It can degrade model accuracy, making continuous monitoring essential in production environments.
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What is the significance of data drift and concept drift?
Data drift refers to changes in input data, while concept drift refers to changes in the relationship between input and output. Both can degrade model performance and must be addressed in production.
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Will I receive a certificate upon completing the course?
Yes, the course provides a certification upon completion.