2025 Deploy ML Model in Production with FastAPI and Docker
Deploy ML Model with ViT, BERT and TinyBERT HuggingFace Transformers with Streamlit, FastAPI and Docker at AWS
4.67 (528 reviews)
20,551
students
18 hours
content
Apr 2025
last update
$64.99
regular price
What you will learn
Deploy Machine Learning Models with FastAPI: Learn to build and deploy RESTful APIs for serving ML models efficiently.
Master Cloud-Based ML Deployments with AWS: Gain hands-on experience deploying, managing, and scaling ML models on AWS EC2 and S3.
Automate ML Operations with Boto3 and Python: Automate cloud tasks like instance creation, data storage, and security configuration using Boto3.
Containerize ML Applications Using Docker: Build and manage Docker containers to ensure consistent and scalable ML deployments across environments.
Streamline Model Inference with Real-Time APIs: Develop high-performance APIs that deliver fast and accurate predictions for production-grade applications.
Optimize Machine Learning Pipelines for Production: Design and implement end-to-end ML pipelines, from data ingestion to model deployment, using best practices.
Implement Secure and Scalable ML Infrastructure: Learn to integrate security protocols and scalability features into your cloud-based ML deployments.
Create Interactive Web Apps with Streamlit: Build and deploy interactive ML-powered web applications that are accessible and user-friendly.
Deploy Transformers for NLP and Computer Vision: Fine-tune and deploy TinyBERT and Vision Transformers for sentiment analysis, disaster tweets, and images.
Monitor and Maintain ML Models in Production: Implement monitoring, A/B testing, and bias detection to ensure your models remain reliable and effective in prod.
Related Topics
3174934
udemy ID
5/27/2020
course created date
6/17/2020
course indexed date
Angelcrc Seven
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