Talk Outline:

1. Introduction
   - The disconnect: challenges when transitioning a Streamlit app from development to deployment.
   - Why deployment considerations should influence app design.

2. Optimizing model loading from HuggingFace hub
   - Challenges:

   • Large model sizes slowing down app performance.
   • Inefficient loading processes increasing costs and user wait times.
   • Solutions:
   • Using Streamlit caching to reuse loaded models across sessions.
   • Preloading models during image build.
   • Deploying models and calling them as APIs
   • MLOps Perspective: How optimized model loading reduces deployment complexity and cloud costs.

3. AWS deployment considerations: autoscaling, authentication, and security
   - Autoscaling:

   • Challenges: Handling variable user traffic without incurring unnecessary costs.
   • Solutions:
   • Using Fargate with ECS for containerized apps with auto-scaling policies.
   • Setting thresholds to scale instances based on traffic and resource utilization.
   • Optimizing cost-performance balance with reserved vs. spot instances.

• Authentication:

  • Challenges: Providing a secure and user-friendly authentication mechanism.
  • Solutions:
  • Integrating AWS Cognito for user management.
  • Adding role-based access control to limit app functionality based on user roles.

• Security:

  • Challenges: Protecting the app from attacks and unauthorized access.
  • Solutions:
  • Using AWS Web Application Firewall (WAF) to block malicious traffic.
  • Configuring CloudFront to protect against DDoS attacks and improve performance.
  • Setting up HTTPS with Route 53 and TLS certificates for secure connections.
• MLOps Perspective: Balancing simplicity and scalability in app deployment.

1. Secrets Storage
   - Challenges: Hardcoding sensitive credentials into the app.
   - Solutions:

   • Using AWS Secrets Manager or Parameter Store for secure secrets management.
   • Employing environment variables for flexible app configuration.
   • MLOps Perspective: How to ensure security without complicating deployment workflows.

2. Key Takeaways
   - Data Scientist’s Perspective:

   • Why it’s critical to consider performance, scalability, authentication, and security during app development.
   • MLOps Perspective:
   • How to simplify deployment while ensuring performance and security.
   • Encouraging collaboration between Data Scientists and MLOps engineers for smoother deployment processes.

What you will learn:

• How to efficiently load Hugging Face models in Streamlit apps to reduce costs and improve performance.
• How to design apps with AWS autoscaling to handle variable traffic seamlessly.
• Best practices for implementing user authentication with AWS Cognito.
• How to secure your Streamlit app using cloud services.
• Best practices for secure secrets management in Streamlit apps.
• How to approach Streamlit app development with deployment in mind.