Deploy Serverless Machine Learning Models to AWS Lambda

🚀 Course Title: Deploy Serverless Machine Learning Models to AWS Lambda with Serverless Framework

🧠 Course Headline: Unleash the Power of Machine Learning with Serverless Computing on AWS!

Welcome to the Future of Machine Learning Deployment!

In this comprehensive course, you will embark on a journey into the realm of serverless computing, where scalability, cost-efficiency, and speed are not just buzzwords but tangible realities for your machine learning projects. Milan Pavlović, an expert in the field, will guide you through the process of deploying various ML models to AWS Lambda, leveraging the powerful Serverless Framework.

Why Go Serverless with Machine Learning Models?

• 📈 Scalability: Automatically scale your ML model to meet any demand without manual intervention.
• 💰 Cost-Effectiveness: Pay only for the compute time you consume, avoiding the overhead of maintaining servers around the clock.
• ⚡ Speed: Rapidly deploy models from development to production with minimal configuration and management.

What You'll Learn:

• AWS Mastery: Dive into Amazon Web Services, focusing on Lambda, API Gateway, S3, and CloudWatch.
• Real-World Use Cases: Explore a variety of ML use cases such as NLP with spaCy, deep learning for computer vision with Tensorflow, and regression analysis with scikit-learn.
• Serverless Framework Skills: Master this tool for streamlined Lambda function creation and deployment.
• Cost-Conscious Learning: Utilize free AWS resources throughout the course, ensuring you learn without financial strain.

Course Outline:

1. Introduction to Serverless Computing on AWS 🌟

   • Understanding AWS Lambda and its benefits for ML deployment.
   • Setting up your environment with AWS free tier resources.

2. Getting Familiar with the Serverless Framework 🛠️

   • Core concepts and components of the Serverless Framework.
   • Deploying your first Lambda function using the Serverless Framework.

3. Machine Learning Models Preparation for AWS Lambda 🧫

   • Adapting different ML models (scikit-learn, spaCy, Keras/Tensorflow) to work within Lambda's constraints.
   • Optimizing model size and performance for serverless environments.

4. Real-Life Machine Learning Use Cases 🎯

   • Exploring various ML applications with real-world examples.
   • Best practices for deploying NLP, vision, and regression models to AWS Lambda.

5. Monitoring and Managing Your Serverless ML Models 📊

   • Utilizing Amazon CloudWatch for monitoring Lambda function performance.
   • Implementing logging, error handling, and security best practices.

6. Training Machine Learning Models in Jupyter Notebooks 📝

   • Setting up a development environment for ML model training.
   • Examples of training processes and the use of pre-trained models.

7. Course Summary and Next Steps 🎓

   • Recap of key concepts and deployment strategies.
   • Guidance on how to proceed with your serverless ML journey.

By the end of this course, you'll have a solid understanding of deploying machine learning models serverlessly on AWS Lambda using the Serverless Framework. You'll be equipped with the skills needed to manage and monitor these models effectively, ensuring they run efficiently and at a fraction of the cost traditional deployment methods would incur.

👩‍🏫 Instructor: Milan Pavlović Milan is an experienced data scientist and cloud architect who has a passion for teaching and a deep understanding of serverless technologies and machine learning. His expertise will be your guiding light throughout this course.

🛠️ Tools & Technologies:

• AWS Lambda, API Gateway, S3, CloudWatch
• Serverless Framework
• scikit-learn, spaCy, Keras/Tensorflow

🎉 Join us now and take your machine learning models to the next level with serverless computing on AWS! 🎉