AI Career Hub!

ML Deployment using AWS
1. Learn how to take your ML model from Jupyter Notebook to real-world production using Amazon Web Services.
2. Deploy models using Amazon EC2, Amazon SageMaker and serverless options like AWS Lambda.
3. Understand real-time APIs, scaling and cost optimization.
4. Includes a hands-on live deployment demo.
Key Takeaways:
1. Deploy ML models independently on AWS
2. Create real-time prediction APIs
3. Understand cloud architecture for ML projects
4. Gain practical exposure to production workflows

Agenda:
1. Introduction to Databricks
2. Hands-On Data Processing with PySpark
3. Building a Machine Learning Model in Databricks
Outcome:
1. Understand how Databricks simplifies ML workflows.
2. Perform data preprocessing using PySpark in notebooks.
3. Build and evaluate a Machine Learning model.

ML Deployment using Azure
1. Master ML model deployment on Microsoft Azure with practical examples.
2. Deploy using Azure Virtual Machines, Azure Machine Learning and Azure App Service.
3. Learn endpoint creation, scaling and production best practices.
4. Includes end-to-end live deployment walkthrough.
Key Takeaways:
1. Deploy ML models on Azure cloud environment
2. Create scalable online endpoints
3. Understand enterprise-grade ML deployment flow
4. Build production-ready ML solutions