Goal

Leverage AWS Serverless services to build a simple food delivery app in less than 3 hours!

Abstract

In this workshop, you will architect, develop and deploy a simple food delivery application that enables users to order food from the app using services like AWS Lambda, Amazon API Gateway, Amazon S3, Amazon DynamoDB, Amazon EventBridge, and Amazon Cognito. With the knowledge from the workshop, you can build and deploy your own serverless applications using these services for common use cases like web applications, analytics, and more.

Description

Who doesn’t love food? Who doesn’t love the comfort of ordering food from our couch? And as curious engineers, we all love the technology that goes behind building such amazing apps. What if we could build something similar (basic and not a full-blown app :)) in less than 3 hours? This is the power of Serverless - building highly scalable, highly available, and cost-effective applications!

In this workshop, we will understand how easy & quick it is to build real-world applications using Serverless on AWS and understand the various services that can be leveraged. The application will present users with a React user interface for ordering food items. The interface on the backend with RESTful and WebSocket services will accept the request, and find and dispatch food to the requester’s location. The application will also provide facilities for users to register with the service and log in before ordering food.

AWS Services Covered:

1. The frontend of the application will be hosted on Amazon S3, with CDN enabled using Amazon CloudFront

2.The backend of the application will be entirely driven by AWS Lambda functions

3. Amazon EventBridge will be used for inter-communication across different microservices

4. Amazon API Gateway for REST and WebSocket APIs

5. Amazon CloudWatch & AWS X-Ray for monitoring, observability, and debugging

6. Amazon Cognito for user management, authentication and authorization

7. DynamoDB for the storage

AWS Account

In order to complete this workshop you'll need an AWS Account with access to create IAM, S3, DynamoDB, Lambda, EventBridge, API Gateway, and Cognito resources. All of the resources you will launch as part of this workshop are eligible for the AWS free tier if your account is less than 12 months old. See the AWS Free Tier page for more details.

Essential React & Python knowledge

The front end is built on React, and the backend code is written in Python; hence working knowledge is needed of these languages is for this workshop.

Speakers

Dipali Kulshrestha

Natwest Group

Rajat Arora

Natwest Group

Rashmi Subhash

KonfHub

Srushith R

KonfHub

Goal

Generate Insight by Breaking Data Silos on AWS!

Abstract

● Data is the underlying force that fuels the insights and predictions that result in better decision-making and innovation.

● In this workshop, learn how to use data to reinvent your strategy now and in the future. We explore how to build a modern data strategy blueprint that will help you manage, access, analyze, and act on data

● With the knowledge from the workshop and exposure to different AWS Data services, you can replicate them for any enterprise use case.

Description

● This workshop will focus on hands-on labs and modules that focus on ingestion, hydration, exploration, and consumption of data in a data lake in AWS.

● It includes hands-on time with AWS analytics services, including AWS Data Migration service for batch data ingestion, AWS Glue for data catalog and running ETL on Data lake, Amazon Athena to query data lake, and Amazon Quicksight for visualization.

● The workshops provided here will guide you through creating a modern data platform on AWS and will demonstrate the fundamental principles:

1. Scalable Data Lakes

2. Purpose Built Data Services

3. Seamless Data Movement

4. Easy Data Visualization

5. Performant and Cost-Effective

Target Audience

● Engineers

● Business Analytics

● Data Analysts

AWS Account (BYOA - bring your own account):

● To complete this workshop, you'll need an AWS Account with full access to create different AWS services like Amazon RDS, Amazon S3, AWS Glue, Amazon Athena, Amazon Quicksight, AWS StepFunctions, and AWS Airflow Managed Service.

● See the AWS page & video tutorial for details on deploying the new AWS Account.

Agenda

Outcome:

● Presentation covering Overview of AWS DNA services

● Lab Manual to execute the workshop offline step by step

● Automation scripts to provision any prerequisite required for performing the lab on AWS, like deploying AWS IAM and other services like AWS S3.

● Best practice for handling AWS Account

Speakers

Sachit Jain

Quantiphi

Goal

Using Amazon SageMaker to Train & Deploy ScikitLearn ML Model and Integrate to API Gateway using AWS Lambda Function

ABSTRACT

- In this workshop, You will learn how to train, develop and deploy Machine Learning model. How AWS Services can be leveraged to create a production ready Machine Learning model.

- You will practice using Amazon SageMaker Instance, S3 Bucket, API Gateway, Lambda Function services.

Description

- Machine Learning is something which is always constrained to data scientists. It is always difficult as an individual to carry out many complex Machine learning tasks in a normal environment.

- In this Workshop, We will understand how AWS Provides Cloud Environment to Create Machine Learning Models which have a very specific set of requirements.

- We will also learn to use AWS Lambda and API Gateway to expose the Machine Learning Details to Outside applications which can access them through REST APIs.

AWS SERVICES COVERED

- The Machine Learning model with Python.

- We will be leveraging the ScikitLearn model for training.

- Amazon Sagemaker features such as Jupyter Notebooks, Training Jobs, Models and Endpoint configurations to run Machine Learning Trained models.

- Amazon S3 for storing and transferring data points.

- Creating Machine Learning Predictions using the Trained Library.

- AWS Lambda for Sagemaker communication.

- API Gateway for creating Rest APIs for Prediction Results.

AWS ACCOUNT

- Whizlabs will be providing AWS Platform for the workshop.

- Each workshop attendee will get a separate AWS Account for doing workshop from whizlabs.

- Zero Additional cost as the cloud environment is already created for you.

ESSENTIAL KNOWLEDGE

- Basics in Python -- Codebase will be provided during workshop.

- Basic understanding in Machine Learning Concepts - It Helps.

- AWS Cloud services basics Understanding - Lambda, Sagemaker - You will be shown how to use them as part of workshop.

OUTCOME

- Variety of features provided by Amazon Sagemaker.

- How Machine Learning models can be designed and developed on AWS.

- Understanding how to make the production ready APIs for Machine Learning Predictions.

Speakers

Pavan Gumaste

Whizlabs