12:00 PM
Run Cost Efficient Workloads on EKS with Karpenter

A large number of AWS customers run containerized workloads on EKS. Customers are continuously looking for avenues to reduce cost of running workloads on Cloud. At AWS, customers can leverage multiple levers to run containerized workloads cost efficiently. In this session, we walk you through the process of building and running cost efficient containers on EKS.

By the end of this session, you will be able to:

• Build multi-arch containers that can run on x86 and arm64 CPU architectures
• Leverage Karpenter to provision cost efficient spot-instances in data plane
• Efficiently distribute workloads across Graviton and non-Graviton instances
• Build this infrastructure using IaC templates

Speaker: Deepesh Dhapola, Senior Solutions Architect, AWS India

12:45 AM
[Customer Use Case] How Postman accelerated innovation and simplified the development lifecycle with Elastic Beanstalk

Are you using Elastic beanstalk to automatically handle the deployment details of capacity provisioning, load balancing, auto-scaling, and application health monitoring. ? Learn how Postman scaled from a side project to a platform supporting 25 million developers using Elastic Beanstalk.

Speakers:

Anup Somashekharan, Sr. Technical Account Manager, AWS India
Jibin Mathews, Engineering Manager, Postman

12:00 PM
Building and Validating your Resiliency and Disaster Recovery Plans using AWS Resilience Hub

As part of a resilience plan, it is important to proactively protect your mission critical applications from the risk of downtime. This includes understanding your applications’ ability to maintain availability and recover from disruptions. AWS Resilience Hub provides you with a central place to define, validate, and track the resilience of your applications on AWS. In this session, we will show you how you can avoid unnecessary downtime caused by software, infrastructure, or operational disruptions. Learn how you can identify and resolve issues before they occur in production and in turn improve your RTO and RPO in case of any event.

Speaker: Shreya Pathak, Solutions Architect, AWS India

12:45 PM
[Customer Use Case]: Scaling with AWS Elastic Kubernetes Service (EKS) in a Cost-Optimized Way

In this we will cover EKS Architecture patterns which Swiggy uses to deliver millions of order everyday and keep their end customers happy. Along with this, in this session we will cover few Best practices on how to do the same in the most cost optimized way.

Speakers:
Gaurav Gupta, Principal Solutions Architect, AWS India
Vivek Garg – Principal Solutions Architect, Swiggy

12:00 PM
Generative AI on AWS - Build and scale generative AI applications with foundation models

Generative AI is a type of AI that can create new content and ideas, including conversations, stories, images, videos, and music. Like all AI, generative AI is powered by ML models—very large models that are pretrained on vast corpuses of data and commonly referred to as Foundation Models

Join this session to learn how Amazon Jumpstart Foundation Models and Bedrock can helps you get started quickly, privately customize foundation models with your own data, and easily integrate and deploy them into your applications using the AWS tools. In this session we will discuss how AI code companion Code Whisperer which is based on Large Language Model (LLM) can help developer productivity.

Speaker: Praveen Jayakumar, Head of ML Solutions Architecture, AWS India

12:45 PM
[Customer Use Case- Fidelity] How Event streaming platform supports business critical applications for Financial customers

Event streaming platforms are very essential for financial services customers like Fidelity; as they form the basis of transactions, money movement and reporting. Any event missed leads to loss of transaction, meaning loss of trust or revenue or both for a company. In this session we will learn about the importance of a robust event streaming platform for financial services organizations, which can be replicated by larger developer community to develop a fault tolerant event streaming platform, particularly in optimizing client application parameters when connecting to event streaming platform using MSK or opensource Kafka. Fidelity has simplified this process through Kafka client-side governance automation, using an OPA rules engine and AWS ML model prediction to optimize performance and throughput while ensuring compliance with security best practices. This governance process is essential for the smooth operation of a financial services organization and can help save costs while maintaining the trust of customers. The talk covers Kafka platform and its governance process with a Demo of linting APIs.

Speakers:
Umamahesh Sistu, Director Cloud Platforms, Fidelity Investments
Ruchi Kapur, Solutions Architect, AWS India

2:45 PM
Application Networking with Amazon VPC Lattice and Amazon EKS

In this session, we will demonstrate a use case where a service in one EKS cluster communicates with a service in another cluster and VPC, using VPC Lattice. We will show how service discovery works, with support for using custom domain names for services. We also demonstrate how VPC Lattice enables services in EKS clusters with overlapping CIDRs to communicate with each other without the need for any networking constructs like private NAT Gateways and Transit Gateways. On the security front, we cover the details of securing access to EKS services using SSL/TSL as well as VPC Lattice authorization policies and enabling other EKS client services to authenticate their requests to such secured services

Speaker: Ayush Kumar, Solutions Architect, AWS India

3:30 PM
Using Memory safe encryption in your applications using AWS SDK for RUST and AWS KMS

Lack of memory safety is the cause of 60%-90% of all software vulnerabilities that extends across desktop OSs, browsers and mobile OSs. It poses a major threat to data protection and internet infrastructure. Rust is a language that provides memory safe programming model out of the box, and is being increasingly adopted as the language to design secure applications.

AWS KMS is a key management service that provides support for encrypting data at rest in AWS.

In this session, we'll walk you through the process of creating memory safe code in Rust leveraging the AWS SDK for RUST and AWS KMS that you can use to encrypt and decrypt sensitive data in your applications.

This session is geared towards developers, solutions architects and security specialists.

Speaker: Vipul Tankariya, Sr. Partner Solutions Architect, AWS India

4:15 PM
Modernise your monolith using Strangler pattern

When migrating complex monolithic applications to microservices based architectures, organisations face transformation risk and business disruption. The Strangler pattern is used to reduce those risks by progressively replacing existing business domain bounded contexts with new services. In this session, see how to implement the Strangler pattern to migrate an existing monolithic application by incrementally creating new microservices on AWS.

Speaker: Chandrasekaran Sivaraman, Senior SA–Dev Specialist, AWS India

2:45 PM
[Customer Use Case- CRED]: Accelerating Time to market with AWS Serverless by launching 5 products in 5 months

Time to Market is key for organizations to have competitive edge. In this session, we will focus on how CRED leverages AWS sererless such as AWS Step Function, SNS, ECS with fargate, DynamoDB for rapid development of their business features to get competitive edge. Serverless architecture enables CRED to focus on business functionality and saves 60% of their time from undifferentiated heavy lifting , deployments, boiler plate code. This blueprint for CRED has enabled them to launch 5 lines of businesses in 5 months and become youngest Unicorn in India.

Speaker:
Jasmine Maheshwari, Senior Solutions Architect, AWS India
Vaitheeswaran S, Head Cloud Infra & SRE, CRED

3:30 PM
Monitoring system, custom and external metrics of EC2 based workloads with Amazon Managed Prometheus and Amazon Managed Grafana

All workloads can be measured with metrics at the infra (system), application (custom) and supporting platform components e.g., load balancer, database etc. (external) metrics. These metrics can be monitored using AWS Distro for Open Telemetry (ADOT), Amazon Managed Service for Prometheus (AMP) and Amazon Manged Grafana (AMG). This is valuable for customers who use proprietary monitoring tools and shift to open source based, managed monitoring solutions such as AMP and AMG. Partners will also benefit when building advanced monitoring solutions as part of their managed services offerings.

Speaker: Nagesh Subrahmanyam, Partner Management SA, AWS India

4:15 PM
Understanding Gray Failures and Improving Application Availability using Amazon Route 53 Application Recovery Controller

Deep dive into Route 53 ARC service allows you to centrally coordinate failovers and recovery readiness of your application. Using Route 53 ARC with a sample single-Region and multi-Region infrastructure stack, this session will be to provides guidance for planning and implementing a resiliency strategy for your environment.

Speaker: Medha Shree, Solutions Architect, AWS India

2:45 PM
From Data to Deployment: Simplifying the End-to-End ML Lifecycle on AWS

In this talk, we'll explore how to simplify and streamline the end-to-end machine learning lifecycle on AWS. We'll cover best practices for data preparation, model training and deployment, and monitoring and optimization. By the end of this talk, you'll have a solid understanding of how to build and deploy ML models on AWS with ease and efficiency.

Speaker: Vivek Raja, AWS ML Hero and VP - Product at NexStem

3:30 PM
Analyze & Explore Data Without Managing Servers

In this session we start our data journey, build a serverless data lake and look at the tools and services that allow you to go from raw data to a dataset ready for analysis, including data exploration and preparation, ETL, data catalog, in-place SQL queries, and reporting.

Speaker: Niyati Upadhyay, Solutions Architect, AWS India

4:15 PM
Cost effective way to derive insights from transactional data using Redshift ML

Customers who wanted to train ML models using transactional data residing in RDS, often copy the data to S3 using migration services like DMS or use other alternatives such as Debezium/MSK connect etc. They use S3 data for building ML models using Sagemaker. These approaches involve cost associated with maintaining the replication layer and consume more time. On the other hand, the option of using Redshift federated queries might impact the performance of source RDS database. The session talks about a low cost, non-disruptive and easier alternative of leveraging export feature in RDS to copy the data to S3 and how it can be used for training an ML model using Redshift. This approach will benefit the customers who are in the early stages of ML adoption without worrying about cost and skill gap.

Speaker: Sornavel Perumal, Technical Account Manager, AWS India