Data-Driven Enterprise: Vision to Value (Level 100)
Organizations are looking to derive greater value from their data to increase agility, improve efficiency, and accelerate innovation. While data is abundant and growing rapidly, just producing or storing a lot of it doesn’t automatically create value. Value is realized by creating a culture and operating model that use the data to invent on behalf of customers using actioned insights, analytics, and AI/ML. However, cultural challenges, outdated governance models, organizational silos, and legacy execution approaches stand in the way of realizing this vision. Join this session to learn the strategies from two former CXOs on how they worked to create a data-driven culture and overcame the challenges to turn their vision into reality.

Speaker: John Clark, Enterprise Strategist, AWS
Duration: 30mins

Building a smarter organization powered by data and machine learning (Level 100)
Many organizations know they need AI/ML to create unique competitive advantage, drive better customer engagement, and deliver desired business outcomes. While some are reaping the benefits from the transformative impact of AI/ML, others are searching for answers on where to start. This session covers how to apply AI/ML and make your digital transformation a reality. We share the data network effect and the areas that successful organizations master to bring about greater value from the data, powered by machine learning to make their vision a reality.

Speaker: Tom Godden, Enterprise Strategist, AWS
Duration: 30mins

Driving sustainability with AI and data at Amazon’s Climate Pledge Arena (Level 100)
Amazon is the world’s biggest corporate buyer of renewable energy and needs to ensure sustainability is at the heart of all of its operations in order to meet its carbon emission targets. An area it is working on is with Seattle Kraken to build solutions and help make its Climate Pledge Arena the most progressive, responsible, and sustainable in the world. Join this session to learn how AWS professional services and Amazon sustainability teams are using AWS services to ingest and analyze energy, water, and air-quality data. Get insights on how they build real-time forecasting models with data discovery, security, and design patterns at the heart.

Speakers: 
Rahul Sareen, Global Practice Manager, Sustainability, AWS
Rob Johnson, VP Sustainability & Transportation, Climate Pledge Arena

Duration: 30mins

Getting started on your ML journey: A leader's perspective (Level 100)
AI and ML hold the promise of transforming industries, increasing efficiencies, and driving innovation. The key to machine learning success is scale. This session covers how executives and managers who are looking to achieve success using ML at scale get guidance including mechanisms to build an effective system to accelerate innovation and drive technological progress. We share how customers worked with AWS to align their teams in introducing ML and providing their teams the right technical skillset to deliver business outcomes. Learn how to create strong AI/ML product and engineering teams, aligned to organization’s shared goals, to deliver a roadmap of innovation and value.

Speaker: Naomi Teng, AI/ML Specialist, APJ, AWS
Duration: 30mins

Transform your business with AI/ML: Create a competitive advantage in your organization by leveraging AI/ML latest trends (Level 100)
AI/ML techniques are increasingly important fundamentals for organizations looking to transform and deliver their objectives. However, applying AI/ML in the right place is not easy. Join this session to learn how to apply practical and proven machine learning use cases to quickly achieve real business impact. We share the AWS AI/ML suite of services that allow you to build transformative products without requiring prior machine learning expertise. Uncover how to create your own flywheel of AI powered transformation based on both existing and emerging technologies to conceptualize new opportunities, achieve competitive advantage and deliver organizational outcomes.

Speaker: Nieves Gracia, AI/ML Specialist Lead, Public Sector, APJ
Duration: 30mins

Rethinking machine learning for regulated industries (Level 100)
Reproducibility, traceability, explainability have become the fundamental requirement in the machine learning lifecycle for regulated industries. But building a data science platform for a bank or the government to supports this lifecycle is no easy feat as it requires a deep set of capabilities and experience. AWS provides a set of services and solutions to create a secure, governed, and compliant machine learning environment, that does not compromise on data science teams’ agility. Join this session to learn how to close the gap between the traditional machine learning lifecycle and regulated industry requirements. We share how AWS provides you the solutions, best practices, programs, and resources to help you build a successful data science and machine learning platform on AWS.

Speaker: Juan Bedoya, Public Sector Solutions Architect, AWS
Duration: 30mins

Delivering multimodal customer engagement in financial services (Level 100)
The widespread adoption of mobile services, new digital native market entrants, adapting to generational shifts, and critical industry requirements on regulatory compliance make it challenging for financial services institutions to deliver a personalized, consistent and seamless customer experience from different channels. This session showcases how you can easily build a multimodal customer experience with AWS AI and machine learning. Learn how to create the personalized mobile, web and text-first activated experiences for both inbound and outbound customer engagement, by leveraging contextualized data while maintaining a single conversation across touchpoints.

Speakers:
Akash Jain, Head - FSI GTM Solutions Architect, APJ, AWS
Rahul Kulkarni, Senior Partner Solutions Architect, AWS India

Duration: 30mins

Personalize customer engagements with marketing automation (Level 200)
When it comes to customer communications, it is not surprising that personalization is the best way to ensure engagement with customers in the long run. Customers are more likely to give their attention to content that is tailored to their needs. In this session, we showcase how to use Amazon Pinpoint journeys to provide personalized multi-step customer experience based on audience attributes and behaviors; and how to use Amazon Personalize to ensure that the communication content is always specific and personalized for the recipient.

Speaker: Pierre Semaan, GTM Strategy and Solutions Lead, SMB, APJ, AWS
Duration: 30mins

Setting up secure, well-governed machine learning environments on AWS (Level 100)
Whether your organization is starting on its AI/ML journey, or has a large number of projects in production, it is vital to have secure environments that protect your data. In this session, we share how you can organize, standardize, and expedite the provisioning of governed ML environments by leveraging the AWS security best practices and meet the strict security requirements of ML workloads.

Speaker: Tony Fendall, Principal Solutions Architect, AWS
Duration: 30mins

Deep learning on AWS with NVIDIA: From training to deployment (Level 200)
Over the past decade, NVIDIA has been able to illustrate the effectiveness of its GPUs across the board for both deep learning training and inference. As these models become larger, the inherent need to scale up for training and scale out for deploying such large models has become a necessity. In this session, we will walk through a few NVIDIA software stacks for efficient distributed training as well as streamlined deployment and dive deep into how Amazon adopts them for some of their most demanding workloads.

Speaker: Michael Lang, Solutions Architecture Manager, APAC South, NVIDIA
Duration: 30mins

Build centralized smarter search capabilities from distributed data stores with Amazon Kendra (Level 200)
How can you get the most accurate and specific answers in search queries when the answers may require you to sieve through large volumes of distributed data sources? In this session, we show how to use Amazon Kendra, an intelligent search solution to get straightforward answers. Learn how you can bridge a number of third-party tools, sources, and products to create unified and smarter data search capabilities, improve cross-team knowledge sharing, enhance sales, and customer support services, which makes it much easier to get the information you need.

Speakers: 
Sam Gordon, Senior Cloud Architect, AWS
Ed Fraga, Cloud Architect, AWS

Duration: 30mins

Implement unified text and image search application with analytics and ML (Level 200)
While text and semantic search engines has enabled many organizations to search for information quickly, organizations that offer unified text and image search engines can provide competitive advantage and revenue streams by offering their customers the flexibility to show physical examples or images to describe the items in the search engines. This session showcases how to build a ML-powered search engine to easily retrieve and recommend products based on text or image queries. Learn how to use Amazon SageMaker to host and manage the pre-trained Contrastive Language–Image Pre-training (CLIP) model, and run visual search from a query image. We also share how to use easy to deploy, operate, and scale OpenSearch clusters and the other AWS services to build this end-to-end application.

Speaker: Kevin Du, Senior ML Data Lab Solutions Architect, AWS
Duration: 30mins

Scalable data preparation & ML using Apache Spark on AWS (Level 200)
Analyzing, transforming and preparing large amounts of data is a foundational step of any data science and ML workflow. This session shows how to build end-to-end data preparation and machine learning (ML) workflows. We explain how to connect Apache Spark, for fast data preparation in your data processing environments on Amazon EMR and AWS Glue interactive sessions from Amazon SageMaker Studio. Uncover how to access data governed by AWS Lake Formation to interactively query, explore, visualize data, run and debug Spark jobs as you prepare large-scale data for use in ML.

Speaker: Suman Debnath, Principal Developer Advocate, Data Engineering, AWS
Duration: 30mins

Build an intelligent document processing solution (Level 200)
Organizations have millions of physical documents and forms that hold critical business data. These documents, such as insurance claims or loan applications, have structured and unstructured data that are either extracted by humans or by rule-based systems which are not easily scalable, costly and may produce low-accuracy extraction results. In this session, learn how to build an end-to-end intelligent document processing solution to overcome the legacy document processing challenges, enabling you to extract structured data, redact sensitive information, and deploy automated document processing workflow at scale.

Speaker: Abhijit Kalita, Senior AI/ML Evangelist, Public Sector Partners, AWS
Duration: 30mins

Customize and improve your document extraction with machine learning (Level 300)
Documents come in various file types, varied formats, and contain valuable information. The extraction and processing the documents can be time consuming, prone to error, and costly. In this session, we share the options on how to easily extract information from complex content in any document format. including PDFs or scanned images with AWS. Learn how to tune and customize that extraction with ML including common OCR error patterns and re-structuring output data. The session covers different patterns and tools on AWS to help across all stages of the pipeline from initial image pre-processing, through to process automation or intelligent search, and online human review, taking into account the complexity of your use case and ML maturity in your organization.

Speaker: Alex Thewsey, ML Specialist Solutions Architect, AWS
Duration: 30mins

From accuracy to business case: Building a successful demand forecasting PoC (Level 200)
Forecasting future demand accurately with AI/ML has numerous benefits across different functions including increasing sales, improving capacity utilization and inventory turn-over, and enhancing customer experience. But many face challenges in justifying the value and implementing demand forecasting systems into production. This session shows you the step by step workflow to build a rapid prototyping for ML-based forecasting system using Amazon Forecast. We showcase the different ways for measuring the real business value of demand forecasting models while allowing flexibility in experimentation.

Speaker: Julia Ang, Associate Solutions Architect, AWS
Duration: 30mins

Simplify customer purchase intent predictions with analytics and ML (Level 200)
Companies are integrating AI/ML solutions to their business to stay ahead of competition. However, machine learning can be hard and often requires specialized skillet. It begins with collecting and preparing the data, followed by building, training the machine learning models before deploying it. Even choosing an algorithm to build the model can be tough. Which algorithm or machine learning model should you pick? How can you reliably figure out which model will perform the best based on your business problem? How to do hyper parameter tuning to get the best out of the model? In this session, we explain how to simplify machine learning lifecycle on purchase intent prediction using Amazon SageMaker Autopilot combined with AWS analytics services.

Speakers: 
Kamal Machanda, Solutions Architect, AWS India
K V, Sureshkumar, Prototyping Architect, AWS India
Duration: 30mins

Improve customer experience with analytics and ML powered contact centers (Level 300)
Your contact center is the biggest touchpoint between you and your customers, and every engagement can provide your team with powerful insights. In this session, we demonstrate the integration of Amazon Connect with AWS analytics and ML services, so you can use the self-service configuration tools to accomplish in days, instead of what would have taken you months to build. Learn how this end-to-end cloud center solution built on AWS allows you to surface valuable insights from every customer engagement including real-time churn predictions so you can improve your customer experience.

Speakers: 
Nelson Martinez, Senior Technical Account Manager, Productivity Apps, AWS
Melanie Li, Senior Technical Account Manager, Analytics, AWS
Partha Sarathi Sahoo, Senior Technical Account Manager, Analytics, AWS

Duration: 30mins

Breaking language barriers with AI (Level 200)
Amazon brings natural language processing, speech recognition, text to speech, and machine translation within the reach of every developer. API-driven application services enable data scientist and developers to easily plug pre-built artificial intelligence functionality into their applications and to automate workflows. In this session, we explain how to build the next generation of intelligent apps that hear, speak, and understand the world around us.

Speakers: 
Jyoti Sharma, Prototyping Engineer, AWS India
Arun Balaji, Principal Prototyping Engineer, AWS India

Duration: 30mins

Running a closed feedback loop computer vision quality inspection application (Level 200)
Defect and anomaly detection in the quality inspection is a vital step to ensure the quality of the products, as timely detection of faults or defects and taking appropriate action often incur significant operational and quality-related costs. In addition, manual feedback loops are often subjective, time consuming and difficult to scale, resulting in production bottlenecks and slows down time to market. In this session, we share how you can build a robust, effective, and scalable closed loop quality inspection at the edge, generate objective decisions with the quick feedback loop and reduce quality related costs.

Speaker: Derrick Choo, Solutions Architect, AWS

Intelligent media analytics with machine learning (Level 200)
Media assets, such as audio and video, can be used to increase discoverability and drive greater user engagement and satisfaction. However, managing, analyzing, and monitoring media content is both complex and expensive. This session demonstrates how to use AWS AI services and Amazon SageMaker for better content search and discovery, increase accessibility through captioning and localization, and uncover new content monetization. We also show to use fully-managed image, video, text, and speech moderation APIs and automated machine learning to improve compliance and brand safety for you and your customer.

Speakers: 
Sakthi Srinivasan, Engagement Manager, Prototyping, AWS India
Arun Balaji, Principal Prototyping Engineer, AWS India

Duration: 30mins

Build an end-to-end credit card fraud detection system (Level 300)
As we move towards cashless society, the ability to detect fraudulent card transactions accurately and quickly has become increasingly important, because false positives can result in negative customer experiences. In this session, uncover how to build end-to-end credit card fraud detection system with Amazon SageMaker. Learn how to train mathematical models in the cloud for detecting fraudulent card payment fraud with an approach that is more agile and cost-efficient. We demonstrate how you can integrate this model with your business applications using APIs and build reporting dashboards with Amazon QuickSight, a fast, cloud-powered BI service that makes it easy for everyone in an organization to get insights from their data through rich, interactive dashboards.

Speaker: Indrajit Ghosalkar, Solutions Architect, AWS
Duration: 30mins

Combat account takeover fraud with AWS (Level 300)
Every year, many user accounts are compromised by different techniques such as credential stuffing, phishing, and social engineering, leading to account takeover (ATO) fraud. Apart from financial losses, ATO fraud has effects on customer experience, brand loyalty, and reputation. In this session, we explain how AWS Web Application Firewall enables you to proactively stop account takeover attempts at the network edge, prevent unauthorized access that may lead to fraudulent activities, and notify users in advance to take preventive action. We also showcase how to use additional ways to protect your application with machine learning using Amazon Fraud Detector, a fully managed service that enables you to build, deploy, and manage custom fraud detection ML models quickly without previous ML experience.

Speakers: 
Julian Ju, Senior Edge Specialist Solutions Architect, AWS
Ram Cholan, Senior Edge Specialist Solutions Architect, AWS

Duration: 30mins

Extracting meaningful radiology insights from natural language using Amazon Comprehend Medical (Level 300)
The insights needed to optimize the use of scarce and in-demand clinical resources are often hidden plain sight within unstructured clinical reports. This session explains how to integrate machine learning and analytics technology into their applications and automate processes to optimize the use of clinical resources. We show the use of near-real-time Apache Spark pipeline, with Amazon Comprehend Medical, to capture radiology examinations as they are added to the hospital's clinical data repositories. Learn how to classify natural language clinical notes, and translate the clinical entities into relational views built on standard SNOMED Clinical Terminology. We conclude by showcasing how general-purpose visualization and analysis tools can enable your users to access the data insights.

Speaker: Craig Roach, Principal Solutions Architect, AWS
Duration: 30mins

Get started with Amazon SageMaker in minutes (Level 200)
Amazon SageMaker provides every developer, business analyst, and data scientist with the ability to build, train, and deploy machine learning models for any use case with fully managed infrastructure, tools, and workflows. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. This session dives deep into the technical details of each of the modules of Amazon SageMaker which showcases the capabilities of the platform.

Speaker: Pauline Kelly, Solutions Architect, AWS
Duration: 30mins

Transform semi-structured nested JSON data for machine learning with no-code solutions on AWS (Level 200)
In many industries, data comes from various sources in structured, semi-structured, and unstructured formats. For semi-structured data, one of the most common lightweight file format is JSON. However, due to the complex nature of JSON data type, it often includes nested key-value structure and is difficult to be used directly in ML tasks. In this session, we discuss how to leverage AWS Glue DataBrew to unnest the data, handle sensitive information, and ensure data quality for ML data preparation. We share how to use Amazon SageMaker no-code solution to automatically train ML models with the processed data to unlock actionable insights quickly.

Speakers: 
Melanie Li, Senior Technical Account Manager, AI/ML, AWS
Partha Sarathi Sahoo, Senior Technical Account Manager, Analytics, AWS

Duration: 30mins

Build accurate models combining diverse data types with AutoGluon on Amazon SageMaker (Level 300)
Real-world machine learning use-cases often involve data in many forms. In this session, we cover the overview of Amazon SageMaker JumpStart which automatically trains and tunes hundreds of ML models and helps you pick the best model for your use case. We demonstrate how to use AutoGluon, an open-source library for AutoML on Amazon SageMaker to build your high-quality model. We also share proven techniques, best practices and tools for diving deeper with custom multi-modal ML.

Speaker: Seema Gupta, Senior Solutions Architect, AWS
Duration: 30mins

Train ML models quickly and cost-effectively with Amazon SageMaker (Level 200)
Training machine learning models at scale often requires significant investments. In this session, we show how Amazon SageMaker enables you to reduce time and costs to train and tune machine learning (ML) models without the need to manage infrastructure. Learn how to use models using built-in tools to manage and track training experiments, automatically choose optimal hyperparameters, debug training jobs, and monitor the utilization of system resources such as GPUs, CPUs, and network bandwidth. We show how SageMaker Training tools enables faster distributed training, including libraries for data parallelism and model parallelism, and the Amazon SageMaker distributed training libraries automatically split models and training datasets across GPU instances to help you complete distributed training faster.

Speaker: Gaurav Singh, Solutions Architect, AWS India
Duration: 30mins

Beyond model development, training and deployment - Deep dive on Amazon SageMaker Model Monitoring  (Level 200)
Unlike traditional software development, ML model development is an iterative process that requires the continuous monitoring of the deployed model’s input and output to ensure the optimum results. Join this session to learn the fundamentals of model monitoring with Amazon SageMaker. We cover how to detect the drift in your data and model, and share relevant steps to ensure your model quality in production.

Speaker: Sahil Verma, Solutions Architect, AWS India
Duration: 30mins

Deploying a Text to Image Model with Amazon SageMaker and Amazon Rekognition (Level 200)
Join this session to learn how global visual communications platform Canva built their new text-to-image functionality with Stable Diffusion on Amazon SageMaker, enabling them to scale the text-to-image feature to 100 million users quickly in less than 3 weeks. We dive deep into the architectural framework behind the end-to-end solution, how to remove heavy lifting from each step of the ML process, making it easier to develop high-quality models, rapidly roll out innovate new features to users and scale for future growth. We also share how Canva leverage Amazon Rekognition, which offers pre-trained and customizable computer vision (CV) capabilities to extract information and insights from images and videos. Learn how this solution enabled them to build user trust, safety and improve productivity, as manual moderation would have required Canva to deploy hundreds of moderators round the clock.

Speakers: 
Ben Friebe, Senior ISV Solutions Architect, AWS
Greg Roodt, Head of Data Platforms, Canva

Duration: 30mins

Rapidly launch ML solutions at scale on AWS infrastructure (Level 200)
AWS offers the broadest and deepest services around quickly building and launching AI and machine learning for all types of organizations, businesses, and industries. In this session, we explain how to deploy your inference models on AWS, explore what factors to consider, and how to optimize the deployments. We share best practices and approaches to get your ML workloads running smoothly and efficiently on AWS.

Speaker: Santhosh Urukonda, Senior Prototyping Engineer, AWS India
Duration: 30mins