Session ID: FPA01
Language: English
Level: 300
Duration: 30mins
Speaker: Matheus Guimaraes, Senior Developer Advocate UK/Ireland, AWS

Modeling your data in a DynamoDB database requires a different approach from modeling in relational databases. Learn about key step, principles and best practices that you can apply as you work with DynamoDB.

Session ID: FPA02
Language: English
Level: 200
Duration: 30mins
Speaker: Yohan Wadia, Senior Public Sector Solutions Architect, AWS

With an innovative architecture that decouples compute from storage as well as offering advanced features like Global Database and low-latency read replicas, Amazon Aurora reimagines what it means to be a relational database. The result is a modern database service that offers performance and high availability at scale, fully open-source MySQL- and PostgreSQL-compatible editions, and a range of developer tools for building serverless and machine learning–driven applications. In this session, dive deep into some of the most exciting features Aurora offers, including Aurora Serverless v2 and Global Database. Also learn about recent innovations that enhance performance, scalability, and security, while reducing operational challenges.

Session ID: FPA03
Language: English
Level: 200
Duration: 30mins
Speaker: Om Prakash Jha, Senior Solutions Architect, AWS

Amazon ElastiCache is a fully managed caching service that delivers real-time performance for modern, internet-scale applications. We will share innovation that now delivers 100% performance improvement for  ElastiCache and how using ElastiCache can reduce your total cost of ownership.

Session ID: FPA04
Language: English
Level: 200
Duration: 30mins
Speaker: Mirabela Dan, Solutions Architect, AWS

Modernizing your applications is beyond moving them to the cloud. That is simply the first step. By moving your applications to the cloud, you gain access to the latest database technologies, allowing you to modernize your applications to better serve your customers, stakeholders, and/or constituents. In this session, learn how you can use open-source relational databases and new technologies such as key-value, document, and graph to enhance performance of your applications.

Session ID: FPA05
Language: English
Level: 200
Duration: 30mins
Speaker: Nikhil Anand, Senior Solutions Architect, AWS

Don’t just move your databases to the cloud. Instead, migrate or optimize your database workloads on the AWS Cloud with purpose-built databases. For many modern applications, such as e-commerce, content management systems, and profile management, the traditional relational data model can become cumbersome and rigid. Leverage Amazon DocumentDB as a fully managed native JSON document database. The JSON document data model provides the flexibility, increased performance, and agility needed to keep pace with the demands of these use cases. In this session, learn how data modeling for Amazon DocumentDB (with MongoDB compatibility) differs from data modeling for relational databases. Also explore how to use Amazon DocumentDB schema design and data modeling concepts to build flexible and scalable applications.

Session ID: ANA01
Language: English
Level: 200
Duration: 30mins
Speakers: Pragnesh Shah, Solutions Architect, AWS & Victory Uchenna, Solutions Architect, AWS

AWS analytics services empower data users, such as data scientists, analysts, and business users with diverse technical expertise, across an organization to quickly access, analyze, and gain insights from their data. In this session, learn how AWS can democratize analytics for your organization with ease of use and better price performance. Dive into how serverless AWS simplifies data preparation and makes it easier for analysts and data scientists of all skill levels to use machine learning.

Session ID: ANA-02
Language: English
Level: 300
Duration: 30mins
Speaker: Roy Yung, QuickSight Specialist Solutions Architect, AWS

In this session, learn how to use AWS serverless Business Intelligence Solution to provide your users with machine learning–powered and natural language query (NLQ) supported interactive dashboards. And learn how to programmatically create analytic dashboard from scratch.

Session ID: ANA03
Language: English
Level: 200
Duration: 30mins
Speaker: Wojtek Gawroński, Senior Developer Advocate (CEE), AWS

Many organizations are trying to build streaming  analytics architectures from their real-time data sources often struggle with  finding the proven architectural patterns that customers have implemented.  When building a modern data architecture, there is sometimes the need for  data to flow with low latency between components to power real-time decisions.  This session helps cloud architects, data scientists, and developers to  design and building modern data streaming architectures that can quickly  generate insights by leveraging AWS streaming services such Amazon Kinesis  Data Streams, Amazon Kinesis Firehose, Amazon Kinesis Data Analytics and  Amazon Managed Service for Apache Kafka (Amazon MSK). We will also talk about  best practices while building a low latency modern data streaming  architecture on AWS.

Session ID: ANA04
Language: English
Level: 200
Duration: 30mins
Speaker: Gregory Knowles, Analytics Specialist Solutions Architect, AWS

To meet the challenges of harnessing business insights from vast amounts of data, your data warehouse needs to be scalable, easy to set up, and easy to leverage analytics. It also needs to be machine learning capable and able to analyse all your data.

Learn about how Amazon Redshift, a pioneer in cloud data warehousing, is expanding how cloud data warehousing can deliver insights and enable data driven decisioning for customers in various industries.

Session ID: ANA05
Language: English
Level: 200
Duration: 30mins
Speaker: Suman Debnath, Principal Developer Advocate (Data Engineering), AWS

The first step in an analytics or machine  learning project is to discover and prepare your data to obtain quality  results. AWS Glue is a serverless, scalable data integration service that  helps you discover, prepare, move, and integrate data from multiple sources.  In this session, learn about the latest innovations in AWS Glue and hear how  an AWS customer uses AWS Glue to enable self-service data preparation across their organization.

Session ID: MLT01
Language: English
Level: 200
Duration: 30mins
Speaker: Adam Temple, Senior Solutions Architect, AWS

The ML journey requires continuous experimentation and rapid prototyping to be successful. These processes are traditionally time-consuming and expensive. Amazon SageMaker offers no-code and low-code options for each step of the ML lifecycle so you can build, train, and deploy high quality models faster. In this session, learn how low-code tools, including Amazon SageMaker Canvas, Amazon SageMaker Data Wrangler, Amazon SageMaker Autopilot, and Amazon SageMaker JumpStart, make it easier to experiment faster and so you can focus more on refining predictions and less on low level code.

Session ID: MLT02
Language: English
Level: 200
Duration: 30mins
Speaker: Ahmed Raafat, Principal Solutions Architect AWS

Join us in this session to dive deep into the Amazon SageMaker two options for fully-managed Jupyter Notebooks for data exploration and building ML models. See how to use the built-in data prep capability,collaborative SageMaker Studio Notebooks to increase productivity across all steps in your ML development. And learn how to get started with standalone SageMaker Notebook Instances that offer the broadest choice of compute resources available in the cloud, including GPUs for accelerated computing, and the latest versions of open source ML packages.

Session ID: MLT03
Language: English
Level: 200
Duration: 30mins
Speaker: Stephen Gallagher, Senior Solutions Architect, AWS

Organizations today are in search of vetted solutions and architectural guidance to rapidly solve business challenges with AI and ML. Whether customers prefer off-the-shelf deployments, or customizable architectures, the AWS Solutions Library for Machine Learning (AI/ML) carries solutions built by AWS and AWS Partners for a broad range of industry use cases. These are free to use, open source templates that you can launch in your AWS Account with a click of a button.

Session ID: MLT04
Language: English
Level: 300
Duration: 30mins
Speaker: Ayman Salama, Senior Partner Solutions Architect, AWS

Training machine learning models at scale can bring numerous benefits, such as faster and more accurate predictions, but it also comes with its own set of challenges. Amazon SageMaker is a fully-managed platform that helps you overcome these challenges and accelerate the training of your machine learning models at scale. In this session, we will dive deep into the benefits and challenges of large-scale machine learning and how SageMaker can help you overcome them. We will also discuss how SageMaker enables distributed training and explore a case study where we train and host stable diffusion on 200 SageMaker GPUs. Additionally, we will spotlight AI21 Labs, a company that has successfully leveraged SageMaker to train and deploy their machine learning models at scale. By the end of this session, you will have a solid understanding of how to use SageMaker to train machine learning models at scale and the benefits it can bring to your organization.

Session ID: MLT05
Language: English
Level: 200
Duration: 30mins
Speaker: Srivalsan Mannoor Sudhagar, Cloud Architect, AWS

This session will discuss in detail about how to productionize ML workloads using Amazon Sagemaker MLOps . This will also talk in detail about the new features available in Amazon Sagemaker for MLOps and how to scale MLOps based on organisational requirements.

Session ID: DAI01
Language: English
Level: 300
Duration: 30mins
Speaker: Aamna Najmi, AI/ML Consultant, Professional Services, AWS

In this session, learn how you can solve common business problems using AWS AI/ML services.Results fall into four categories: enhancing customer experience, enabling employees and organizations to make better and faster decisions, improving business operations while reducing cost, and creating completely new products and services powered by AI and ML. During the session, we shall also specifically dive deeper into three major use cases namely, intelligent document processing, text summarization and personalization. We shall also talk about how customers leverage various AWS AI/ML services like Amazon Textract, Amazon SageMaker, Amazon Comprehend and Amazon Personalize for common use cases along with some brief demos to see the services in action.

Session ID: DAI02
Language: English
Level: 200
Duration: 30mins
Speakers: Chara Gravani, Solutions Architect Manager, AWS & Emilio Garcia Montano, Solutions Architect, AWS

Consumers expect real time, curated experiences across digital channels as they consider, purchase and use products and services. With the simple integration of Amazon Personalize into your existing applications, you can create high-value personalization at every touchpoint. Learn how to deliver individually curated recommendations and personalize every user touchpoint with Amazon Personalize, no ML expertise required. During this session, we will show how Amazon Personalize works as well as the service key features. We will finally share a demo that shows how Amazon Personalize can be used to add personalisation into a shopping customer experience.

Session ID: DAI03
Language: English
Level: 200
Duration: 30mins
Speaker: Mia Chang, ML Specialist Solutions

Many organizations are burdened with a fragile or inadequate document processing pipeline. In this session, learn how organizations can take advantage of the latest innovations in AI and machine learning from AWS. With the latest update of document classification and analysis lending, customer will learn how to improve the efficiency of their document processing use case.

Session ID: DAI04
Language: English
Level: 300
Duration: 30mins
Speakers: Sandipan Bhaumik, Senior Specialist Solution Architect, Analytics, AWS & Subham Rakshit, Senior Specialist Solution Architect, Analytics, AWS

Connecting to all the data systems across your organization, at a scale and speed the business runs is crucial for innovation. To enable such integration, you have to build complex ETL (extract, transform, load) pipelines. These pipelines are used to extract data from a variety of sources, transform it into a usable format, and then load it into a central repository such as a data warehouse or data lake. You have to spend a significant amount of time and money building these ETL pipelines. To keep up with the dynamic nature of data and the speed at which your business needs to move, data integration has to be seamless. To make this easier, AWS is investing in a Zero ETL future where you never have to manually build an ETL pipeline again. In this session we will explore how you can leverage some of the features in AWS data and analytics services to minimize the time data engineers spend in ETL development. We will discuss some of the features we announced in re:Invent on integration between services like Amazon Aurora, Amazon Redshift, Amazon S3 and Apache Spark workloads in Amazon EMR and AWS Glue. If you are a solution architect, data architect, data engineer, developer or in a role that aligns with data integration tasks, this session is for you.

Session ID: DAI05
Language: English
Level: 200
Duration: 30mins
Speaker: Navaneeth Ramakrishnan, Senior UKIR SAP Specialist Solutions Architect, AWS

Cloud is transformational for our customers when they can derive new business insights from a combination of both SAP and non-SAP data. SAP on AWS customers can leverage AWS analytics, artificial intelligence, and machine learning capabilities to get near real-time insights which move the needle of their business performance; improving operational efficiency; increasing supply chain efficiency; generating new revenue streams; and detecting and responding to business risks at a faster clip. Watch a demo on how to transfer data from SAP application to a serverless S3 data lake using AppFlow, catalog using AWS Glue, execute queries using AWS Athena, and visualize it on QuickSight.

Session ID: SGD01
Language: English
Level: 200
Duration: 30mins
Speakers: Ismail Makhlouf, Senior Specialist Solutions Architect - Data Analytics, AWS

Unlocking the value from data can benefit every organization and every industry. Many organizations are sitting on a treasure trove of data, but don’t know where to start to get value out of it. In this session we will learn how AWS enables leading use cases of putting data to work. We will see real-world examples of how organizations are taking advantage of data-driven insights to improve decision-making, optimize processes, save costs, and deliver better customer experiences.

Session ID: SGD02
Language: English
Level: 200
Duration: 30mins
Speakers: Meena Morrish, Senior Solutions Architect, AWS & Javier Poveda-Panter, Data Scientist, AWS

Come and join us to hear the story of how Bundesliga got their enthusiastic fans closer to their players and built amazing story telling behind every match events using data and AWS machine learning.

Session ID: SGD03
Language: English
Level: 200
Duration: 30mins
Speaker: Zamira Jaupaj, Solutions Architect, AWS

Organizations of all sizes have recognized that data drives innovation and allows them to build experiences for their customers. To gain value from your data, it needs to be accessible by people and systems that need it for analytics. With AWS analytics, data producers (data engineers and data scientists) can share data securely with data consumers (analysts and business users) across the organization while remaining compliant with security and governance measures imposed by the organization. In this session, learn how organizations can apply AWS analytics services to discover, access, and share their data across organizational boundaries.

Session ID: SGD04
Language: English
Level: 200
Duration: 30mins
Speaker: Ed Gummett (he/him), Senior Solutions Architect, Storage Technologies, AWS

Resilience and availability are top of mind for organisations choosing a data service in the cloud. This session will explain the ways that AWS storage services are architected with built-in resilience, and will share best practices for configuring our storage services for your own resilience and high availability needs. Services covered include S3, EBS, FSx, EFS and Backup including how to employ AWS Availability Zones and Regions, contingency planning and features to manage workload failover, testing of your resiliency plan, configuring data encryption, and the role of data protection and recovery planning.

Session ID: SGD05
Language: English
Level: 200
Duration: 30mins
Speaker: Miguel Angel Huerta, Data Analytics Consultant, AWS

Governance let you move faster to reach your targets under certain parameters set and managed by yourself. Data and predictive Models are not an exception so making the most of them under a sound Governance umbrella will let you to make better, faster and safe decisions based on your Data. During this session we will cover at first hand why we need a Top-down approach to the Data and ML Governance to set a long-term data-driven strategy not guided just by technology, but people and processes as well in common approach to successful transformation path. Secondly, a bottom-up approach where we can effectively put the focus on the architecture where the strategy will rely on under two different perspectives: Data and Machine Learning models. The core of not only predictive models but also analytical studies is trusted data, to govern the whole life-cycle of your Data ensuring this asset is available in the expected quality and over a secure framework will set your data analysis at the next level. AWS Lakeformation in combination with AWS Glue and Amazon S3 help to build a Modern Data Platform to handle this two governance perspectives The success of a robust predictive model resides in large degree on this governed data, the next step in the ML Governance is to ensure the optimal performance and operation of the predictive data during the entire model life-cycle. AWS Sagemaker helps to handle this process covering all the Data Science phases in modelling: Data Exploration, Feature Selection, Model Training, Model Evaluation, Model Deployment, Model Monitoring and Model Maintenance. Please join this session to see how with AWS you can govern end-to-end data and machine learning models in detail making use of all this AWS services.

Session ID: SUP01
Language: English
Level: 300
Duration: 30mins
Speaker: Ceren Tahtasiz, Startup Solutions Architect, AWS

As a startup, get your analytics faster without worrying about data warehouse management and maximizing your utilization. Amazon Redshift Serverless lets you get started in seconds and run data warehousing and analytics workloads at scale as your business grows. In this session, learn how Amazon Redshift intelligently scales underlying resources to deliver consistently high performance, efficient costs and simplified operations for even the most demanding workloads.

Session ID: SUP02
Language: English
Level: 300
Duration: 30mins
Speaker: Fernando Gonçalves, Startup Solutions Architect, AWS

Streaming data can provide significant value by enabling real-time decision making, creating new revenue streams, and improving customer experiences. In this talk, we will focus on how to design a fully-managed data streaming solution for your startup. We will cover why data streaming matters, look into common use cases and run a design exercise that you can replicate to create a solution that best fits the needs of your startup.

Session ID: SUP03
Language: English
Level: 200
Duration: 30mins
Speaker: Jamila Jamilova, Solutions Architect, AWS

Data Lake and cost optimisation on AWS. Learn how to secure, monitor and manage your data lake performance.

Session ID: SUP04
Language: English
Level: 300
Duration: 30mins
Speaker: Jenny Vega, Startups Solutions Architect, AWS

State of the art deep learning models are getting larger and larger as we find that larger models generalize better. Training time and model size can create a bottleneck. To speed up training, developers can use data parallelism to train the model on a cluster of GPU instances. To train models that are memory-constrained, developers can use model parallelism to partition models across multiple GPUs. In this session, we talk about training and hosting large language models using parallelism on Amazon SageMaker.

Session ID: SUP05
Language: English
Level: 200
Duration: 30mins
Speaker: Mathieu Desvé, Solutions Architect, AWS

You have some challenges with your relational databases? Well done, you made it! Your startup is growing like crazy! Now we need to look at your data strategy to reduce cost, improve performance and be able to scale further. In this session, we want to be practical and demonstrate which workload can be migrated to a purpose-built database and demonstrate how to do it. We will also step a foot in the Big Data world.

Session ID: T&C01
Language: English
Level: 200
Duration: 45mins (9:35 - 10:20 GMT)
Speaker: Marco Tamassia, AWS Senior Technical Trainer, AWS

In this session you will learn the characteristics that a modern data analytics architecture on AWS should have. As an example, the speaker will show you a demo about a serverless event-driven streaming analytics architecture to perform clickstream analysis. The architecture will be showed service by service.

Session ID: T&C02
Language: English
Level: 200
Duration: 120mins (10:25 - 12:25 GMT)
Speaker: Laura Verghote, Solutions Architect, AWS & Marco Tamassia, AWS Senior Technical Trainer, AWS

Put your cloud-skills to the test by solving challenges that emulate real AWS use-cases! In this session we will be conducting a AWS JAM, giving you the unique chance to test your AWS knowledge and problem-solving skills in a free lab-environment. The JAM works via a 'Play - Learn - Validate' structure and gives you the chance to solve real-world problems related to AI/ML and data. Win amazing prizes like classroom training, certification vouchers and Amazon devices! See you there!

Session ID: FPAQA
Language: English
Duration: 40mins

Session ID: ANAQA
Language: English
Duration: 40mins

Session ID: MLTQA
Language: English
Duration: 40mins

Session ID: DAIQA
Language: English
Duration: 40mins

Session ID: MLTQA
Language: English
Duration: 40mins

Session ID: SUPQA
Language: English
Duration: 40mins

Session ID: T&CQA
Language: English
Duration: 40mins