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Modernize log analytics with Amazon Elasticsearch Service
Amazon Elasticsearch Service is uniquely positioned to handle log analytics workloads. With a multitude of open-source and AWS-native service options, users can assemble effective log data ingestion pipelines and couple these with Amazon Elasticsearch Service to build a robust, cost-effective log analytics solution. This session reviews patterns and frameworks leveraged by companies such as Capital One to build an end-to-end log analytics solution using Amazon Elasticsearch Service.
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1

Achieve compliance as code using AWS Config
Many companies in regulated industries have achieved compliance requirements using AWS Config. They also need a record of the incidents generated by AWS Config in tools such as ServiceNow for audits and remediation. In this session, learn how you can achieve compliance as code using AWS Config. Through the creation of a noncompliant Amazon EC2 machine, this demo shows how AWS Config triggers an incident into a governance, risk, and compliance system for audit recording and remediation. The session also covers best practices for how to automate the setup process with AWS CloudFormation to support many teams.
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2

Amazon Location Service: Enable apps with location features
Location data is a vital ingredient in today’s applications, enabling use cases from asset tracking to geomarketing. Now, developers can use the new Amazon Location Service to add maps, tracking, places, geocoding, and geofences to applications, easily, securely, and affordably. Join this session to see how to get started with the service and integrate high-quality location data from geospatial data providers Esri and HERE. Learn how to move from experimentation to production quickly with location capabilities. This session can help developers who require simple location data and those building sophisticated asset tracking, customer engagement, fleet management, and delivery applications.
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3

Automate, track, and manage tasks with Amazon Connect Tasks
In this session, learn how Amazon Connect Tasks makes it easy for you to prioritize, assign, and track all the tasks that agents need to complete, including work in external applications needed to resolve customer issues (such as emails, cases, and social posts). Tasks provides a single place for agents to be assigned calls, chats, and tasks, ensuring agents are focused on the highest-priority work. Also, learn how you can also use Tasks with Amazon Connect’s workflow capabilities to automate task-related actions that don’t require agent interaction. Come see how you can use Amazon Connect Tasks to increase customer satisfaction while improving agent productivity.
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4

Solve customer issues quickly with Amazon Connect Wisdom
New agent-assist capabilities from Amazon Connect Wisdom make it easier and faster for agents to find the information they need to solve customer issues in real time. In this session, see how agents can use simple ML-powered search to find information stored across knowledge bases, wikis, and FAQs, like Salesforce and ServiceNow. Join the session to hear Traeger Pellet Grills discuss how it’s using these new features, along with Contact Lens for Amazon Connect, to deliver real-time recommendations to agents based on issues automatically detected during calls.
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5

Introducing Amazon Managed Service for Grafana
Grafana is a popular, open-source data visualization tool that enables you to centrally query and analyze observability data across multiple data sources. Learn how the new Amazon Managed Service for Grafana, announced with Grafana’s parent company Grafana Labs, solves common observability challenges. With the new fully managed service, you can monitor, analyze, and alarm on metrics, logs, and traces while offloading the operational management of security patching, upgrading, and resource scaling to AWS. This session also covers new Grafana capabilities such as advanced security features and native AWS service integrations to simplify configuration and onboarding of data sources.
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6

Introducing Amazon Managed Service for Prometheus (AMP)
Prometheus is a popular open-source monitoring and alerting solution optimized for container environments. Customers love Prometheus for its active open-source community and flexible query language, using it to monitor containers across AWS and on-premises environments. Amazon Managed Service for Prometheus is a fully managed Prometheus-compatible monitoring service. In this session, learn how you can use the same open-source Prometheus data model, existing instrumentation, and query language to monitor performance with improved scalability, availability, and security without having to manage the underlying infrastructure.
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7

Announcing AWS IoT Core for LoRaWAN
Today, enterprises use low-power, long-range wide-area network (LoRaWAN) connectivity to transmit data over long ranges, through walls and floors of buildings, and in commercial and industrial use cases. However, this requires companies to operate their own LoRa network server (LNS). In this session, learn how you can use LoRaWAN for AWS IoT Core to avoid time-consuming and undifferentiated development work, operational overhead of managing infrastructure, or commitment to costly subscription-based pricing from third-party service providers.
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8

AWS CloudShell: The fastest way to get started with AWS CLI
AWS CloudShell is a free, browser-based shell available from the AWS console that provides a simple way to interact with AWS resources through the AWS command-line interface (CLI). In this session, see an overview of both AWS CloudShell and the AWS CLI, which when used together are the fastest and easiest ways to automate tasks, write scripts, and explore new AWS services. Also, see a demo of both services and how to quickly and easily get started with each.
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9

Introducing AWS IoT SiteWise Edge
Industrial organizations use AWS IoT SiteWise to liberate their industrial equipment data in order to make data-driven decisions. Now with AWS IoT SiteWise Edge, you can collect, organize, process, and monitor your equipment data on premises before sending it to local or AWS Cloud destinations—all while using the same asset models, APIs, and functionality. Learn how you can extend the capabilities of AWS IoT SiteWise to the edge with AWS IoT SiteWise Edge.
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10

AWS Fault Injection Simulator: Fully managed chaos engineering service
AWS Fault Injection Simulator is a fully managed chaos engineering service that helps you improve application resiliency by making it easy and safe to perform controlled chaos engineering experiments on AWS. In this session, see an overview of chaos engineering and AWS Fault Injection Simulator, and then see a demo of how to use AWS Fault Injection Simulator to make applications more resilient to failure.
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11

Data lakes: Easily build, secure, and share with AWS Lake Formation
Organizations are breaking down data silos and building petabyte-scale data lakes on AWS to democratize access to thousands of end users. Since its launch, AWS Lake Formation has accelerated data lake adoption by making it easy to build and secure data lakes. In this session, AWS Lake Formation GM Mehul A. Shah showcases recent innovations enabling modern data lake use cases. He also introduces a new capability of AWS Lake Formation that enables fine-grained, row-level security and near-real-time analytics in data lakes.
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12

Understand ML model predictions and biases with Amazon SageMaker Clarify
Machine learning (ML) models may generate predictions that are not fair, whether because of biased data, a model that contains bias, or bias that emerges over time as real-world conditions change. Likewise, closed-box ML models are opaque, making it difficult to explain to internal stakeholders, auditors, external regulators, and customers alike why models make predictions both overall and for individual inferences. In this session, learn how Amazon SageMaker Clarify is providing built-in tools to detect bias across the ML workflow including during data prep, after training, and over time in your deployed model.
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13

Proactively monitor the health of your business using Amazon Lookout for Metrics
Finding unexpected anomalies in metrics can be challenging. Some organizations look for data that falls outside of arbitrary ranges; if the range is too narrow, they miss important alerts, and if it is too broad, they receive too many false alerts. In this session, learn about Amazon Lookout for Metrics, a fully managed anomaly detection service that is powered by machine learning and over 20 years of anomaly detection expertise at Amazon to quickly help organizations detect anomalies and understand what caused them. This session guides you through setting up your own solution to monitor for anomalies and showcases how to deliver notifications via various integrations with the service.
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14

Improve application availability with ML-powered insights using Amazon DevOps Guru
As applications become increasingly distributed and complex, developers and IT operations teams need more automated practices to maintain application availability and reduce the time and effort spent detecting, debugging, and resolving operational issues manually. In this session, discover Amazon DevOps Guru, an ML-powered cloud operations service, informed by years of Amazon.com and AWS operational excellence, that provides an easy and automated way to improve an application’s operational performance and availability. See how you can transform your IT operations and reduce mean time to recovery (MTTR) with contextual insights.
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15

ML-powered voice authentication with Amazon Connect Voice ID
Amazon Connect Voice ID provides real-time caller authentication that makes voice interactions in contact centers more secure and efficient. Voice ID uses machine learning to verify the identity of genuine customers by analyzing a caller’s unique voice characteristics. This allows contact centers to use an additional security layer that doesn’t rely on the caller answering multiple security questions, and it makes it easy to enroll and verify customers without disrupting the natural flow of the conversation. Join this session to see how fast and secure ML-based voice authentication can power your contact center.
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16

Introducing EC2 G4ad instances for graphics-intensive apps
G4ad instances feature the latest AMD Radeon Pro V520 GPUs and second-generation AMD EPYC processors. These new instances deliver the best price performance in Amazon EC2 for graphics-intensive applications such as virtual workstations, game streaming, and graphics rendering. This session dives deep into these instances, ideal use cases, and performance benchmarks, and it provides a demo.
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17

An introduction to Amazon ECS Anywhere
New capability that enables deployment of Amazon ECS tasks on customer-managed infrastructure.
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18

Amazon Aurora Serverless v2: Instant scaling for demanding workloads
Amazon Aurora Serverless is an on-demand, auto scaling configuration of Amazon Aurora that automatically adjusts database capacity based on application demand. With Amazon Aurora Serverless v2, you can now scale database workloads instantly from hundreds to hundreds of thousands of transactions per second and adjust capacity in fine-grained increments to provide just the right amount of database resources. This session dives deep into Aurora Serverless v2 and shows how it can help you operate even the most demanding database workloads worry-free.
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19

Bringing AWS benefits to all Apple developers with EC2 Mac instances
Apple delights its customers with stunning devices like iPhones, iPads, MacBooks, Apple Watches, and Apple TVs, and developers want to create applications that run on iOS, macOS, iPadOS, tvOS, watchOS, and Safari. In this session, learn how Amazon is innovating to improve the development experience for Apple applications. Come learn how AWS now enables you to develop, build, test, and sign Apple applications with the flexibility, scalability, reliability, and cost benefits of Amazon EC2.
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20

Enable predictive maintenance for your industrial equipment: Amazon Monitron
When industrial equipment breaks down, this means costly downtime. To avoid this, you perform maintenance at regular intervals, which is inefficient and increases your maintenance costs. Predictive maintenance allows you to plan the required repair at an optimal time before a breakdown occurs. However, predictive maintenance solutions can be challenging and costly to implement given the high costs and complexity of sensors and infrastructure. You also have to deal with the challenges of interpreting sensor data and accurately detecting faults in order to send alerts. Come learn how Amazon Monitron helps you solve these challenges by offering an out-of-the-box, end-to-end, cost-effective system.
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21

Introduction to AQUA for Amazon Redshift 
As data grows, we need innovative approaches to get insight from all the information at scale and speed. AQUA is a new hardware-accelerated cache that uses purpose-built analytics processors to deliver up to 10 times better query performance than other cloud data warehouses by automatically boosting certain types of queries. It’s available in preview on Amazon Redshift RA3 nodes in select regions at no extra cost and without any code changes. Attend this session to understand how AQUA works and which analytic workloads will benefit the most from AQUA.
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22

Amazon Lookout for Vision
Figuring out if a part has been manufactured correctly, or if machine part is damaged, is vitally important. Making this determination usually requires people to inspect objects, which can be slow and error-prone. Some companies have applied automated image analysis—machine vision—to detect anomalies. While useful, these systems can be very difficult and expensive to maintain. In this session, learn how Amazon Lookout for Vision can automate visual inspection across your production lines in few days. Get started in minutes, and perform visual inspection and identify product defects using as few as 30 images, with no machine learning (ML) expertise required.
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23

AWS Proton: Automating infrastructure provisioning & code deployments
AWS Proton is a new service that enables infrastructure operators to create and manage common container-based and serverless application stacks and automate provisioning and code deployments through a self-service interface for their developers. Learn how infrastructure teams can empower their developers to use serverless and container technologies without them first having to learn, configure, and maintain the underlying resources.
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24

Introducing Babelfish for Aurora PostgreSQL
Migrating applications from SQL Server to an open-source compatible database can be time-consuming and resource-intensive. Solutions such as the AWS Database Migration Service (AWS DMS) automate data and database schema migration, but there is often more work to do to migrate application code. This session introduces Babelfish for Aurora PostgreSQL, a new translation layer for Amazon Aurora PostgreSQL that enables Amazon Aurora to understand commands from applications designed to run on Microsoft SQL Server. Learn how Babelfish for Aurora PostgreSQL works to reduce the time, risk, and effort of migrating Microsoft SQL Server-based applications to Aurora, and see some of the capabilities that make this possible.
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25

Make sense of health data with Amazon HealthLake
Over the past decade, we’ve witnessed a digital transformation in healthcare, with organizations capturing huge volumes of patient information. But this data is often unstructured and difficult to extract, with information trapped in clinical notes, insurance claims, recorded conversations, and more. In this session, explore how the new Amazon HealthLake service removes the heavy lifting of organizing, indexing, and structuring patient information to provide a complete view of each patient’s health record in the FHIR standard format. Come learn how to use prebuilt machine learning models to analyze and understand relationships in the data, identify trends, and make predictions, ultimately delivering better care for patients.
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26

Introducing Amazon QuickSight Q: Ask questions on data & get answers in seconds
When business users want to ask new data questions that are not answered by existing business intelligence (BI) dashboards, they rely on BI teams to create or update data models and dashboards, which can take several weeks to complete. In this session, learn how Merlin lets users simply enter their questions on the Merlin search bar and get answers in seconds. Merlin uses natural language processing and semantic data understanding to make sense of the data. It extracts business terminologies and intent from users’ questions, retrieves the corresponding data from the source, and returns the answer in the form of a number, chart, or table in Amazon QuickSight.
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27

Amazon ECR Public: Share, discover, deploy, and monetize container apps easily
When developers publish images publicly for anyone to find and use—whether for free or under license—they must make copies of common images and upload them to public websites and registries that do not offer the same availability commitment as Amazon ECR. This session explores a new Amazon public registry, Amazon ECR Public, built with AWS experience operating Amazon ECR. Here, developers can share georeplicated container software worldwide for anyone to discover and download. Developers can quickly publish public container images with a single command. Learn how anyone can browse and pull container software for use in their own applications.
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28

Detect abnormal equipment behavior by analyzing sensor data
Industrial companies are constantly working to avoid unplanned downtime due to equipment failure and to improve operational efficiency. Over the years, they have invested in physical sensors, data connectivity, data storage, and dashboarding to monitor equipment and get real-time alerts. Current data analytics methods include single-variable thresholds and physics-based modeling approaches, which are not effective at detecting certain failure types and operating conditions. In this session, learn how Amazon Lookout for Equipment uses data from your sensors to detect abnormal equipment behavior so that you can take action before machine failures occur and avoid unplanned downtime.
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29

Real-time ML analytics with Contact Lens for Amazon Connect
In this session, learn how Contact Lens for Amazon Connect enables your contact center supervisors to understand the sentiment of customer conversations, identify call drivers, evaluate compliance with company guidelines, and analyze trends. This can help supervisors train agents, replicate successful interactions, and identify crucial company and product feedback. Your supervisors can conduct fast full-text search on all transcripts to quickly troubleshoot customer issues. With real-time capabilities, you can get alerted to issues during live customer calls and deliver proactive assistance to agents while calls are in progress, improving customer satisfaction. Join this session to see how real-time ML-powered analytics can power your contact center.
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30

Introducing 15 new Local Zones for ultra-low latency compute across the US
AWS Local Zones places compute, storage, database, and other select services closer to locations where no AWS Region exists today. Last year, AWS launched the first two Local Zones in Los Angeles, and organizations are using Local Zones to deliver applications requiring ultra-low-latency compute. AWS is launching Local Zones in 15 metro areas to extend access across the contiguous US. In this session, learn how you can run latency-sensitive portions of applications local to end users and resources in a specific geography, delivering single-digit millisecond latency for use cases such as media and entertainment content creation, real-time gaming, reservoir simulations, electronic design automation, and machine learning.
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31

Accelerate data preparation with Amazon SageMaker Data Wrangler
Preparing training data can be tedious. Amazon SageMaker Data Wrangler provides a faster, visual way to aggregate and prepare data for machine learning. In this session, learn how to use SageMaker Data Wrangler to connect to data sources and use prebuilt visualization templates and built-in data transforms to streamline the process of cleaning, verifying, and exploring data without having to write a single line of code. See a demonstration of how SageMaker Data Wrangler  can be used to perform simple tasks as well as more advanced use cases. Finally, see how you can take your data preparation workflows into production with a single click.
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32

Increase availability with AWS observability solutions
To provide access to critical resources when needed and also limit the potential financial impact of an application outage, a highly available application design is critical. In this session, learn how you can use Amazon CloudWatch and AWS X-Ray to increase the availability of your applications. Join this session to learn how AWS observability solutions can help you proactively detect, efficiently investigate, and quickly resolve operational issues. All of which help you manage and improve your application’s availability.
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33

Personalized service with Amazon Connect Customer Profiles
Your customers expect a fast, frictionless, and personalized customer service experience. In this session, learn about Amazon Connect Customer Profiles—a new unified customer profile capability to allow agents to provide more personalized service during a call. Customer Profiles automatically brings together customer information from multiple applications, such as Salesforce, Marketo, Zendesk, ServiceNow, and Amazon Connect contact history, into a unified customer profile. With Customer Profiles, agents have the information they need, when they need it, directly in their agent application, resulting in improved customer satisfaction and reduced call resolution times (by up to 15%).
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