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Summarized Videos

AWS-Online-Tech-Talks

Use Amazon SageMaker to Build Generative AI Applications AWS Virtual Workshop

The AWS virtual workshop showcases the use of Amazon SageMaker to build generative AI applications. The speakers discuss the concept of prompt engineering to obtain specific responses from a generative model and demonstrate the use of SageMaker console and its proprietary and available models. They also touch upon the training process of custom models and the installation of additional packages using containers, data parallelism, and accelerators like Trainium and Inferentia. The speakers highlight the optimization options available for inference containers to parallelize models across multiple GPUs and reduce memory requirements. Overall, the workshop emphasizes experimenting with different parameters and options to select the right model for each use case.

Building Data Mesh Architectures on AWS AWS Online Tech Talks

The video discusses the limitations of a centralized data lake model and introduces the concept of a data mesh, a decentralized domain-driven modern data architecture that allows autonomous data workspaces for producers and consumers. The four key pillars of the data mesh concept are discussed, as well as the AWS analytics services available for building these patterns, such as Amazon S3, lake formation, glue, and EMR. The importance of maintaining a full lifecycle of data and cataloging all datasets is explored, with a focus on federated governance models to allow for seamless control of access patterns and entitlement sharing between different accounts, organizations, domains, and consumers. The speaker then demonstrates how AWS-enabled data mesh architectures allow for ownership to data domain owners and easy sharing and understanding of data sets among different consumers while providing strong security governance.

Build Flutter apps with AWS Amplify for mobile, web, and desktop platforms AWS Online Tech Talks

The video discusses Flutter, its advantages, and AWS Amplify for mobile, web, and desktop platforms. The speaker provides tips for setting up and installing Flutter and explains how AWS Amplify helps developers build full-stack applications. They show how to use the Amplify CLI to configure and maintain a backend, add authentication using Amplify UI and Cognito, and implement persistence using Amplify DataStore. The video also demonstrates Amplify Starter, which allows for easy project creation and initialization.

Build an intelligent search application in a few clicks with Amazon Kendra AWS Online Tech Talks

Amazon Kendra is an intelligent search tool, detailed in this video, that allows users to ask natural language queries and receive extracted answers from any indexed document or webpage. Kendra offers incremental deployment and easy setup using built-in connectors to popular data sources, making it an efficient enterprise searching application. Three primary use cases for Kendra are enterprise search, customer service, and embedded search. Amazon Kendra's analytics dashboard provides detailed search metrics, including top queries, top documents clicked, search click-through rate, and zero results, to help users understand search trends and areas for improvement. The video provides a demo on how to create an intelligent search application with Kendra and showcases document enrichment features, FAQs, and custom synonyms for better search results, a publisher for customization, and tips on running a quick Proof of Concept.

DynamoDB design patterns with GraphQL APIs and AppSync AWS Online Tech Talks

The video discusses how AWS AppSync allows developers to create a single endpoint to connect any type of application to data sources in an AWS account and hide the backend complexity from clients. The presenter goes on to discuss the use of GraphQL with AWS AppSync, introducing the concept of a schema and how it allows for multiple fields to be requested in one query, as well as the use of resolvers for defining business logic to retrieve fields from data sources. The section then covers the benefits of using AWS AppSync with DynamoDB, a NoSQL database that provides consistent performance through horizontal scaling, and the importance of primary and partition keys in data modeling for optimal performance. Finally, the speaker discusses the trade-offs between using DynamoDB with multi-table and single table designs in GraphQL and AppSync, providing sample code to showcase their differing levels of complexity and highlighting the importance of understanding access patterns.

Build ML models using SageMaker Studio Notebooks AWS Virtual Workshop

The AWS Virtual Workshop on building ML models using SageMaker Studio Notebooks covers the essential capabilities of Amazon SageMaker Studio for building, training, and deploying machine learning models. SageMaker Studio provides pre-built images for popular machine-learning frameworks, including MXNet, PyTorch, and TensorFlow. The workshop demonstrates how to explore data using SageMaker Studio Notebooks, install additional packages to the kernel for libraries such as Plotly and Matplotlib, and compute histograms and apply data transformations. It also covers SageMaker training jobs, experiments, and trials, and logging and comparing metrics using regex for the standard output of the container. The workshop concludes by exploring SageMaker Studio features such as SageMaker projects, pipelines, experiments, trials, model registry, compiled jobs, and edge packaging for machine learning (ML) at the edge.

How to use Amazon S3 Batch Operations

This video explains the Amazon S3 Batch Operations feature, which allows users to perform large-scale operations on millions or billions of objects with just a few clicks, without the need to build and maintain an application. Users can set up permissions, create a job, and manage and track it within the AWS Management console. The process of creating a job using the feature is explained in detail, including selecting a bucket, creating an S3 inventory report, and specifying the desired operations. Additionally, users can manage jobs by filtering them based on status types, regions, and other parameters, and prioritize, confirm, cancel, or clone a job as needed.

AWS Databases in 15 Getting Started with ElastiCache for Redis Performance & Cost Optimization

In the video, Tom Kuehle highlights the importance of providing real-time application performance and optimizing expenditures, and how databases aren't designed for modern applications, often requiring a significant investment in performance. He introduces Amazon ElastiCache, a caching service that acts as a go-between the client and the database, providing in-memory speed and reducing pressure on the primary data store, allowing for more flexibility and cost-effectiveness in boosting application performance. The video demonstrates scenarios in which using ElastiCache for Redis improves query execution speed by more than twice and allows for four times more work to be done in less than half the time compared to going directly to the database.

Analytics in 15: Serverless Data Streaming Workloads with Amazon Kinesis

In "Analytics in 15: Serverless Data Streaming Workloads with Amazon Kinesis", Nihar Sheth, a product manager at Amazon Kinesis, explains the importance of real-time analytics and the challenges of implementing open-source frameworks for streaming workloads. He breaks down the five key components of streaming architecture and highlights three Amazon Kinesis services that simplify the process of building serverless streaming workflows. The video also provides a use case on how Amazon Kinesis Data Streaming Services can be used by a medical device company with millions of IoT devices. The video concludes by encouraging viewers to check out Kinesis Data Streaming Services references and apply this knowledge to their own use cases.

Getting started with Amazon Aurora Global Database

I apologize for misunderstanding the input earlier. In this video, Rohan Bhatia from AWS explains the features and benefits of Amazon Aurora Global Databases. With Global Databases, users get a fully managed, low-cost solution for replicating data across regions and recovering quickly from disasters with automated failover mechanisms. The separation of compute and storage layers enables independent scaling and reducing compute costs. Users can use Global Databases for disaster recovery setups, scale low-latency global reads, build global applications, and integrate with AWS Secrets Manager and Identity and Access Management Service for improved security and availability. The demo shows how to create an Aurora database and promote it to a Global Database, create a proxy, and scale global reads with RDS Proxy while reducing the number of connections to the database instance.