The document provides a summary of new and updated AWS services being announced at an AWS conference. It includes summaries of new compute, storage, database, analytics, networking, serverless, AI/ML, and security services as well as updates to existing services. Many of the new services are currently in preview or beta stages. The document also advertises upcoming sessions that will provide more details on the new services.
Sensors Everywhere: Unlocking the Promise of IoT | AWS Public Sector Summit 2016Amazon Web Services
With the growth of IoT, more and more objects can be classified as sensors. The police officer with the body camera, the trashcan monitoring waste, and the ambulance rushing to the hospital--all of these “things” can, in fact, be “sensors” that collect vital data. In this session, we provide an overview of innovations within IoT and mobile technology, and explore how the AWS platform is playing a role in the transformative new technology wave.
My slides from the re:Invent Recap Conferences.
The AWS Well-Architected Framework enables customers to understand best practices around security, reliability, performance, and cost optimisation when building systems on AWS. This approach helps customers make informed decisions and weigh the pros and cons of application design patterns for the cloud. In this session, you'll learn how to follow AWS guidelines and best practices. By developing a strategy based on Amazon Web Services's Well-Architected Framework, you will be able to significantly increase the frequency of code deployments and reduce deployment times. As a result, you will be able to deliver more scalable, dynamic and resilient applications.
Power up Your AWS Data Lake and Warehouse with Trusted Data (Sponsored by Tal...Amazon Web Services
Do data quality issues, demanding business needs and increasingly stringent regulations sound familiar? You may have moved your data to a data lake on Amazon S3 or a data warehouse on Amazon Redshift, but how do you deliver the ‘single source of trust’ needed to make decisions with scale and speed? We will share best practices that have helped our customers overcome these challenges. Betfair Pty Ltd, the world’s largest online betting exchange, will also share their experience using Talend and Redshift in enabling a analytics data warehouse with strong data quality and light governance practices.
Getting started with streaming analytics: Deep Divejavier ramirez
Now that we know how to create simple streaming analytics pipelines, it is time to learn something more interesting. In this session I will show you how to add Complex Event Processing to your Apache Flink (or Kinesis Data Analytics) application using JAVA. For those of you that prefer SQL, I will show you how to run streaming analytics using only SQL.
0 best practices for architecting for the cloud
1. Enable Scalability
2. Use Disposable Resources
3. Automate Your Environment
4. Loosely Couple Your Components
5. Design Services, Not Servers
6. Choose the Right Database Solutions
7. Avoid Single Points of Failure
8. Optimize for Cost
9. Use Caching
10. Secure Your Infrastructure Everywhere
Speaker: Anson Shen
Sensors Everywhere: Unlocking the Promise of IoT | AWS Public Sector Summit 2016Amazon Web Services
With the growth of IoT, more and more objects can be classified as sensors. The police officer with the body camera, the trashcan monitoring waste, and the ambulance rushing to the hospital--all of these “things” can, in fact, be “sensors” that collect vital data. In this session, we provide an overview of innovations within IoT and mobile technology, and explore how the AWS platform is playing a role in the transformative new technology wave.
My slides from the re:Invent Recap Conferences.
The AWS Well-Architected Framework enables customers to understand best practices around security, reliability, performance, and cost optimisation when building systems on AWS. This approach helps customers make informed decisions and weigh the pros and cons of application design patterns for the cloud. In this session, you'll learn how to follow AWS guidelines and best practices. By developing a strategy based on Amazon Web Services's Well-Architected Framework, you will be able to significantly increase the frequency of code deployments and reduce deployment times. As a result, you will be able to deliver more scalable, dynamic and resilient applications.
Power up Your AWS Data Lake and Warehouse with Trusted Data (Sponsored by Tal...Amazon Web Services
Do data quality issues, demanding business needs and increasingly stringent regulations sound familiar? You may have moved your data to a data lake on Amazon S3 or a data warehouse on Amazon Redshift, but how do you deliver the ‘single source of trust’ needed to make decisions with scale and speed? We will share best practices that have helped our customers overcome these challenges. Betfair Pty Ltd, the world’s largest online betting exchange, will also share their experience using Talend and Redshift in enabling a analytics data warehouse with strong data quality and light governance practices.
Getting started with streaming analytics: Deep Divejavier ramirez
Now that we know how to create simple streaming analytics pipelines, it is time to learn something more interesting. In this session I will show you how to add Complex Event Processing to your Apache Flink (or Kinesis Data Analytics) application using JAVA. For those of you that prefer SQL, I will show you how to run streaming analytics using only SQL.
0 best practices for architecting for the cloud
1. Enable Scalability
2. Use Disposable Resources
3. Automate Your Environment
4. Loosely Couple Your Components
5. Design Services, Not Servers
6. Choose the Right Database Solutions
7. Avoid Single Points of Failure
8. Optimize for Cost
9. Use Caching
10. Secure Your Infrastructure Everywhere
Speaker: Anson Shen
In this hands-on workshop, the idea is that we provide attendees with sample code to simulate telemetry from devices (Wind Turbines), sample training data to train the machine learning models. AWS IoT rules engine will use machine learning for predicting failures and control the device when failure is predicted.
Performing serverless analytics in AWS Glue - ADB202 - Chicago AWS SummitAmazon Web Services
Serverless computing offers a fundamentally new and more efficient abstraction for architecting systems in the cloud. Instead of managing VMs, developers submit “functions” or scripts that execute behind the scenes with minimal required resources. In this session, we present an overview of serverless computing and introduce AWS Glue analytics features for data science, data discovery, data cleaning/transformation, and data-lake management. We also demonstrate how, unlike other analytic systems, AWS Glue enables you to run arbitrary Python or Spark code that automatically scales, with no limitations on runtime, through your favorite notebooks.
Optimizing data lakes with Amazon S3 - STG302 - New York AWS SummitAmazon Web Services
Data comes in many different forms that don’t easily fit into a traditional database structure. This is where data lakes help, enabling you to store vast amounts of data in its raw form. In this session, AWS experts dive into the benefits of Amazon S3 for building and managing data lakes in the AWS Cloud. Learn about the Amazon S3 integrations with the AWS analytics suite and Amazon FSx for Lustre. Also learn how to seamlessly run big data analytics, high performance computing applications, machine learning training models, media data processing workloads, and more, across your Amazon S3 data lakes.
Monitoring, Hold the Infrastructure - Getting the Most out of AWS Lambda - AW...Amazon Web Services
Just as we got a hang of monitoring our server-based applications, they take away the server. How do you monitor something that doesn’t exist? What metrics matter most in a serverless world? In this session, we will look at how applications are different in a AWS Lambda-based world and how to monitor them. Join us as we work our way through the stack and demonstrate how to capture the health and performance of your services.
Replicate and Manage Data Using Managed Databases and Serverless Technologies Amazon Web Services
If you have disparate datasets within your data center and on AWS, it can be challenging to manage all of them while you extract and analyze data. In this workshop, we use AWS managed database services, migration tools, and serverless technologies to replicate data and manage it in the cloud. We replicate an on-premises database to Amazon Aurora using AWS Database Migration Service, and we show you how Aurora Serverless can automatically scale your database and reduce your database costs. Ensure that you have an AWS account, and familiarize yourself with the AWS Management Console at least a day before the workshop. You don't need any credit on the account.
ENT201 Simplifying Microsoft Architectures with AWS ServicesAmazon Web Services
Learn how to architect fully available and scalable Microsoft solutions and environments in AWS. Find out how Microsoft solutions can leverage various AWS services to achieve more resiliency, replace unnecessary complexity, simplify architecture, provide scalability, and introduce DevOps concepts, such as compliance, governance, automation, and repeatability. Also, plan authentication and authorization, and explore various hybrid scenarios with other cloud environment and on-premises solutions or infrastructure. Learn about common architecture patterns for network design, Microsoft Active Directory, and business productivity solutions like Dynamics AX, CRM, and Microsoft SharePoint, also common scenarios for custom .NET, .NET Core with Microsoft SQL deployments and migrations.
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning (Amazon ML) services work together to build a successful data lake for various roles, including data scientists and business users.
Getting started with streaming analytics: Setting up a pipelinejavier ramirez
In this session I will show you how to create a simple streaming analytics pipeline, first using open source tools and developing locally, then moving to a VM, then moving to fully managed AWS services. The session will serve as an introduction to some details of Apache Kafka, Apache Flink, ElasticSearch, Amazon Managed Streaming for Kafka, Kinesis Data Analytics, and Amazon ElasticSearch. It will be an almost slideless presentation, as I will spent most of the time at the command line and the IDE.
All Databases Are Equal, But Some Databases Are More Equal than Others: How t...javier ramirez
Data comes in different shapes and sizes, at different speeds, and with different processing needs. AWS offers managed databases covering SQL, NoSQL, in-memory, graphs, time series, and ledger databases. Learn which are the main use cases for each of those, which ones offer autoscaling or serverless capabilities, how you can start from scratch or via database migration services, and why Amazon Aurora is the fastest-growing service in the history of AWS.
Modernizing Your Microsoft Business Applications - CMP201 - Anaheim AWS SummitAmazon Web Services
Modernizing Microsoft applications to the cloud is becoming more important for customers as they continue to innovate their workloads. AWS has over 10 years' experience running Windows applications, and according to IDC, over 57% of all Windows operating systems in the cloud run on AWS. In this webinar, you learn why AWS is the preferred choice for customers around the world for running and scaling their Windows applications.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Hybrid Cloud Storage: Why HUSCO International Left Traditional Storage BehindAmazon Web Services
When CIO Eric Hanson joined HUSCO International’s leadership team, he quickly set about identifying opportunities for his organization to reduce storage costs and improve performance. Using his previous experience at a multi-location manufacturing firm, he determined that HUSCO International’s challenges with file latency, increasing storage costs, and inability to collaborate cross-site could be solved by transitioning from traditional storage using NetApp filers to a consolidated cloud infrastructure powered by Panzura and Amazon S3.
Register for our upcoming webinar to learn how HUSCO International is using Panzura and Amazon S3 to take advantage of cloud storage economics that has the potential to save the company hundreds of thousands of dollars annually.
AWS re:Invent 2016: Delighting Customers Through Device Data with Salesforce ...Amazon Web Services
The Internet of Things (IoT) produces vast quantities of data that promise a deep, always connected view into customer experiences through their devices. In this connected age, the question is no longer how do you gather customer data, but what do you do with all that data. How do you ingest at massive scale and develop meaningful experiences for your customers? In this session, you learn how Salesforce IoT Cloud works in concert with the AWS IoT engine to ingest and transform all of the data generated by every one of your customers, partners, devices, and sensors into meaningful action. You also see how customers are using Salesforce and AWS together to process massive quantities of data, build business rules with simple, intuitive tools, and engage proactively with customers in real time. Session sponsored by Salesforce.
In this hands-on workshop, the idea is that we provide attendees with sample code to simulate telemetry from devices (Wind Turbines), sample training data to train the machine learning models. AWS IoT rules engine will use machine learning for predicting failures and control the device when failure is predicted.
Performing serverless analytics in AWS Glue - ADB202 - Chicago AWS SummitAmazon Web Services
Serverless computing offers a fundamentally new and more efficient abstraction for architecting systems in the cloud. Instead of managing VMs, developers submit “functions” or scripts that execute behind the scenes with minimal required resources. In this session, we present an overview of serverless computing and introduce AWS Glue analytics features for data science, data discovery, data cleaning/transformation, and data-lake management. We also demonstrate how, unlike other analytic systems, AWS Glue enables you to run arbitrary Python or Spark code that automatically scales, with no limitations on runtime, through your favorite notebooks.
Optimizing data lakes with Amazon S3 - STG302 - New York AWS SummitAmazon Web Services
Data comes in many different forms that don’t easily fit into a traditional database structure. This is where data lakes help, enabling you to store vast amounts of data in its raw form. In this session, AWS experts dive into the benefits of Amazon S3 for building and managing data lakes in the AWS Cloud. Learn about the Amazon S3 integrations with the AWS analytics suite and Amazon FSx for Lustre. Also learn how to seamlessly run big data analytics, high performance computing applications, machine learning training models, media data processing workloads, and more, across your Amazon S3 data lakes.
Monitoring, Hold the Infrastructure - Getting the Most out of AWS Lambda - AW...Amazon Web Services
Just as we got a hang of monitoring our server-based applications, they take away the server. How do you monitor something that doesn’t exist? What metrics matter most in a serverless world? In this session, we will look at how applications are different in a AWS Lambda-based world and how to monitor them. Join us as we work our way through the stack and demonstrate how to capture the health and performance of your services.
Replicate and Manage Data Using Managed Databases and Serverless Technologies Amazon Web Services
If you have disparate datasets within your data center and on AWS, it can be challenging to manage all of them while you extract and analyze data. In this workshop, we use AWS managed database services, migration tools, and serverless technologies to replicate data and manage it in the cloud. We replicate an on-premises database to Amazon Aurora using AWS Database Migration Service, and we show you how Aurora Serverless can automatically scale your database and reduce your database costs. Ensure that you have an AWS account, and familiarize yourself with the AWS Management Console at least a day before the workshop. You don't need any credit on the account.
ENT201 Simplifying Microsoft Architectures with AWS ServicesAmazon Web Services
Learn how to architect fully available and scalable Microsoft solutions and environments in AWS. Find out how Microsoft solutions can leverage various AWS services to achieve more resiliency, replace unnecessary complexity, simplify architecture, provide scalability, and introduce DevOps concepts, such as compliance, governance, automation, and repeatability. Also, plan authentication and authorization, and explore various hybrid scenarios with other cloud environment and on-premises solutions or infrastructure. Learn about common architecture patterns for network design, Microsoft Active Directory, and business productivity solutions like Dynamics AX, CRM, and Microsoft SharePoint, also common scenarios for custom .NET, .NET Core with Microsoft SQL deployments and migrations.
In this session, we show you how to understand what data you have, how to drive insights, and how to make predictions using purpose-built AWS services. Learn about the common pitfalls of building data lakes and discover how to successfully drive analytics and insights from your data. Also learn how services such as Amazon S3, AWS Glue, Amazon Redshift, Amazon Athena, Amazon EMR, Amazon Kinesis, and Amazon Machine Learning (Amazon ML) services work together to build a successful data lake for various roles, including data scientists and business users.
Getting started with streaming analytics: Setting up a pipelinejavier ramirez
In this session I will show you how to create a simple streaming analytics pipeline, first using open source tools and developing locally, then moving to a VM, then moving to fully managed AWS services. The session will serve as an introduction to some details of Apache Kafka, Apache Flink, ElasticSearch, Amazon Managed Streaming for Kafka, Kinesis Data Analytics, and Amazon ElasticSearch. It will be an almost slideless presentation, as I will spent most of the time at the command line and the IDE.
All Databases Are Equal, But Some Databases Are More Equal than Others: How t...javier ramirez
Data comes in different shapes and sizes, at different speeds, and with different processing needs. AWS offers managed databases covering SQL, NoSQL, in-memory, graphs, time series, and ledger databases. Learn which are the main use cases for each of those, which ones offer autoscaling or serverless capabilities, how you can start from scratch or via database migration services, and why Amazon Aurora is the fastest-growing service in the history of AWS.
Modernizing Your Microsoft Business Applications - CMP201 - Anaheim AWS SummitAmazon Web Services
Modernizing Microsoft applications to the cloud is becoming more important for customers as they continue to innovate their workloads. AWS has over 10 years' experience running Windows applications, and according to IDC, over 57% of all Windows operating systems in the cloud run on AWS. In this webinar, you learn why AWS is the preferred choice for customers around the world for running and scaling their Windows applications.
Big Data per le Startup: come creare applicazioni Big Data in modalità Server...Amazon Web Services
La varietà e la quantità di dati che si crea ogni giorno accelera sempre più velocemente e rappresenta una opportunità irripetibile per innovare e creare nuove startup.
Tuttavia gestire grandi quantità di dati può apparire complesso: creare cluster Big Data su larga scala sembra essere un investimento accessibile solo ad aziende consolidate. Ma l’elasticità del Cloud e, in particolare, i servizi Serverless ci permettono di rompere questi limiti.
Vediamo quindi come è possibile sviluppare applicazioni Big Data rapidamente, senza preoccuparci dell’infrastruttura, ma dedicando tutte le risorse allo sviluppo delle nostre le nostre idee per creare prodotti innovativi.
Hybrid Cloud Storage: Why HUSCO International Left Traditional Storage BehindAmazon Web Services
When CIO Eric Hanson joined HUSCO International’s leadership team, he quickly set about identifying opportunities for his organization to reduce storage costs and improve performance. Using his previous experience at a multi-location manufacturing firm, he determined that HUSCO International’s challenges with file latency, increasing storage costs, and inability to collaborate cross-site could be solved by transitioning from traditional storage using NetApp filers to a consolidated cloud infrastructure powered by Panzura and Amazon S3.
Register for our upcoming webinar to learn how HUSCO International is using Panzura and Amazon S3 to take advantage of cloud storage economics that has the potential to save the company hundreds of thousands of dollars annually.
AWS re:Invent 2016: Delighting Customers Through Device Data with Salesforce ...Amazon Web Services
The Internet of Things (IoT) produces vast quantities of data that promise a deep, always connected view into customer experiences through their devices. In this connected age, the question is no longer how do you gather customer data, but what do you do with all that data. How do you ingest at massive scale and develop meaningful experiences for your customers? In this session, you learn how Salesforce IoT Cloud works in concert with the AWS IoT engine to ingest and transform all of the data generated by every one of your customers, partners, devices, and sensors into meaningful action. You also see how customers are using Salesforce and AWS together to process massive quantities of data, build business rules with simple, intuitive tools, and engage proactively with customers in real time. Session sponsored by Salesforce.
AWS Data Pipeline Tutorial | AWS Tutorial For Beginners | AWS Certification T...Edureka!
( ** AWS Architect Traininhg: https://www.edureka.co/cloudcomputing ** )
This “AWS Data Pipeline Tutorial” by Edureka will help you understand how to process, store & analyze data with ease from the same location using AWS Data Pipeline.
Below is the list of topics covered in this session:
1. Need for Data Pipeline
2. What is AWS Data Pipeline?
3. AWS Data Pipeline Components
4. Demo on AWS Data Pipeline
Check out our complete AWS Playlist here: https://goo.gl/8qrfKU
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
You’re interested in the cloud, and you want to start learning more. In this webinar we will answer the following questions:
• What is Cloud Computing?
• What are the benefits of Cloud Computing?
• What are AWS’s products and what workloads can I run with them?
• Who is using the cloud and what are they using it for?
Presenter: Jeff Barr, AWS Chief Evangelist
How to Architect a Serverless Cloud Data Lake for Enhanced Data AnalyticsInformatica
This presentation is geared toward enterprise architects and senior IT leaders looking to drive more value from their data by learning about cloud data lake management.
As businesses focus on leveraging big data to drive digital transformation, technology leaders are struggling to keep pace with the high volume of data coming in at high speed and rapidly evolving technologies. What's needed is an approach that helps you turn petabytes into profit.
Cloud data lakes and cloud data warehouses have emerged as a popular architectural pattern to support next-generation analytics. Informatica's comprehensive AI-driven cloud data lake management solution natively ingests, streams, integrates, cleanses, governs, protects and processes big data workloads in multi-cloud environments.
Please leave any questions or comments below.
re:Invent Recap: Security Week at the San Francisco Loft
Join us for a round up of all things re:Invent, the largest global cloud computing conference that will have taken place November 25 to 30 in Las Vegas. We'll share security and compliance related highlights from the keynote sessions, and will summarize launches and features to watch.
Level: 100
Speaker: Bill Reid - Sr. Manager, Solutions Architecture, AWS
Build real-time streaming data pipelines to AWS with Confluentconfluent
Traditional data pipelines often face scalability issues and challenges related to cost, their monolithic design, and reliance on batch data processing. They also typically operate under the premise that all data needs to be stored in a single centralized data source before it's put to practical use. Confluent Cloud on Amazon Web Services (AWS) provides a fully managed cloud-native platform that helps you simplify the way you build real-time data flows using streaming data pipelines and Apache Kafka.
Data & Analytics ReInvent Recap [AWS Basel Meetup - Jan 2023].pdfChris Bingham
After recapping the key data & analytics announcements from AWS re:Invent 2022, we look a little deeper at three key new services:
• AWS DataZone
• AWS Omics
• AWS Clean Rooms
And follow up with a demo of using AWS IoT ExpressLink hardware in conjunction with AWS IoT Core, Lambda, and Amplify to build a Gatsby web app that interacts with the AWS IoT ExpressLink demo badge via a device shadow.
Accelerate your journey to AI: IBM Cloud Pak for Data on AWS - DEM18-S - New ...Amazon Web Services
Join us as we discuss IBM’s data & AI platform, IBM Cloud Pak for Data, led by our recently announced Quick Start for AWS customers. IBM Cloud Pak for Data enables customers to collect, organize, and analyze all types of data inside a cloud-native data and AI architecture that natively supports AWS. Learn about IBM’s other solutions that run on AWS, such as Db2 Warehouse on Cloud. Leave this talk with an understanding of how IBM can help you get started on your journey to AI. This presentation is brought to you by AWS partner, IBM.
Architecting Web Applications for the Cloud - Design Principles and Practical...Adnene Guabtni
This presentation provides an overview of the best practice and design principles for architecting highly scalable and highly available web applications on Amazon Web Services (AWS) cloud. Architecting web applications for the cloud requires a deep understanding of the true benefits of cloud computing and the implementation of 8 design principles for AWS.
How to deploy a production ready serverless application
Level: 300
To get the most out of the agility afforded by serverless, it is essential to build CI/CD pipelines that help teams iterate on code and quickly release features.
Serverless and distributed systems in production cannot be done any other way than with a good and efficient CI/CD pipeline.
In this talk, I demonstrate how you can use infrastructure-as-code (IaC) models to build effective CI/CD release workflows to manage serverless deployments on AWS, using tools like AWS CodeBuild, AWS CodePipeline, and AWS CodeDeploy.
Specifically, we focus on how to automate safer deployments that can be monitored and rolled back automatically.
I will do demos where I deploy Lambdas with Code* suite and show examples with complex systems.
20201013 - Serverless Architecture Conference - How to migrate your existing ...Marcia Villalba
How to migrate an existing application to serverless?
Level 200 - 250
You want to migrate your existing application to serverless and you don’t know where to start.
This is a common problem that a lot of the architects, CTOs and developers have, as it is very rare that we start a project from a Greenfield.
In this talk I will walk you through different strategies to migrate an existing application to serverless. We will look at known architectures existing challenges in applications and how we can overcome them with serverless. And also I will share what I learnt when I worked on the migration of one existing micro services application into serverless.
2020-04-02 DevConf - How to migrate an existing application to serverlessMarcia Villalba
You want to migrate your existing application to serverless and you don’t know where to start. This is a common problem that a lot of the architects, CTOs and developers have, as it is very rare that we start a project from a Greenfield. In this talk, I will walk you through different strategies to migrate an existing application to serverless. We will look at known architectures existing challenges in applications and how we can overcome them with serverless. And also I will share what I learnt when I worked on the migration of one existing microservices application into serverless.
Track: DevOps: Culture of working together and the technology that makes it happen
Serverless Days Milano - Developing Serverless applications with GraphQLMarcia Villalba
This is the presentation that I gave at Serverless Days Milano 2019. Its a 10-minute presentation with lots of videos.
If you want to learn more about AppSync check my playlist on how to get started with this. https://www.youtube.com/playlist?list=PLGyRwGktEFqdX2cjO5xQVKb96q2DpwASR
Octubre 2018 - AWS UG Montevideo - Intro a Serverless y buenas practicasMarcia Villalba
Presentación de mas o menos una hora que di en la meetup de AWS UG Montevideo.
En esta presentación hablo de serverless, que es y como funciona. También doy buenas practicas para el desarrollo de proyectos serverless en producción
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
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6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
9. Amazon Braket
Introducing
Fully managed service that makes it easy for scientists and developers to
explore and experiment with quantum computing.
DRAFTQuantum Technology
Preview – December 2
LEARN MORE CMP213: Introducing Quantum Computing with AWS
10. AWS Compute Optimizer
Introducing
Identify optimal Amazon EC2 instances and EC2 Auto Scaling group
for your workloads using a ML-powered recommendation engine
DRAFTManagement Tools
General Availability – December 3
LEARN MORE CMP323: Optimize Performance and Cost for Your AWS Compute
12. Receive lower rates
automatically. Easy to use
with recommendations in
AWS Cost Explorer
Significant
savings of up to 72%
Flexible across instance family,
size, OS, tenancy or AWS
Region; also applies to AWS
Fargate & soon to AWS
Lambda usage
Compute/Cost Management
LEARN MORE CMP210: Dive deep on Savings Plans
Announced – November 6
Simplify purchasing with a flexible pricing model that offers savings of
up to 72% on Amazon ECS, AWS Fargate & AWS Lambda usage
Savings Plans
13. DRAFTContainers
General Availability – December 3
LEARN MORE CON-326R - Running Kubernetes Applications on AWS Fargate
Introducing
The only way to run serverless Kubernetes containers securely,
reliably, and at scale
Amazon EKS for AWS Fargate
14.
15. The Amazon Builders’ Library
Architecture, software delivery, and operations
By Amazon’s senior technical executives and engineers
Real-world practices with detailed explanations
Content available for free on the website
18. Amazon S3 Access Points
Introducing
Simplify managing data access at scale for applications using shared data
sets on Amazon S3. Easily create hundreds of access points per bucket,
each with a unique name and permissions customized for each application.
DRAFTStorage
General Availability – December 3
21. Amazon Managed Apache Cassandra Service
Introducing
A scalable, highly available, and serverless Apache Cassandra–compatible
database service. Run your Cassandra workloads in the AWS cloud using the
same Cassandra application code and developer tools that you use today.
Apache Cassandra-
compatible
Performance
at scale
Highly available
and secure
No servers
to manage
DRAFTDatabases
Preview – December 3
LEARN MORE DAT324: Overview of Amazon Managed Apache Cassandra Service
22. DRAFTDatabases
Announced – November 26
Amazon Aurora Machine Learning Integration
Simple, optimized, and secure Aurora, SageMaker, and Comprehend (in preview)
integration. Add ML-based predictions to databases and applications using SQL,
without custom integrations, moving data around, or ML experience.
28. Amazon RDS Proxy
Introducing
Fully managed, highly available database proxy feature for Amazon
RDS. Pools and shares connections to make applications more
scalable, more resilient to database failures, and more secure.
DRAFTDatabases
Public Beta – December 3
LEARN MORE DAT368: Setting up database proxy servers with RDS Proxy
29.
30. UltraWarm for Amazon Elasticsearch Service
Introducing
A low cost, scalable warm storage tier for Amazon Elasticsearch Service. Store
up to 10 PB of data in a single cluster at 1/10th the cost of existing storage tiers,
while still providing an interactive experience for analyzing logs.
DRAFTAnalytics
Public Beta – December 3
LEARN MORE ANT229: Scalable, secure, and cost-effective log analytics
31. Amazon Redshift Data Lake Export
New Feature
No other data warehouse makes it as easy to gain new insights from
all your data.
DRAFTAnalytics
General Availability – December 3
LEARN MORE
ANT335R: How to build your data analytics stack at scale with Amazon
Redshift
32. AWS Data Exchange
Quickly find diverse data
in one place
Efficiently access
3rd-party data
Easily analyze data
Reach millions of
AWS customers
Easiest way to package and
publish data products
Built-in security and
compliance controls
For
Subscribers
For
Providers
DRAFTAnalytics
Announced – November 13
L E A R N M O R E
ANT238-R: AWS Data Exchange: Easily find & subscribe to third-party
data in the cloud
Easily find and subscribe to 3rd-party data in the cloud
35. DRAFTManagement Tools
Announced – November 21
Identify unusual activity in your AWS accounts
ü Save time sifting through logs
ü Get ahead of issues before
they impact your business
CloudTrail Insights
Introducing
• Unexpected spikes in resource
provisioning
• Bursts of IAM management
actions
• Gaps in periodic maintenance
activity
L E A R N M O R E MGT420-R: CloudTrail Insights: Identify and Solve Operational Issues
36. AWS Detective
Introducing
Quickly analyze, investigate, and identify the root cause of security
findings and suspicious activities.
Automatically distills
& organizes data into
a graph model
Easy to use visualizations
for faster & effective
investigation
Continuously updated as
new telemetry becomes
available
Preview – December 3
DRAFTSecurity
LEARN MORE SEC312: Introduction to Amazon Detective
37. AWS IAM Access Analyzer
Introducing
Continuously ensure that policies provide the intended public and cross-account access
to resources, such as Amazon S3 buckets, AWS KMS keys, & AWS Identity and Access
Management roles.
General Availability – December 2
DRAFTSecurity
Uses automated reasoning, a form of
mathematical logic, to determine all possible
access paths allowed by a resource policy
Analyzes new or updated resource
policies to help you understand
potential security implications
Analyzes resource policies for
public or cross-account access
LEARN MORE SEC309: Deep Dive into AWS IAM Access Analyzer
39. L E A R N M O R E SVS401 - Optimizing your serverless applications
Provisioned Concurrency on AWS Lambda
New Feature
• Keeps functions initialized and hyper-ready, ensuring
start times stay in the milliseconds
• Builders have full control over when provisioned
concurrency is set
• No code changes are required to provision concurrency
on functions in production
• Can be combined with AWS Auto Scaling at launch
DRAFTServerless
General Availability – December 3
40.
41.
42. Achieve up to 67% cost reduction and 50% latency reduction compared
to REST APIs. HTTP APIs are also easier to configure than REST APIs,
allowing customers to focus more time on building applications.
Reduce application costs by
up to 67%
Reduce application latency by
up to 50%
Configure HTTP APIs easier
and faster than before
HTTP APIs for Amazon API Gateway
Introducing
DRAFTMobile Services
Preview – December 4
L E A R N M O R E
CON213-L - Leadership session: Using containers and serverless to
accelerate modern application development (incl schema registry demo)
43.
44.
45. AWS Step Functions Express Workflows
Introducing
Orchestrate AWS compute, database, and messaging services at rates
greater than 100,000 events/second, suitable for high-volume event
processing workloads such as IoT data ingestion, streaming data
processing and transformation.
DRAFTApp Integration
General Availability – December 3
L E A R N M O R E API321: Event-Processing Workflows at Scale with AWS Step Functions
47. Amazon EventBridge Schema Registry
Introducing
Store event structure - or schema - in a shared central location, so it’s
faster and easier to find the events you need. Generate code bindings
right in your IDE to represent an event as an object in code.
DRAFTApp Integration
Preview – December 3
LEARN MORE
CON213-L - Leadership session: Using containers and serverless to
accelerate modern application development (incl schema registry demo)
55. Container Support for AWS IoT Greengrass
New Feature
DRAFTInternet of Things
Announced – November 25
Deploy containers seamlessly to edge devices
Move containers from the cloud
to edge devices using AWS IoT
Greengrass, without rewriting
any code.
Enables both Docker & AWS
Lambda components to
operate seamlessly together at
the edge
Use AWS IoT Greengrass Secrets
Manager to manage credentials
for private container registries.
56. AWS Outposts
Now Available
Fully managed service that extends AWS infrastructure, AWS services, APIs, and tools to virtually any
connected customer site. Truly consistent hybrid experience for applications across on-premises and
cloud environments. Ideal for low latency or local data processing application needs.
Same AWS-designed infrastructure
as in AWS regional data centers
(built on AWS Nitro System)
delivered to customer facilities
Fully managed, monitored, and
operated by AWS
as in AWS Regions
Single pane of management
in the cloud providing the
same APIs and tools as
in AWS Regions
Compute
General Availability – December 3
LEARN MORE
CMP302-R: AWS Outposts: Extend the AWS experience to on-premises
environments
Wednesday at 11:30am, Aria
Thursday at 3:15pm, Mirage
Friday at 10:45am, Mirage
60. Local Zones
Introducing
Extend the AWS Cloud to more locations and closer to your end-users
to support ultra low latency application use cases. Use familiar AWS
services and tools and pay only for the resources you use.
DRAFTCompute
General Availability – December 3
The first Local Zone to be released will be located in Los Angeles.
61. AWS Wavelength
Introducing
Embeds AWS compute and storage inside telco providers’ 5G
networks. Enables mobile app developers to deliver applications with
single-digit millisecond latencies. Pay only for the resources you use.
DRAFTCompute
Announcement – December 3
62. AWS Wavelength
Introducing
Embeds AWS compute and storage inside telco providers’ 5G
networks. Enables mobile app developers to deliver applications with
single-digit millisecond latencies. Pay only for the resources you use.
DRAFTCompute
Announcement – December 3
64. VISION SPEECH TEXT SEARCH NEW CHATBOTS PERSONALIZATION FORECASTING FRAUD NEW DEVELOPMENT NEW CONTACT CENTERS NEW
Amazon SageMaker Ground
Truth
Augmented
AI
SageMaker
Neo
Built-in
algorithms
SageMaker
Notebooks NEW
SageMaker
Experiments NEW
Model
tuning
SageMaker
Debugger NEW
SageMaker
Autopilot NEW
Model
hosting
SageMaker
Model Monitor NEW
Deep Learning
AMIs & Containers
GPUs &
CPUs
Elastic
Inference
Inferentia
(Inf2)
FPGA
Amazon
Rekognition
Amazon
Polly
Amazon
Transcribe
+Medical
Amazon
Comprehend
+Medical
Amazon
Translate
Amazon
Lex
Amazon
Personalize
Amazon
Forecast
Amazon
Fraud Detector
Amazon
CodeGuru
AI SERVICES
ML SERVICES
ML FRAMEWORKS & INFRASTRUCTURE
Amazon
Textract
Amazon
Kendra
Contact Lens
For Amazon Connect
SageMaker Studio IDE NEW
NEW
AWS Machine Learning stack
NEW
68. Introducing Amazon Rekognition Custom Labels
• Import images labeled by Amazon
SageMaker Ground Truth…
• Or label images automatically based on folder structure
• Train a model on fully managed
infrastructure
• Split the data set for training and validation
• See precision, recall, and F1 score at the end of training
• Select your model
• Use it with the usual Rekognition APIs
69.
70. Customers are forced to choose
ML only systems are high speed and low
cost, but do not support nuanced decision
making
Human only workflows offer nuanced
decision making, but they’re low speed and
high cost.
OR
72. A2I lets you easily implement human review in
machine learning workflows to improve the accuracy,
speed, and scale of complex decisions.
Introducing Amazon Augmented AI (A2I)
73. How Amazon Augmented AI works
Client application
sends input data
AWS AI Service or
custom ML model
makes predictions
Results stored
to your S3
1 2
4
Low confidence predictions
sent for human review
3
High-confidence predictions
returned immediately to client
application
5
Amazon Rekognition
Amazon Textract
74. Human Review Workforces
Amazon Mechanical Turk
An on-demand 24x7 workforce
of over 500,000 independent
contractors worldwide, powered
by Amazon Mechanical Turk
Private
A team of workers that you have
sourced yourself, including your
own employees or contractors
for handling data that needs to
stay within your organization
Vendors
A curated list of third-party
vendors that specialize in
providing data labeling services,
available via de AWS Marketplace
75.
76.
77. Fraud detection is difficult
$$$ billions lost to
fraud each year
Online business prone
to fraud attacks
Bad actors often
change tactics
Changing rules =
more human reviews
Dependent on others to
update detection logic
78. Fraud detection with ML is also difficult
Top data scientists are
costly & hard to find
One-size-fits-all models
underperform
Often need to
supplement data
Data transformation +
feature engineering
Fraud imbalance =
needle in a haystack
79. Introducing Amazon Fraud Detector
A fraud detection service that makes
it easy for businesses to use machine
learning to detect online fraud in
real-time, at scale
80.
81.
82. Amazon Fraud Detector – Key Features
Pre-built fraud
detection model
templates
Automatic
creation of
custom fraud
detection
models
Models learn
from past
attempts to
defraud Amazon
Amazon
SageMaker
integration
One interface to
review past
evaluations and
detection logic
83. Typical Application Build and Run Process
Write +
Review
Build +
Test
Deploy Measure Improve
1. Code Reviews require expertise in multiple areas such as
knowledge of AWS APIs, Concurrency, etc.
2. Code analyzer tools require high accuracy.
3. Distributed Cloud application are difficult to optimize.
4. Performance engineering expertise is hard to find.
84. Introducing AWS CodeGuru
Built-in code reviews
with intelligent
recommendations
Detect and optimize
expensive lines of
code before
production
Easily identify latency
and performance
improvements
production
environment
CodeGuru Reviewer CodeGuru Profiler
LEARN MORE Introduction to Amazon CodeGuru (DOP211)
85. CodeGuru Reviewer: How It Works
Input:
Source Code
Feature Extraction Machine Learning
Output:
Recommendations
Customer provides source
code as input
Java
AWS CodeCommit
Github
Extract semantic features /
patterns
ML algorithms identify similar
code for comparison
Customers see
recommendations as Pull
Request feedback
86. CodeGuru Example – Looping vs Waiting
do {
DescribeTableResult describe = ddbClient.describeTable(new DescribeTableRequest().withTableName(tableName));
String status = describe.getTable().getTableStatus();
if (TableStatus.ACTIVE.toString().equals(status)) {
return describe.getTable();
}
if (TableStatus.DELETING.toString().equals(status)) {
throw new ResourceInUseException("Table is " + status + ", and waiting for it to become ACTIVE is not useful.");
}
Thread.sleep(10 * 1000);
elapsedMs = System.currentTimeMillis() - startTimeMs;
} while (elapsedMs / 1000.0 < waitTimeSeconds);
throw new ResourceInUseException("Table did not become ACTIVE after ");
This code appears to be waiting for a resource before it runs. You could use the waiters feature to help improve
efficiency. Consider using TableExists, TableNotExists. For more information,
see https://aws.amazon.com/blogs/developer/waiters-in-the-aws-sdk-for-java/
Recommendation
Code
We should use waiters instead - will help remove a lot of this code.Developer Feedback
88. CodeGuru Profiler: How It Works
Input:
Live application
stack trace
Application profile
sampling
Pattern matching
Output:
Method names,
Recommendations
and searchable
visualizations
Customer application
runs in production
CodeGuru Profiler
continuously captures
application stack trace
information
CodeGuru Profiler detects
performance inefficiencies in the
live application
Customers see recommendations
in their automated efficiency
reports and visualizations
Amazon Confidential
89.
90. Employees spend 20% of their
time looking for information.
—McKinsey
20%
44%44% of the time, they cannot
find the information they need to
do their job.
—IDC
91. Introducing Kendra
Easy to find what you are
looking for
Fast search, and
quick to set up
Native connectors
(S3, Sharepoint,
file servers,
HTTP, etc.)
Natural language
Queries
NLU and
ML core
Simple API
and console
experiences
Code samples
Incremental
learning through
feedback
Domain
Expertise
94. Getting started with Kendra
Step 1
Create an index
An index is the place where
you add your data sources
to make them searchable
in Kendra.
Step 2
Add data sources
Add and sync your data
from S3, Sharepoint, Box
and other data sources, to
your index.
Step 3
Test & deploy
After syncing your data,
visit the Search console
page to test search &
deploy Kendra in your
search application.
98. Introducing Amazon SageMaker Studio
The first fully integrated development environment (IDE) for machine learning
Organize, track, and
compare thousands of
experiments
Easy experiment
management
Share scalable notebooks
without tracking code
dependencies
Collaboration at
scale
Get accurate models for
with full visibility & control
without writing code
Automatic model
generation
Automatically debug errors,
monitor models, & maintain
high quality
Higher quality ML
models
Code, build, train, deploy, &
monitor in a unified visual
interface
Increased
productivity
99.
100. Data science and collaboration
needs to be easy
Setup and manage resources
Collaboration across
multiple data scientists
Different data science
projects have different
resource needs
Managing notebooks and
collaborating across
multiple data scientists is
highly complicated
+
+
=
101. Introducing Amazon SageMaker Notebooks
Access your notebooks in
seconds with your corporate
credentials
Fast-start shareable notebooks
Administrators manage
access and permissions
Share your notebooks
as a URL with a single click
Dial up or down
compute resources
Start your notebooks
without spinning up
compute resources
102.
103. Data Processing and
Model Evaluation involves a lot of
operational overhead
Building and scaling infrastructure
for data processing workloads is
complex
Use of multiple tools or services
implies learning and
implementing new APIs
All steps in the ML workflow need
enhanced security, authentication
and compliance
Need to build and manage tooling
to run large data processing and
model evaluation workloads
+
+
=
104. Introducing Amazon SageMaker Processing
Analytics jobs for data processing and model evaluation
Use SageMaker’s built-in
containers or bring your own
Bring your own script for
feature engineering
Custom processing
Achieve distributed
processing for clusters
Your resources are created,
configured, & terminated
automatically
Leverage SageMaker’s
security & compliance
features
105. Managing trials and experiments is
cumbersome
Hundreds of experiments
Hundreds of parameters
per experiment
Compare and contrast
Very cumbersome and
error prone
+
+
=
106. Introducing Amazon SageMaker Experiments
Experiment
tracking at scale
Visualization for
best results
Flexibility with
Python SDK & APIs
Iterate quickly
Track parameters & metrics
across experiments & users
Organize
experiments
Organize by teams, goals, &
hypotheses
Visualize & compare
between experiments
Log custom metrics &
track models using APIs
Iterate & develop high-
quality models
A system to organize, track, and evaluate training experiments
107.
108. Debugging and profiling
deep learning is painful
Large neural networks
with many layers
Many connections
Additional tooling for analysis
and debug
Extraordinarily difficult
to inspect, debug, and profile
the ‘black box’
+
+
=
109. Automatic data
analysis
Relevant data
capture
Automatic error
detection
Improved productivity
with alerts
Visual analysis
and debug
Introducing Amazon SageMaker Debugger
Analyze and debug data
with no code changes
Data is automatically
captured for analysis
Errors are automatically
detected based on rules
Take corrective action based
on alerts
Visually analyze & debug
from SageMaker Studio
Analysis & debugging, explainability, and alert generation
110.
111. Deploying a model is not the end, you
need to continuously monitor it in
production and iterate
Concept drift due to
divergence of data
Model performance can
change due to unknown
factors
Continuous monitoring of model
performance and data involves a
lot of effort and expense
Model monitoring is
cumbersome but critical
+
+
=
112. Introducing Amazon SageMaker Model Monitor
Automatic data
collection
Continuous
Monitoring
CloudWatch
Integration
Data is automatically
collected from your
endpoints
Automate corrective
actions based on Amazon
CloudWatch alerts
Continuous monitoring of models in production
Visual
Data analysis
Define a monitoring
schedule and detect
changes in quality against
a pre-defined baseline
See monitoring results,
data statistics, and
violation reports in
SageMaker Studio
Flexibility
with rules
Use built-in rules to
detect data drift or write
your own rules for
custom analysis
113. Successful ML requires
complex, hard to discover
combinations
Largely explorative &
iterative
Requires broad and
complete
knowledge of ML domain
Lack of visibility
Time consuming,
error prone process
even for ML experts
+
+
=
of algorithms, data, parameters
114. Introducing Amazon SageMaker Autopilot
Quick to start
Provide your data in a
tabular form & specify target
prediction
Automatic
model creation
Get ML models with feature
engineering & automatic model
tuning automatically done
Visibility & control
Get notebooks for your
modelswith source code
Automatic model creation with full visibility & control
Recommendations &
Optimization
Get a leaderboard & continue
to improve your model
117. AWS DeepRacer improvements
• AWS DeepRacer Evo
• Stereo camera
• LIDAR sensor
• New racing opportunities
• Create your own races
• Object Detection & Avoidance
• Head-to-head racing
118. AWS DeepComposer
• The world’s first machine
learning-enabled musical
keyboard
• Compose music using Generative
Adversarial Networks (GAN)
• Use a pretrained model, or train
your own
119. AWS DeepComposer
• The world’s first machine
learning-enabled musical
keyboard
• Compose music using Generative
Adversarial Networks (GAN)
• Use a pretrained model, or train
your own
120. T h a n k y o u !
Marcia Villalba
Developer Advocate, AWS
@mavi888uy