The document provides an overview of Amazon's machine learning capabilities including:
- Platform services like EC2 P3 instances and Deep Learning AMIs for training models
- Managed services like SageMaker for building, training, and deploying models, and applications services like Rekognition, Transcribe, Translate, and Comprehend for vision, speech and text analysis
- It describes how these capabilities are used across Amazon for applications like fulfilment, search, and developing new products
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Using AWS Control Tower to govern multi-account AWS environments at scale - G...Amazon Web Services
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
AWS delivers an integrated suite of services that provide everything needed to quickly and easily build and manage a data lake for analytics. AWS-powered data lakes can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot. In this session, we will show you how you can quickly build a data lake on AWS that ingests, catalogs and processes incoming data and makes it ready for analysis. Using a live demo, we demonstrate the capabilities of AWS provided analytical services such as AWS Glue, Amazon Athena and Amazon EMR and how to build a Data Lake on AWS step-by-step.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to get started with Artificial Intelligence on the AWS Cloud. In particular, we will explore AWS cloud-native machine learning and deep learning technologies that address a range of different use cases and needs. These include AWS Lex, which provides natural language understanding (NLU) and automatic speech recognition (ASR); Amazon Rekognition, which provides visual search and image recognition capabilities; Amazon Polly for text-to-speech (TTS) capabilities; and Amazon Machine Learning tools. The session will also cover the AWS Deep Learning AMI, which lets you run deep learning in the cloud at any scale.
If you're based in South East Asia, join us for upcoming AWS Webinar Series https://aws.amazon.com/events/asean/webinars/
Data processing and analysis is where big data is most often consumed - driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing and Interactive analytics. AWS services to be covered include: Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.
Big Data Analytics Architectural Patterns and Best Practices (ANT201-R1) - AW...Amazon Web Services
In this session, we discuss architectural principles that helps simplify big data analytics.
We'll apply principles to various stages of big data processing: collect, store, process, analyze, and visualize. We'll disucss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on.
Finally, we provide reference architectures, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Using AWS Control Tower to govern multi-account AWS environments at scale - G...Amazon Web Services
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
AWS Control Tower is a new AWS service that cloud administrators can use to set up and govern their secure, compliant, multi-account environments on AWS. In this session, we show you how Control Tower automates the creation of a secure and compliant landing zone with best-practice blueprints for a multi-account structure, identity and federated access management, a central log archive, cross-account security audits, and workflows for provisioning accounts with pre-approved configurations. We also discuss guardrails—pre-packaged governance rules created for security, operations, and compliance that you can apply enterprise-wide or to groups of accounts to enforce policies or detect violations. Finally, we show you how to easily manage and monitor all this through the Control Tower dashboard.
AWS delivers an integrated suite of services that provide everything needed to quickly and easily build and manage a data lake for analytics. AWS-powered data lakes can handle the scale, agility, and flexibility required to combine different types of data and analytics approaches to gain deeper insights, in ways that traditional data silos and data warehouses cannot. In this session, we will show you how you can quickly build a data lake on AWS that ingests, catalogs and processes incoming data and makes it ready for analysis. Using a live demo, we demonstrate the capabilities of AWS provided analytical services such as AWS Glue, Amazon Athena and Amazon EMR and how to build a Data Lake on AWS step-by-step.
Dal riconoscimento facciale al riconoscimento di frodi o difetti di fabbricazione, l'analisi di immagini e video che sfruttano tecniche di intelligenza artificiale, si stanno evolvendo e raffinando a ritmi elevati. In questo webinar esploreremo le possibilità messe a disposizione dai servizi AWS per applicare lo stato dell'arte delle tecniche di computer vision a scenari reali.
Whether you’re just getting started with AI or you’re a deep learning expert, this session will provide a meaningful overview of how to get started with Artificial Intelligence on the AWS Cloud. In particular, we will explore AWS cloud-native machine learning and deep learning technologies that address a range of different use cases and needs. These include AWS Lex, which provides natural language understanding (NLU) and automatic speech recognition (ASR); Amazon Rekognition, which provides visual search and image recognition capabilities; Amazon Polly for text-to-speech (TTS) capabilities; and Amazon Machine Learning tools. The session will also cover the AWS Deep Learning AMI, which lets you run deep learning in the cloud at any scale.
If you're based in South East Asia, join us for upcoming AWS Webinar Series https://aws.amazon.com/events/asean/webinars/
Data processing and analysis is where big data is most often consumed - driving business intelligence (BI) use cases that discover and report on meaningful patterns in the data. In this session, we will discuss options for processing, analyzing and visualizing data. We will also look at partner solutions and BI-enabling services from AWS. Attendees will learn about optimal approaches for stream processing, batch processing and Interactive analytics. AWS services to be covered include: Amazon Machine Learning, Elastic MapReduce (EMR), and Redshift.
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
Learn about AWS Managed Services and SaaS Partner Programs that provide partners with business and technical enablement on AWS. As more customers move mission-critical workloads to the cloud, there is increasing demand for Managed Service Providers to help customers migrate and operate those customer environments on an ongoing basis. Software-as-a-Service (SaaS) is quickly becoming the prevalent model for deploying software to business customers. The AWS SaaS Partner Program provides APN Partners with support as they build, launch, and grow SaaS solutions on AWS. This session will provide you all the information you need to know to get started in the MSP and SaaS Partner Programs.
Amazon GuardDuty: Intelligent Threat Detection and Continuous Monitoring to P...Amazon Web Services
Amazon GuardDuty is a managed threat detection service that continuously monitors for malicious or unauthorized behavior to help you protect your AWS accounts and workloads. It monitors for activity such as unusual API calls or potentially unauthorized deployments that indicate a possible account compromise. Enabled with a few clicks in the AWS Management Console, Amazon GuardDuty can immediately begin analyzing billions of events across your AWS accounts for signs of risk. It does not require you to deploy and maintain software or security infrastructure, meaning it can be enabled quickly with no risk of negatively impacting existing application workloads.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
Amazon Web Services (AWS) provides on-demand computing resources and services in the cloud, with pay-as-you-go pricing. This session provides an overview and describes how using AWS resources instead of your own is like purchasing electricity from a power company instead of running your own generator. Using AWS resources provides many of the same benefits as a public utility: Capacity exactly matches your need, you pay only for what you use, economies of scale result in lower costs, and the service is provided by a vendor experienced in running large-scale networks. A high-level overview of AWS infrastructure (such as AWS Regions and Availability Zones) and AWS services is provided as part of this session.
Speaker: Tom Whateley, Solutions Architect and Stephanie Zieno, Account Manager, Amazon Web Services
This talk will be a 2-300 level discussion on Serverless Architectures on AWS. We’ll first explore the Serverless ecosystem on AWS, looking at some particular use cases for Serverless. Looking through the lens of AWS customers, we’ll look at the typical Serverless journey, as well some of the key emerging patterns and benefits of Serverless Architectures. We’ll also touch some of the key challenges in a distributed environment and some potential solutions and tools that customers might want to consider.
AWS provides a range of Compute Services, Amazon EC2, Amazon ECS, AWS Lambda, and AWS Elastic Beanstalk – allowing you to build everything from web applications, mobile backends to data processing applications.
In this session, we will provide an intro level overview of these services and highlight suitable use cases. We will discuss which service to choose to best get your applications up and running on AWS.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018Amazon Web Services
In this session, we discuss how to deploy a scalable environment that considers the AWS account structure, security services, network architecture, and user access. We present an overview of the AWS Landing Zone solution, an automated solution for setting up a robust and flexible AWS environment designed from the collective experience of AWS and our customers. The AWS Landing Zone helps automate the setup of a flexible account structure, security baseline, network structure, and user access based on best practices. Future growth is facilitated by an account vending machine component that simplifies the creation of additional accounts. Learn how the AWS Landing Zone can ensure that you start your AWS journey with the right foundation. We encourage you to attend the full AWS Landing Zone track, including SEC303. Search for #awslandingzone in the session catalog.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
by Bill Reid, Sr. Manager of Solutions Architecture, AWS
This session is designed to introduce you to fundamental cloud computing and AWS security concepts that will help you prepare for the Security Week sessions, demos, and labs.
The AWS cloud infrastructure has been architected to be one of the most flexible and secure cloud computing environments available today. Security for AWS is about three related elements: visibility, auditability, and control. You have to know what you have and where it is before you can assess the environment against best practices, internal standards, and compliance standards. Controls enable you to place precise, well-understood limits on the access to your information. Did you know, for example, that you can define a rule that says that “Tom is the only person who can access this data object that I store with Amazon, and he can only do so from his corporate desktop on the corporate network, from Monday-Friday 9-5 and when he uses MFA?” That’s the level of granularity you can choose to implement if you wish. In this session, we’ll cover these topics to provide a practical understanding of the security programs, procedures, and best practices you can use to enhance your current security posture.
Moving Large Scale Contact Centers to Amazon Connect (BAP324) - AWS re:Invent...Amazon Web Services
In this session, you will learn how Intuit and Hilton are migrating their large scale contact centers to Amazon Connect, a self-service, cloud-based contact center offering based on the same technology used by over 70,000 Amazon Customer Service Associates. We will begin the session with an overview of Amazon Connect and hear from Intuit and Hilton about their experiences and best practices that will help prepare any large scale business planning a migration to Amazon Connect.
Introduction to AWS Cloud Computing | AWS Public Sector Summit 2016Amazon Web Services
Amazon Web Services (AWS) provides on-demand computing resources and services in the cloud, with pay-as-you-go pricing. This session provides an overview and describes how using AWS resources instead of your own is like purchasing electricity from a power company instead of running your own generator. Using AWS resources provides many of the same benefits as a public utility: Capacity exactly matches your need, you pay only for what you use, economies of scale result in lower costs, and the service is provided by a vendor experienced in running large-scale networks. A high-level overview of AWS’s infrastructure (such as AWS Regions and Availability Zones) and AWS services is provided as part of this session.
(AWS Certification Training: https://www.edureka.co/cloudcomputing)
This “Amazon Redshift" tutorial by Edureka will help you understand what Amazon Redshift is & how to set up a data warehouse on cloud using Amazon Redshift. Below are the topics covered in the ppt:
1. Traditional Data Warehouse
2. Amazon Redshift – A to Z
3. Demo on Amazon Redshift
Check out our complete AWS Playlist here: https://goo.gl/8qrfKU
Amazon GuardDuty - Let's Attack My Account! - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- How to safely generate a number of Amazon GuardDuty findings
- How to analyze Amazon GuardDuty findings
- How to think about remediation of threats
AI & ML at Amazon: AWS Developer Workshop - Web Summit 2018Amazon Web Services
AI & Machine Learning at Amazon: AWS Developer Workshop - Web Summit 2018
Amazon has been applying machine learning to create artifical intelligence features within its products and services for over 20 years. Join this session and learn about the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and how you can use easily accessible services from AWS to enable you to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
Effective Data Lakes: Challenges and Design Patterns (ANT316) - AWS re:Invent...Amazon Web Services
Data lakes are emerging as the most common architecture built in data-driven organizations today. A data lake enables you to store unstructured, semi-structured, or fully-structured raw data as well as processed data for different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning. Well-designed data lakes ensure that organizations get the most business value from their data assets. In this session, you learn about the common challenges and patterns for designing an effective data lake on the AWS Cloud, with wisdom distilled from various customer implementations. We walk through patterns to solve data lake challenges, like real-time ingestion, choosing a partitioning strategy, file compaction techniques, database replication to your data lake, handling mutable data, machine learning integration, security patterns, and more.
Learn about AWS Managed Services and SaaS Partner Programs that provide partners with business and technical enablement on AWS. As more customers move mission-critical workloads to the cloud, there is increasing demand for Managed Service Providers to help customers migrate and operate those customer environments on an ongoing basis. Software-as-a-Service (SaaS) is quickly becoming the prevalent model for deploying software to business customers. The AWS SaaS Partner Program provides APN Partners with support as they build, launch, and grow SaaS solutions on AWS. This session will provide you all the information you need to know to get started in the MSP and SaaS Partner Programs.
Amazon GuardDuty: Intelligent Threat Detection and Continuous Monitoring to P...Amazon Web Services
Amazon GuardDuty is a managed threat detection service that continuously monitors for malicious or unauthorized behavior to help you protect your AWS accounts and workloads. It monitors for activity such as unusual API calls or potentially unauthorized deployments that indicate a possible account compromise. Enabled with a few clicks in the AWS Management Console, Amazon GuardDuty can immediately begin analyzing billions of events across your AWS accounts for signs of risk. It does not require you to deploy and maintain software or security infrastructure, meaning it can be enabled quickly with no risk of negatively impacting existing application workloads.
This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
The Zen of DataOps – AWS Lake Formation and the Data Supply Chain PipelineAmazon Web Services
Many organizations have adopted or are in the process of adopting DevOps methodologies in their quest to accelerate the delivery of software capabilities, features, and functionalities to support their organizational objectives. By applying the same practices, DataOps aims to provide the same level of agility in delivering data and information to the organization. AWS Lake Formation, in coordination with other AWS Services, enables DevOps methodologies to be realized through the Data Supply Chain Pipeline.
Amazon Web Services (AWS) provides on-demand computing resources and services in the cloud, with pay-as-you-go pricing. This session provides an overview and describes how using AWS resources instead of your own is like purchasing electricity from a power company instead of running your own generator. Using AWS resources provides many of the same benefits as a public utility: Capacity exactly matches your need, you pay only for what you use, economies of scale result in lower costs, and the service is provided by a vendor experienced in running large-scale networks. A high-level overview of AWS infrastructure (such as AWS Regions and Availability Zones) and AWS services is provided as part of this session.
Speaker: Tom Whateley, Solutions Architect and Stephanie Zieno, Account Manager, Amazon Web Services
This talk will be a 2-300 level discussion on Serverless Architectures on AWS. We’ll first explore the Serverless ecosystem on AWS, looking at some particular use cases for Serverless. Looking through the lens of AWS customers, we’ll look at the typical Serverless journey, as well some of the key emerging patterns and benefits of Serverless Architectures. We’ll also touch some of the key challenges in a distributed environment and some potential solutions and tools that customers might want to consider.
AWS provides a range of Compute Services, Amazon EC2, Amazon ECS, AWS Lambda, and AWS Elastic Beanstalk – allowing you to build everything from web applications, mobile backends to data processing applications.
In this session, we will provide an intro level overview of these services and highlight suitable use cases. We will discuss which service to choose to best get your applications up and running on AWS.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
AWS Landing Zone Deep Dive (ENT350-R2) - AWS re:Invent 2018Amazon Web Services
In this session, we discuss how to deploy a scalable environment that considers the AWS account structure, security services, network architecture, and user access. We present an overview of the AWS Landing Zone solution, an automated solution for setting up a robust and flexible AWS environment designed from the collective experience of AWS and our customers. The AWS Landing Zone helps automate the setup of a flexible account structure, security baseline, network structure, and user access based on best practices. Future growth is facilitated by an account vending machine component that simplifies the creation of additional accounts. Learn how the AWS Landing Zone can ensure that you start your AWS journey with the right foundation. We encourage you to attend the full AWS Landing Zone track, including SEC303. Search for #awslandingzone in the session catalog.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
by Bill Reid, Sr. Manager of Solutions Architecture, AWS
This session is designed to introduce you to fundamental cloud computing and AWS security concepts that will help you prepare for the Security Week sessions, demos, and labs.
The AWS cloud infrastructure has been architected to be one of the most flexible and secure cloud computing environments available today. Security for AWS is about three related elements: visibility, auditability, and control. You have to know what you have and where it is before you can assess the environment against best practices, internal standards, and compliance standards. Controls enable you to place precise, well-understood limits on the access to your information. Did you know, for example, that you can define a rule that says that “Tom is the only person who can access this data object that I store with Amazon, and he can only do so from his corporate desktop on the corporate network, from Monday-Friday 9-5 and when he uses MFA?” That’s the level of granularity you can choose to implement if you wish. In this session, we’ll cover these topics to provide a practical understanding of the security programs, procedures, and best practices you can use to enhance your current security posture.
Moving Large Scale Contact Centers to Amazon Connect (BAP324) - AWS re:Invent...Amazon Web Services
In this session, you will learn how Intuit and Hilton are migrating their large scale contact centers to Amazon Connect, a self-service, cloud-based contact center offering based on the same technology used by over 70,000 Amazon Customer Service Associates. We will begin the session with an overview of Amazon Connect and hear from Intuit and Hilton about their experiences and best practices that will help prepare any large scale business planning a migration to Amazon Connect.
Introduction to AWS Cloud Computing | AWS Public Sector Summit 2016Amazon Web Services
Amazon Web Services (AWS) provides on-demand computing resources and services in the cloud, with pay-as-you-go pricing. This session provides an overview and describes how using AWS resources instead of your own is like purchasing electricity from a power company instead of running your own generator. Using AWS resources provides many of the same benefits as a public utility: Capacity exactly matches your need, you pay only for what you use, economies of scale result in lower costs, and the service is provided by a vendor experienced in running large-scale networks. A high-level overview of AWS’s infrastructure (such as AWS Regions and Availability Zones) and AWS services is provided as part of this session.
(AWS Certification Training: https://www.edureka.co/cloudcomputing)
This “Amazon Redshift" tutorial by Edureka will help you understand what Amazon Redshift is & how to set up a data warehouse on cloud using Amazon Redshift. Below are the topics covered in the ppt:
1. Traditional Data Warehouse
2. Amazon Redshift – A to Z
3. Demo on Amazon Redshift
Check out our complete AWS Playlist here: https://goo.gl/8qrfKU
Amazon GuardDuty - Let's Attack My Account! - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- How to safely generate a number of Amazon GuardDuty findings
- How to analyze Amazon GuardDuty findings
- How to think about remediation of threats
AI & ML at Amazon: AWS Developer Workshop - Web Summit 2018Amazon Web Services
AI & Machine Learning at Amazon: AWS Developer Workshop - Web Summit 2018
Amazon has been applying machine learning to create artifical intelligence features within its products and services for over 20 years. Join this session and learn about the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and how you can use easily accessible services from AWS to enable you to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
Predicting the Future with Amazon SageMaker
Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this session you will learn how to use built-in, high performance machine learning algorithms for predictions and computer vision within your application. We will deploy machine learning models into production and start generating classifications with a few API calls using the SageMaker SDK. Additionally we will demonstrate how to run your custom trained machine learning model directly out of your web application to classify incoming user generated content.
Steve Shirkey, ASEAN Solutions Architect, Amazon Web Services
Improve Accessibility Using Machine Learning (AIM332) - AWS re:Invent 2018Amazon Web Services
Machine learning (ML) can help people with disabilities by using facial and object recognition, text-to-speech, automatic translation, and transcription to create assistive applications. In this chalk talk, learn how to assemble ML APIs from AWS to help people in new ways.
Build, train, and deploy machine learning models at scale
Machine learning often feels a lot harder than it should be to most developers because the process to build and train models, and then deploy them into production is too complicated and too slow.
Amazon SageMaker includes modules that can be used together or independently to build, train, and deploy your machine learning models.
Olivier Bergeret - AWS
https://dataxday.fr/
Video available: https://www.youtube.com/watch?v=3eV4x_GR_f8
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
Mike Gillespie - Build Intelligent Applications with AWS ML Services (200).pdfAmazon Web Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Building the Organization of the Future: Leveraging AI & ML Amazon Web Services
Artificial intelligence and machine learning are no longer the stuff of science fiction. Organizations of all sizes are using these tools to create innovative artificial intelligence applications – namely, Amazon.com's own retail experience. Join us for an inside look at how Amazon thinks about this technology, and gain insight into a range of new machine learning services on AWS for use in your own organization.
Alex Coqueiro, Solutions Architect, Amazon Web Services
Manu Sud, Manager, Analytics and Advanced Technology Branch, Ontario Ministry of Economic Development, Job Creation and Trade
by Pratap Ramamurthy, Partner Solutions Architect
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Build Text Analytics Solutions with Amazon Comprehend and Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Speaker: Herbert-John Kelly, AWS
Customer Speaker: Data Prophet
Level: 200
Join us to hear about our strategy for driving machine learning (ML) innovation for our customers and learn what's new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...Amazon Web Services
"Learning Objectives:
- Develop intelligent IoT edge solutions using AWS Greengrass
- Develop data science models in the cloud with Amazon SageMaker
- Learn how AWS Greengrass and Amazon SageMaker enable you to perform machine learning at the edge"
Various Open Source projects around Apache MXNet for you to build End to End pipeline from building Models using 7 different languages to choose from or use Keras if you are already a Keras user.
Get the state of the Art models pre-trained with code and examples using GluonCV and GluonNLP
Use ONNX to create and save models right from MXNet so you can port to any framework.
Use Apache 2.0 Licensed MXNet Model Server to deploy your models.
Use TVM to optimize for your own hardware.
Integrando Machine Learning - da ingestão à persistência - AWS Hugo Rozestraten
Apresentação - Integrando Machine Learning - da ingestão à persistência - AWS - Amazon Web Services
Big Data e Inteligência Artificial
Fast Data
Streaming de Dados
Análise em realtime
Osemeke Isibor, Solutions Architect, AWS
With the launch of several new Machine Learning (ML) services on AWS, now is your chance to learn how to quickly apply ML to solve real-world business problems, no prior ML experience necessary. During this session, you will learn about vision services to analyze your images and video for facial comparison, object detection and detecting text (Amazon Rekognition and Amazon Rekognition Video), building conversational interfaces for chatbots (Amazon Lex), and core language services for converting audio to text (Amazon Transcribe), converting text to speech (Amazon Polly), identifying topics and themes in text (Amazon Comprehend) and translating between two languages (Amazon Translate).
Evolution of media workflows aided by Machine Learning- AWS Summit Cape Town ...Amazon Web Services
Speaker: Usman Shakeel, AWS
Level: 200
This session looks at the evolving M&E workloads from Content creation workflows, to smart Supply Chains and Archives and personalization around content delivery. With this evolution we also discuss AWS machine learning tool set and its application towards this evolution of M&E workloads. Learn some of the cool use cases and architecture patterns using AWS ML tool set on how studios, networks and creative service companies as well as broadcasters are using them from boosting creative productivity, efficiencies and creativity in production planning, animation, visual effects, editorial, post and localization. We’ll see what AI and ML apps are capable of doing right now and glimpse their long-term potential to alter workflows.
AI & Machine Learning at AWS - An IntroductionDaniel Zivkovic
Slides from my "Introduction to AI & ML for AWS Pros" Lunch & Learn presentation. The idea was to (1) bridge the gap between Data Scientists & today's Cloud professionals; (2) spur the imagination of AWS Pros about ML possibilities, and (3) explain the importance of SageMaker - because it's not just another tool in Data Scientist's toolbox, but an amazing End-to-End Machine Learning Platform.
Come costruire servizi di Forecasting sfruttando algoritmi di ML e deep learn...Amazon Web Services
Il Forecasting è un processo importante per tantissime aziende e viene utilizzato in vari ambiti per cercare di prevedere in modo accurato la crescita e distribuzione di un prodotto, l’utilizzo delle risorse necessarie nelle linee produttive, presentazioni finanziarie e tanto altro. Amazon utilizza delle tecniche avanzate di forecasting, in parte questi servizi sono stati messi a disposizione di tutti i clienti AWS.
In questa sessione illustreremo come pre-processare i dati che contengono una componente temporale e successivamente utilizzare un algoritmo che a partire dal tipo di dato analizzato produce un forecasting accurato.
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.
Ora puoi utilizzare Amazon Elastic Kubernetes Service (EKS) per eseguire pod Kubernetes su AWS Fargate, il motore di elaborazione serverless creato per container su AWS. Questo rende più semplice che mai costruire ed eseguire le tue applicazioni Kubernetes nel cloud AWS.In questa sessione presenteremo le caratteristiche principali del servizio e come distribuire la tua applicazione in pochi passaggi
Vent'anni fa Amazon ha attraversato una trasformazione radicale con l'obiettivo di aumentare il ritmo dell'innovazione. In questo periodo abbiamo imparato come cambiare il nostro approccio allo sviluppo delle applicazioni ci ha permesso di aumentare notevolmente l'agilità, la velocità di rilascio e, in definitiva, ci ha consentito di creare applicazioni più affidabili e scalabili. In questa sessione illustreremo come definiamo le applicazioni moderne e come la creazione di app moderne influisce non solo sull'architettura dell'applicazione, ma sulla struttura organizzativa, sulle pipeline di rilascio dello sviluppo e persino sul modello operativo. Descriveremo anche approcci comuni alla modernizzazione, compreso l'approccio utilizzato dalla stessa Amazon.com.
Come spendere fino al 90% in meno con i container e le istanze spot Amazon Web Services
L’utilizzo dei container è in continua crescita.
Se correttamente disegnate, le applicazioni basate su Container sono molto spesso stateless e flessibili.
I servizi AWS ECS, EKS e Kubernetes su EC2 possono sfruttare le istanze Spot, portando ad un risparmio medio del 70% rispetto alle istanze On Demand. In questa sessione scopriremo insieme quali sono le caratteristiche delle istanze Spot e come possono essere utilizzate facilmente su AWS. Impareremo inoltre come Spreaker sfrutta le istanze spot per eseguire applicazioni di diverso tipo, in produzione, ad una frazione del costo on-demand!
In recent months, many customers have been asking us the question – how to monetise Open APIs, simplify Fintech integrations and accelerate adoption of various Open Banking business models. Therefore, AWS and FinConecta would like to invite you to Open Finance marketplace presentation on October 20th.
Event Agenda :
Open banking so far (short recap)
• PSD2, OB UK, OB Australia, OB LATAM, OB Israel
Intro to Open Finance marketplace
• Scope
• Features
• Tech overview and Demo
The role of the Cloud
The Future of APIs
• Complying with regulation
• Monetizing data / APIs
• Business models
• Time to market
One platform for all: a Strategic approach
Q&A
Rendi unica l’offerta della tua startup sul mercato con i servizi Machine Lea...Amazon Web Services
Per creare valore e costruire una propria offerta differenziante e riconoscibile, le startup di successo sanno come combinare tecnologie consolidate con componenti innovativi creati ad hoc.
AWS fornisce servizi pronti all'utilizzo e, allo stesso tempo, permette di personalizzare e creare gli elementi differenzianti della propria offerta.
Concentrandoci sulle tecnologie di Machine Learning, vedremo come selezionare i servizi di intelligenza artificiale offerti da AWS e, anche attraverso una demo, come costruire modelli di Machine Learning personalizzati utilizzando SageMaker Studio.
OpsWorks Configuration Management: automatizza la gestione e i deployment del...Amazon Web Services
Con l'approccio tradizionale al mondo IT per molti anni è stato difficile implementare tecniche di DevOps, che finora spesso hanno previsto attività manuali portando di tanto in tanto a dei downtime degli applicativi interrompendo l'operatività dell'utente. Con l'avvento del cloud, le tecniche di DevOps sono ormai a portata di tutti a basso costo per qualsiasi genere di workload, garantendo maggiore affidabilità del sistema e risultando in dei significativi miglioramenti della business continuity.
AWS mette a disposizione AWS OpsWork come strumento di Configuration Management che mira ad automatizzare e semplificare la gestione e i deployment delle istanze EC2 per mezzo di workload Chef e Puppet.
Scopri come sfruttare AWS OpsWork a garanzia e affidabilità del tuo applicativo installato su Instanze EC2.
Microsoft Active Directory su AWS per supportare i tuoi Windows WorkloadsAmazon Web Services
Vuoi conoscere le opzioni per eseguire Microsoft Active Directory su AWS? Quando si spostano carichi di lavoro Microsoft in AWS, è importante considerare come distribuire Microsoft Active Directory per supportare la gestione, l'autenticazione e l'autorizzazione dei criteri di gruppo. In questa sessione, discuteremo le opzioni per la distribuzione di Microsoft Active Directory su AWS, incluso AWS Directory Service per Microsoft Active Directory e la distribuzione di Active Directory su Windows su Amazon Elastic Compute Cloud (Amazon EC2). Trattiamo argomenti quali l'integrazione del tuo ambiente Microsoft Active Directory locale nel cloud e l'utilizzo di applicazioni SaaS, come Office 365, con AWS Single Sign-On.
Amazon Web Services e VMware organizzano un evento virtuale gratuito il prossimo mercoledì 14 Ottobre dalle 12:00 alle 13:00 dedicato a VMware Cloud ™ on AWS, il servizio on demand che consente di eseguire applicazioni in ambienti cloud basati su VMware vSphere® e di accedere ad una vasta gamma di servizi AWS, sfruttando a pieno le potenzialità del cloud AWS e tutelando gli investimenti VMware esistenti.
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
Crea la tua prima serverless ledger-based app con QLDB e NodeJSAmazon Web Services
Molte aziende oggi, costruiscono applicazioni con funzionalità di tipo ledger ad esempio per verificare lo storico di accrediti o addebiti nelle transazioni bancarie o ancora per tenere traccia del flusso supply chain dei propri prodotti.
Alla base di queste soluzioni ci sono i database ledger che permettono di avere un log delle transazioni trasparente, immutabile e crittograficamente verificabile, ma sono strumenti complessi e onerosi da gestire.
Amazon QLDB elimina la necessità di costruire sistemi personalizzati e complessi fornendo un database ledger serverless completamente gestito.
In questa sessione scopriremo come realizzare un'applicazione serverless completa che utilizzi le funzionalità di QLDB.
Con l’ascesa delle architetture di microservizi e delle ricche applicazioni mobili e Web, le API sono più importanti che mai per offrire agli utenti finali una user experience eccezionale. In questa sessione impareremo come affrontare le moderne sfide di progettazione delle API con GraphQL, un linguaggio di query API open source utilizzato da Facebook, Amazon e altro e come utilizzare AWS AppSync, un servizio GraphQL serverless gestito su AWS. Approfondiremo diversi scenari, comprendendo come AppSync può aiutare a risolvere questi casi d’uso creando API moderne con funzionalità di aggiornamento dati in tempo reale e offline.
Inoltre, impareremo come Sky Italia utilizza AWS AppSync per fornire aggiornamenti sportivi in tempo reale agli utenti del proprio portale web.
Database Oracle e VMware Cloud™ on AWS: i miti da sfatareAmazon Web Services
Molte organizzazioni sfruttano i vantaggi del cloud migrando i propri carichi di lavoro Oracle e assicurandosi notevoli vantaggi in termini di agilità ed efficienza dei costi.
La migrazione di questi carichi di lavoro, può creare complessità durante la modernizzazione e il refactoring delle applicazioni e a questo si possono aggiungere rischi di prestazione che possono essere introdotti quando si spostano le applicazioni dai data center locali.
In queste slide, gli esperti AWS e VMware presentano semplici e pratici accorgimenti per facilitare e semplificare la migrazione dei carichi di lavoro Oracle accelerando la trasformazione verso il cloud, approfondiranno l’architettura e dimostreranno come sfruttare a pieno le potenzialità di VMware Cloud ™ on AWS.
Amazon Elastic Container Service (Amazon ECS) è un servizio di gestione dei container altamente scalabile, che semplifica la gestione dei contenitori Docker attraverso un layer di orchestrazione per il controllo del deployment e del relativo lifecycle. In questa sessione presenteremo le principali caratteristiche del servizio, le architetture di riferimento per i differenti carichi di lavoro e i semplici passi necessari per poter velocemente migrare uno o più dei tuo container.
Durante i laboratori pratici, gli esperti AWS ti mostrano quali strumenti aiutano a sviluppare le applicazioni Serverless in locale e nel cloud AWS e ti aiuteranno a programmare i prossimi passi per iniziare ad utilizzare questa tecnologia nella tua azienda.