Amazon has been investing deeply in artificial intelligence (AI) for over 20 years. Machine learning (ML) algorithms drive many of its internal systems. It is also core to the capabilities Amazon's customers experience – from the path optimization in the fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, drone initiative Prime Air, and the new retail experience Amazon Go. This is just the beginning. Amazon's mission is to share learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.
by Yash Pant, Enterprise Solutions Architect AWS
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walk through the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Working with Amazon SageMaker Algorithms for Faster Model TrainingAmazon Web Services
by Amit Sharma, Principal Solutions Architect AWS
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
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/
Machine Learning State of the Union - MCL210 - re:Invent 2017Amazon Web Services
Join us to hear about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. Swami Sivasubramanian, VP of Amazon Machine Learning, 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.
Enabling Deep Learning in IoT Applications with Apache MXNetAmazon Web Services
by Pratap Ramamurthy, SDM and Hagay Lupesko SDM
Many state of the art deep learning models have hefty compute, storage and power consumption requirements which make them impractical or difficult to use on resource-constrained devices. In this TechTalk, you'll learn why Apache MXNet, an open Source library for Deep Learning, is IoT-friendly in many ways. In addition, you'll learn how services like Amazon SageMaker, AWS Lambda, AWS Greengrass, and AWS DeepLens make it easy to deploy MXNet models on edge devices.
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Using Amazon SageMaker to build, train, and deploy your ML ModelsAmazon Web Services
by Gitansh Chadha, Solutions Architect AWS
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. 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.
by Yash Pant, Enterprise Solutions Architect AWS
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walk through the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Working with Amazon SageMaker Algorithms for Faster Model TrainingAmazon Web Services
by Amit Sharma, Principal Solutions Architect AWS
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
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/
Machine Learning State of the Union - MCL210 - re:Invent 2017Amazon Web Services
Join us to hear about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. Swami Sivasubramanian, VP of Amazon Machine Learning, 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.
Enabling Deep Learning in IoT Applications with Apache MXNetAmazon Web Services
by Pratap Ramamurthy, SDM and Hagay Lupesko SDM
Many state of the art deep learning models have hefty compute, storage and power consumption requirements which make them impractical or difficult to use on resource-constrained devices. In this TechTalk, you'll learn why Apache MXNet, an open Source library for Deep Learning, is IoT-friendly in many ways. In addition, you'll learn how services like Amazon SageMaker, AWS Lambda, AWS Greengrass, and AWS DeepLens make it easy to deploy MXNet models on edge devices.
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this workshop, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Using Amazon SageMaker to build, train, and deploy your ML ModelsAmazon Web Services
by Gitansh Chadha, Solutions Architect AWS
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. 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.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019AWS Summits
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
In this session you will get to see AWS DeepLens in action! You will learn how AWS DeepLens empowers developers of all skill levels to get started with deep learning in less than 10 minutes by providing sample projects with practical, hands-on examples which can start running with a single click. In this session you will get an overview of how to build and deploy computer vision models, such as face detection using Amazon SageMaker and AWS DeepLens and learn about some of the great use cases that bring together multiple AWS services to create new to the world deep-learning enabled innovation.
Working with Amazon SageMaker Algorithms for Faster Model TrainingAmazon Web Services
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
Building Deep Learning Applications with TensorFlow and Amazon SageMakerAmazon Web Services
by Steve Shirkey, Solutions Architect ASEAN
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. In this workshop, you’ll learn how to get started with the TensorFlow deep learning framework using Amazon SageMaker, a platform to easily build, train and deploy models at scale. You’ll learn how to build a model using TensorFlow by setting up a Jupyter notebook to get started with image and object recognition. You’ll also learn how to quickly train and deploy a model through Amazon.
Introduction to Serverless computing and AWS Lambda - Floor28Boaz Ziniman
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With Serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more.
In this session, we will learn how to get started with Serverless computing using AWS Lambda, which lets you run code without provisioning or managing servers.
Build Deep Learning Applications with TensorFlow and Amazon SageMakerAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. In this workshop, you’ll learn how to get started with the TensorFlow deep learning framework using Amazon SageMaker, a platform to easily build, train and deploy models at scale. You’ll learn how to build a model using TensorFlow by setting up a Jupyter notebook to get started with image and object recognition. You’ll also learn how to quickly train and deploy a model through Amazon
Microservices on AWS: Architectural Patterns and Best Practices | AWS Summit ...AWS Summits
This session is the first of 5 sessions that will cover a fully functioning system we have built to demonstrate how to rapidly develop systems using the AWS platform. This session we will start with a demo and an architecture review in which we will break into the different subsystems. In the second part of the session we will zoom into the Microservices part of the solution.Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. This session demonstrates the use of services like Amazon ECS, AWS Cloud Map and Amazon API Gateway and can help you understand where you can utilize microservices architecture in your own organization and understand areas of potential savings and increased agility.
Using Amazon SageMaker to Build, Train, and Deploy Your ML ModelsAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. 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.
Artifical Intelligence and Machine Learning 201, AWS Federal Pop-Up LoftAmazon Web Services
Come join us for a one-day session where you will learn about the science of computer vision (CV) and train custom CV models utilizing Amazon SageMaker. In this course, you'll learn about Amazon's managed machine learning platform and utilize publicly available real-world ground truth data sets to train models leveraging the built-in ML algorithms of Amazon SageMaker to detect objects and buildings. This is a hands-on workshop, attendees should bring your own laptops.
Learn how Amazon EC2 Spot and Thinkbox Deadline can make your VFX and CG renders explode off the screen, with minimal effort and low cost. This session focuses on rendering workloads combining Deadline (an AWS rendering pipeline management service), Thinkbox Marketplace usage-based licensing for flexible render licensing, and Spot for scalable low-cost computing. Learn how to seamlessly integrate your existing production pipeline, as well as advanced asset management, synchronization, and connections to other AWS services such as AWS storage and networking for advanced workflows, including the extension of on-premises render workflows into the cloud and all-in-cloud rendering pipelines. We also highlight a few real-world examples of customers with actual Hollywood productions.
Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past you had to provision and scale servers to run your application code install and operate distributed databases and build and run custom software to handle API requests. Now AWS provides a stack of scalable fully-managed services that eliminates these operational complexities. In this session you will learn about the the basics of serverless and especially how your business can benefit from it.
Slides from my talk at the Data Innovations Summit on MXNet Model Server.
https://www.datainnovationsummit.com/
Apache MXNet Model Server (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).
https://github.com/awslabs/mxnet-model-server
SageMaker Algorithms Infinitely Scalable Machine LearningAmazon Web Services
by Nick Brandaleone, Solutions Architect, AWS
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This session will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
Developing deep learning applications just got even simpler and faster. In this session, you will learn how to program deep learning models using Gluon, the new intuitive, dynamic programming interface available for the Apache MXNet open-source framework. We’ll also explore neural network architectures such as multi-layer perceptrons, convolutional neural networks (CNNs) and LSTMs.
IOT311_Customer Stories of Things, Cloud, and Analytics on AWSAmazon Web Services
In this session, AWS IoT customers talk about the nuances, successes, and challenges of running large-scale IoT deployments on AWS. Hear from customers who have been operating on AWS IoT. Learn from their war stories of development and their architectural recommendations on technical best practices on IoT.
Artificial Intelligence (Machine Learning) on AWS: How to StartVladimir Simek
Amazon has been investing deeply in artificial intelligence (AI) for over 20 years. Machine learning (ML) algorithms drive many of its internal systems. It is also core to the capabilities Amazon's customers experience – from the path optimization in the fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, drone initiative Prime Air, and the new retail experience Amazon Go. This is just the beginning. Amazon's mission is to share learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.
If you are interested, how can you develop ML-based smart applications on the AWS platform, and want to see a couple of cool demos, join us for the next AWS meetup. AWS Solutions Architect, Vladimir Simek, will be presenting the full AWS portfolio for AI and ML - from virtual servers enabled for training Deep Learning models up to a fully managed API-based services.
Maschinelles Lernen auf AWS für Entwickler, Data Scientists und ExpertenAWS Germany
In diesem Vortrag geben wir einen Überblick mit Beispielen über aktuelle Werkzeuge für Maschinelles Lernen (ML) auf AWS. Dieser überblick deckt alle Möglichkeiten von einfach zu nutzenden, vollständig verwalteten ML-Services für Entwickler über ML-Plattformen für Data Scientists bis hin zu ML-optimierten Infrastruktur- und Software-Komponenten ab. Beispiele und Online-Demos zeigen, wie einfach ML-Methoden auf AWS genutzt werden können.
Moderator: Christian Petters, Solutions Architect, AWS
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019AWS Summits
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
In this session you will get to see AWS DeepLens in action! You will learn how AWS DeepLens empowers developers of all skill levels to get started with deep learning in less than 10 minutes by providing sample projects with practical, hands-on examples which can start running with a single click. In this session you will get an overview of how to build and deploy computer vision models, such as face detection using Amazon SageMaker and AWS DeepLens and learn about some of the great use cases that bring together multiple AWS services to create new to the world deep-learning enabled innovation.
Working with Amazon SageMaker Algorithms for Faster Model TrainingAmazon Web Services
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This webinar will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
Building Deep Learning Applications with TensorFlow and Amazon SageMakerAmazon Web Services
by Steve Shirkey, Solutions Architect ASEAN
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. In this workshop, you’ll learn how to get started with the TensorFlow deep learning framework using Amazon SageMaker, a platform to easily build, train and deploy models at scale. You’ll learn how to build a model using TensorFlow by setting up a Jupyter notebook to get started with image and object recognition. You’ll also learn how to quickly train and deploy a model through Amazon.
Introduction to Serverless computing and AWS Lambda - Floor28Boaz Ziniman
Serverless computing allows you to build and run applications without the need for provisioning or managing servers. With Serverless computing, you can build web, mobile, and IoT backends; run stream processing or big data workloads; run chatbots, and more.
In this session, we will learn how to get started with Serverless computing using AWS Lambda, which lets you run code without provisioning or managing servers.
Build Deep Learning Applications with TensorFlow and Amazon SageMakerAmazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding and recommendation engines. In this workshop, you’ll learn how to get started with the TensorFlow deep learning framework using Amazon SageMaker, a platform to easily build, train and deploy models at scale. You’ll learn how to build a model using TensorFlow by setting up a Jupyter notebook to get started with image and object recognition. You’ll also learn how to quickly train and deploy a model through Amazon
Microservices on AWS: Architectural Patterns and Best Practices | AWS Summit ...AWS Summits
This session is the first of 5 sessions that will cover a fully functioning system we have built to demonstrate how to rapidly develop systems using the AWS platform. This session we will start with a demo and an architecture review in which we will break into the different subsystems. In the second part of the session we will zoom into the Microservices part of the solution.Microservices are an architectural and organizational approach to software development where software is composed of small independent services that communicate over well-defined APIs. This session demonstrates the use of services like Amazon ECS, AWS Cloud Map and Amazon API Gateway and can help you understand where you can utilize microservices architecture in your own organization and understand areas of potential savings and increased agility.
Using Amazon SageMaker to Build, Train, and Deploy Your ML ModelsAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. 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.
Artifical Intelligence and Machine Learning 201, AWS Federal Pop-Up LoftAmazon Web Services
Come join us for a one-day session where you will learn about the science of computer vision (CV) and train custom CV models utilizing Amazon SageMaker. In this course, you'll learn about Amazon's managed machine learning platform and utilize publicly available real-world ground truth data sets to train models leveraging the built-in ML algorithms of Amazon SageMaker to detect objects and buildings. This is a hands-on workshop, attendees should bring your own laptops.
Learn how Amazon EC2 Spot and Thinkbox Deadline can make your VFX and CG renders explode off the screen, with minimal effort and low cost. This session focuses on rendering workloads combining Deadline (an AWS rendering pipeline management service), Thinkbox Marketplace usage-based licensing for flexible render licensing, and Spot for scalable low-cost computing. Learn how to seamlessly integrate your existing production pipeline, as well as advanced asset management, synchronization, and connections to other AWS services such as AWS storage and networking for advanced workflows, including the extension of on-premises render workflows into the cloud and all-in-cloud rendering pipelines. We also highlight a few real-world examples of customers with actual Hollywood productions.
Serverless architectures let you build and deploy applications and services with infrastructure resources that require zero administration. In the past you had to provision and scale servers to run your application code install and operate distributed databases and build and run custom software to handle API requests. Now AWS provides a stack of scalable fully-managed services that eliminates these operational complexities. In this session you will learn about the the basics of serverless and especially how your business can benefit from it.
Slides from my talk at the Data Innovations Summit on MXNet Model Server.
https://www.datainnovationsummit.com/
Apache MXNet Model Server (MMS) is a flexible and easy to use tool for serving deep learning models exported from MXNet or the Open Neural Network Exchange (ONNX).
https://github.com/awslabs/mxnet-model-server
SageMaker Algorithms Infinitely Scalable Machine LearningAmazon Web Services
by Nick Brandaleone, Solutions Architect, AWS
Amazon SageMaker is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models, at any scale. Amazon SageMaker provides high-performance, machine learning algorithms optimized for speed, scale, and accuracy, to perform training on petabyte-scale data sets. This session will introduce you to the collection of distributed streaming ML algorithms that come with Amazon SageMaker. You will learn about the difference between streaming and batch ML algorithms, and how SageMaker has been architected to run these algorithms at scale. We will demo Neural Topic Modeling of text documents using a sample SageMaker Notebook, which will be made available to attendees.
Developing deep learning applications just got even simpler and faster. In this session, you will learn how to program deep learning models using Gluon, the new intuitive, dynamic programming interface available for the Apache MXNet open-source framework. We’ll also explore neural network architectures such as multi-layer perceptrons, convolutional neural networks (CNNs) and LSTMs.
IOT311_Customer Stories of Things, Cloud, and Analytics on AWSAmazon Web Services
In this session, AWS IoT customers talk about the nuances, successes, and challenges of running large-scale IoT deployments on AWS. Hear from customers who have been operating on AWS IoT. Learn from their war stories of development and their architectural recommendations on technical best practices on IoT.
Artificial Intelligence (Machine Learning) on AWS: How to StartVladimir Simek
Amazon has been investing deeply in artificial intelligence (AI) for over 20 years. Machine learning (ML) algorithms drive many of its internal systems. It is also core to the capabilities Amazon's customers experience – from the path optimization in the fulfillment centers, and Amazon.com’s recommendations engine, to Echo powered by Alexa, drone initiative Prime Air, and the new retail experience Amazon Go. This is just the beginning. Amazon's mission is to share learnings and ML capabilities as fully managed services, and put them into the hands of every developer and data scientist.
If you are interested, how can you develop ML-based smart applications on the AWS platform, and want to see a couple of cool demos, join us for the next AWS meetup. AWS Solutions Architect, Vladimir Simek, will be presenting the full AWS portfolio for AI and ML - from virtual servers enabled for training Deep Learning models up to a fully managed API-based services.
Maschinelles Lernen auf AWS für Entwickler, Data Scientists und ExpertenAWS Germany
In diesem Vortrag geben wir einen Überblick mit Beispielen über aktuelle Werkzeuge für Maschinelles Lernen (ML) auf AWS. Dieser überblick deckt alle Möglichkeiten von einfach zu nutzenden, vollständig verwalteten ML-Services für Entwickler über ML-Plattformen für Data Scientists bis hin zu ML-optimierten Infrastruktur- und Software-Komponenten ab. Beispiele und Online-Demos zeigen, wie einfach ML-Methoden auf AWS genutzt werden können.
Moderator: Christian Petters, Solutions Architect, AWS
by Roy Ben-Alta, Business Development Manager, AWS
Amazon SageMaker is a fully managed platform for data scientists and developers to build, train and deploy machine learning models in production applications. In this session, you will learn how to integrate Amazon SageMaker with other AWS services in order to meet enterprise requirements. Using Amazon S3, Amazon Glue, Amazon KMS, Amazon SageMaker, Amazon CodeStar, Amazon ECR, IAM; we will walkthrough the machine learning lifecycle in an integrated AWS environment and discuss best practices. Attendees must have some familiarities with AWS products as well as a good understanding of machine learning theory. The dataset for the workshop will be provided.
Integrating Deep Learning into your Enterprise
In this workshop we return to one of the popular Machine Learning Framework - scikit-learn. We scikit-learn's decision tree classifier to train the model. Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. We follow the whole machine learning pipeline from algorithm selection, training and finally deployment of an endpoint. We would be working with the widely available Iris dataset and the endpoint would be predicting what species the sample belongs to from the Sepal width and length, Petal width and length. Through this workshop we would know all the internal details of how we use containers to train and deploy our machine learning workloads.
Level: 300-400
AWS Machine Learning Week SF: Integrating Deep Learning into Your EnterpriseAmazon Web Services
AWS Machine Learning Week SF: Integrating Deep Learning into your Enterprise
Hands on Workshop based on BYOD Scikit learn and use of Docker containers in the workflow. More detailed description forthcoming.
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. 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.
Building Your Smart Applications with Machine Learning on AWS | AWS WebinarAmazon Web Services
Machine Learning (ML) has long been an arcane topic, accessible only to experts. In this webinar, you will learn how to easily add Amazon API-driven ML services to your education software. Image and video analysis, text-to-speech, speech-to-text, translation, natural language processing: all these are just an API call away. Through code-level demos, we'll show you how to quickly start integrating these services into your education offerings, with zero ML expertise required.
Speaker: Julien Simon, Principal Evangelist AI/ML EMEA, Amazon Web Services
Learn more: https://aws.amazon.com/education
View the video recording here: https://youtu.be/Dsj5KgER6ec
Financial services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, hear how IHS Markit has used machine learning on AWS to help global banking institutions manage their commodities portfolios. Learn how Amazon Machine Learning can take the hassle out of AI.
Accelerating Apache MXNet Models on Apple Platforms Using Core ML - MCL311 - ...Amazon Web Services
Running deep learning models on devices at the edge is one of the hottest trends in AI today. This workshop provides a tutorial on developing and training deep learning models with Apache MXNet and walks you through how to easily bring them into the Apple ecosystem of products. You will learn how to convert MXNet models easily and efficiently to formats that can be integrated into iOS/macOS applications. To participate in this workshop, attendees will require an Apple MacBook running the latest OS (10.13). An iPhone running iOS 11+ or higher to run Core ML and Apache MXNet is optional.
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. In this session, we'll focus on training and deploying Deep Learning models with popular libraries like TensorFlow, Keras, Apache MXNet or PyTorch.
Devoxx: Building AI-powered applications on AWSAdrian Hornsby
Slides from my talk at devoxx2018
The video: https://www.youtube.com/watch?v=-izfBVlHkSc
https://cfp.devoxx.be/2017/talk/XEO-9942/Building_Serverless_AI-powered_Applications_on_AWS
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"
In this webinar, you will learn how to easily add Amazon AI services to your own applications. Find out how to access image and video analysis, text to speech, speech to text, translation, natural language processing: all of which are just an API call away.
Through code-level demos of Amazon SageMaker, Amazon Translate, Amazon Polly, Amazon Transcribe, Amazon Comprehend, Amazon Rekognition, we'll show you how to quickly get started with these services, with zero AI expertise required.
Spoločnosti na celom svete presúvajú svoje aplikácie do cloudu tak rýchlo, ako sa len dá, aby sa stali flexibilnejšími a znížili náklady. Niektoré aplikácie však musia ostať v lokálnych dátacentrách, či už z dôvodu nízkej latencie alebo požiadaviek na miestne spracovanie údajov. Riešenie AWS Outposts prináša plne spravované cloudové služby a infraštruktúru do akéhokoľvek dátového centra. Rovnaké API rozhranie cez grafickú konzolu, príkazový riadok či SDK bez ohľadu na to, či je aplikácia v cloude alebo v AWS Outpost umožňuje naplno využiť model hybridného cloudu bez kompromisov. V tomto webinári vám predstavíme fungovanie AWS Outposts, rovnako ako prípady použitia v reálnej zákazníckej prevádzke.
AWS CZSK Webinar - Migrácia desktopov a aplikácií do AWS cloudu s Amazon Work...Vladimir Simek
V polovici januára 2020 skončila rozšírená podpora operačného systému Windows 10. Mnoho organizácií stojí pred rozhodnutí, či investovať do existujúcej infraštrukúry alebo radšej poskytnú svojim používateľom flexibilnejšie a modernejšie riešenie - dostupné odkiaľkoľvek a na akomkoľvek zariadení. Presun desktopov a aplikácií do AWS cloudu ponúka vylepšené zabezpečenie, škálovateľnosť, flexibilitu a vyšší výkon. V tomto webinári vám poskytneme prehľad služieb Amazon WorkSpaces a Amazon AppStream 2.0 a ukážeme vám, aké ľahké je začať ich používať.
Serverless on AWS: Architectural Patterns and Best PracticesVladimir Simek
When speaking about serverless on AWS, most people think about AWS Lambda. But there's more than than. AWS provides a set of fully managed services that you can use to build and run serverless applications. Serverless applications don’t require provisioning, maintaining, and administering servers for backend components such as compute, databases, storage, stream processing, message queuing, and more. You also no longer need to worry about ensuring application fault tolerance and availability. Instead, AWS handles all of these capabilities for you. This allows you to focus on product innovation while enjoying faster time-to-market.
Tak ako cloud znížil náklady na ukladanie a procesovanie dát a objavila sa nová generácia aplikácií, vznikli nové požiadavky na databázy. Tieto aplikácie potrebujú databázy na ukladanie tera- či petabajtov dát, nových typov údajov, odozvy v milisekundách, schopnosť spracovať milióny požiadaviek za sekundu od miliónov užívateľov kdekoľvek na svete. Na podporu takýchto požiadaviek potrebujete relačné aj nerelačné databázy, ktoré sú navrhnuté tak, aby vyhovovali špecifickým potrebám vašich aplikácií.
Ak sa chcete dozvedieť viac, aké databázové systémy môžete použiť na AWS pre vaše aplikácie, pripojte sa k nášmu ďalšiemu AWS česko-slovenskému webináru. Budeme demonštrovať rôzne databázové riešenia na AWS, popíšeme prípady použitia, najlepšie postupy a ukážeme niekoľko ukážok.
Premiéra: 09/07/2019
AWS CZSK Webinář 2019.05: Jak chránit vaše webové aplikace před DDoS útokyVladimir Simek
DDoS a další webové útoky (XSS, SQL injection) vedené na vaši infrastrukturu mohou negativně ovlivnit dostupnost vašich aplikací, ohrozit jejich bezpečnost a zvyšovat vaše náklady. Jestli se zajímáte o ochranu webových aplikací, sledujte další díl našeho Česko-Slovenského AWS webináře a dozvíte se víc o doporučených postupech i tom, jak používat služby Amazon CloudFront, AWS WAF, AWS Firewall Manager a AWS Shield.
Česko-Slovenský AWS Webinář 07 - Optimalizace nákladů v AWSVladimir Simek
Široká škála služeb a cenových možností, které AWS nabízí, umožnuje flexibilitu efektivního řízení nákladů a udržení výkonu a kapacity, kterou vaše podnikání vyžaduje. Díky AWS cloudu můžete snadno spravovat své zdroje, využívat rezervované instance a používat výkonné nástroje pro správu nákladů, abyste mohli sledovat své náklady.
AWS Česko-Slovenský Webinár 03: Vývoj v AWSVladimir Simek
Služba Amazon Web Services poskytuje vysoce spolehlivou, škálovatelnou a nízkorozpočtovou cloudovou platformu, kterou používají stovky tisíc firem v 190 zemích po celém světě. Startupy, malé a střední podniky, velké enterprise firmy a zákazníci ve veřejném sektoru mají přístup ke stavebním kamenům, které slouží na rychlý vývoj aplikací jako reakce na měnící se obchodní požadavky. Bez ohledu na to, zda chcete vytvářet webové nebo mobilní aplikace, prípadně postavené na klasických serverech či kontejnerech, AWS davá vývojářům do rukou mnoho nástrojů, které jim pomáhají vytvářet a nasazovat aplikace jednoduše, rychle a při nízkých nákladech.
Technical dive to how gaming companies use AWS to make sure they can deliver faster and better games to their users. We will talk about game studios like Rovio, Ubisoft, EA, Supercell, Zynga.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
4. A system or service which can perform tasks
that usually require human intelligence
Artificial Intelligence
5. is a field of computer science that gives
computers the ability to learn without being
explicitly programmed.
Machine Learning
Supervised Unsupervised