How to Use Jupyter Notebooks with Amazon EMR for Better Productivity (ANT387) - AWS re:Invent 2018
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Adoption of Jupyter Notebooks gives big data customers an easy way to leverage the open source Apache Spark in Amazon EMR for data science, interactive analytics, and to prototype jobs before bringing them to production. In this builders session, we show you how to make Amazon EMR an even simpler to use platform for developers and data engineers, increasing their productivity as they collaborate on different workloads and different clusters.
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Scaling Up To and Beyond 10M Users
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As data exponentially grows in organizations, there is an increasing need to use machine learning (ML) to gather insights from this data at scale and to use those insights to perform real-time predictions on incoming data. In this workshop, we walk you through how to train an Apache Spark model using Amazon SageMaker that is pointed to Apache Livy and running on an Amazon EMR Spark cluster. We also show you how to host the Spark model on Amazon SageMaker to serve a RESTful inference API. Finally, we show you how to use the RESTful API to serve real-time predictions on streaming data from Amazon Kinesis Data Streams.
利用 Fargate - 無伺服器的容器環境建置高可用的系統
Build a highly scalable system with serverless container environment - Fargate
利用 Fargate - 無伺服器的容器環境建置高可用的系統
Level: 200, 中文演講
講師: Jayson Hsieh, Solution Architect, AWS & Alan Hsieh, R&D Manager, Growth Machine
使用無伺服器的 Fargate 來部屬 docker 容器環境，是應用服務面對高擴充性、高可用性需求時很好的選擇，免除了底層虛擬機器的維運工作。在這個 session 之中，我們會聊聊以 Fargate 為主體的無伺服器容器環境如何融入 DevOps，並且聽聽 Growth Machine 如何運營上百個 Fargate 容器來滿足高彈性擴張的需求。
聯繫銷售: https://aws.amazon.com/tw/contact-us/
與銷售線上聊天: https://pages.awscloud.com/tw-hkt-sales-chat.html
Building State-of-the-Art Computer Vision Models Using MXNet and Gluon (AIM36...
The document discusses GluonCV, an open source computer vision toolkit for Apache MXNet and Gluon. GluonCV provides state-of-the-art pre-trained models for classification, detection, segmentation, and other tasks. It offers easy and fast development of vision models as well as deployment of trained models. Examples of pre-trained models and their accuracy are shown for classification, detection, and segmentation tasks. Information on getting started with GluonCV, MXNet, and Gluon NLP is also provided.
Enabling Deep Learning in IoT Applications with Apache MXNet
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.
Modernize Your Desktop and Application Delivery with AWS - AWS Online Tech Talks
The document discusses modernizing desktop and application delivery with AWS. It provides an overview of Amazon WorkSpaces and Amazon AppStream 2.0 for providing managed cloud desktops and streaming applications. It also discusses integrating these services with Active Directory, accessing corporate resources, and bringing the solutions together for security, flexibility and cost savings compared to on-premises virtual desktop infrastructure.
End User Collaboration on AWS - AWS Online Tech Talks
Learning Objectives:
- Learn how to deploy your desktops and apps on AWS with Amazon WorkSpaces and Amazon AppStream 2.0
- Learn best practices when moving your desktops and apps to AWS
- Learn how to integrate Amazon WorkSpaces and Amazon AppStream 2.0 with your existing identity, security, networking, and storage solutions
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IT organizations today need to support a modern, flexible, and global workforce and ensure that their users can be productive anywhere. Moving desktops and applications to the AWS Cloud offers improved security, scale, and performance with cloud economics. In this session, we provide an overview of Amazon WorkSpaces and Amazon AppStream 2.0, and we discuss use cases for each. Then, we dive deep into best practices for implementing Amazon WorkSpaces and AppStream 2.0, including how to integrate with your existing identity, security, networking, and storage solutions.
Estate and Patch Management Infrastructure and Operations as Code
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The machine learning process 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.
This workshop starts with a brief review of the machine learning process, followed by an introduction and deep dive into the individual components of Amazon SageMaker. As part of the workshop we will train artificial neural networks, get insight into some of the built-in machine learning algorithms of SageMaker that you can use for a variety of problem types, and after successfully training a model, look at options on how to deploy and scale a model as a service.
This workshop is aimed at developers that are new to machine learning, as well as data scientists that continue to be challenged by the operational challenges of the machine learning process. Bring your own laptop with Python and Jupyter Notebook, and (ideally) your own activated AWS account to follow through the examples.
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...
"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"
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In this workshop, learn how to build and deploy computer vision models using the AWS DeepLens deep learning-enabled video camera. Learn to build a machine learning model from scratch using Amazon SageMaker, and get hands-on experience with AWS DeepLens by extending that model to build an end-to-end AI application using Amazon Rekognition. Attendees also learn about use cases built by the community which integrate other AWS services and extend the functionality of AWS DeepLens. Please note, you must have an AWS account to participate in this workshop. If setting up a new account, please do so at least 24 hours in advance of the workshop.
End to End Model Development to Deployment using SageMaker
End to End Model Development to Deployment Using SageMaker
In this session we would be developing a model for image classification model (a convolutional neural network, or CNN). We would start off with some theory about CNNs, explore how they learn an image and then proceed towards hands-on lab. We would be using Amazon SageMaker to develop the model in Python, train the model and then to finally create an endpoint and run inference against it. We would be using a custom Conda Kernel for this exercise and would be looking at leveraging SageMaker features like LifeCycle Configurations to help us prepare the notebook before launch. Finally we would be deploying the model in production and run inference against it. We would also be able to monitor various parameters for endpoint performance such as endpoint’s CPU/Memory and Model inference performance metrics.
Level: 200-300
AWS Machine Learning Week SF: End to End Model Development Using SageMaker
This document describes Amazon SageMaker's capabilities for end-to-end machine learning model development and deployment. It discusses how SageMaker provides pre-built algorithms and frameworks, managed training and hosting services, and the ability to customize models with user-provided algorithms or frameworks like fast.ai. The document provides an example workflow of using SageMaker to build, train, and deploy a fast.ai model for inference.
Introducing Amazon SageMaker - AWS Online Tech Talks
Learning Objectives:
- Learn how Amazon SageMaker can be used for exploratory data analysis before training
- Learn how Amazon SageMaker provides managed distributed training with flexibility
- Learn how easy it is to deploy your models for hosting within Amazon SageMaker
Securing Machine Learning Deployments for the Enterprise (SEC369-R1) - AWS re...
Managing that balance between running machine learning (ML) and deep learning algorithms and following approved security practices from your security organization is critical in keeping your data and your customers’ data safe. In this chalk talk, we introduce a common ML/deep learning flow using Amazon SageMaker, identify various points of data exchange with Amazon EMR and Amazon S3, and dive deep into the security and governance of the data and infrastructure. Learn about using security controls and building processes with services like AWS CloudTrail, AWS Config, Amazon CloudWatch Events, and AWS Lambda. Gain an understanding of the options and best practices for running ML and deep learning on AWS.Please join us for a speaker meet-and-greet following this session at the Speaker Lounge (ARIA East, Level 1, Willow Lounge). The meet-and-greet starts 15 minutes after the session and runs for half an hour.
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AWS Machine Learning Week SF: End to End Model Development Using SageMaker
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Introducing Amazon SageMaker - AWS Online Tech Talks
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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.
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Event Agenda :
Open banking so far (short recap)
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• Scope
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How to Use Jupyter Notebooks with Amazon EMR for Better Productivity (ANT387) - AWS re:Invent 2018
- 2. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. EMR Notebooks A managed analytics environment based on Jupyter Notebooks A N T 3 8 7 Vignesh Rajamani Senior Product Manager Amazon Web Services
- 3. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Agenda What is EMR Notebooks? Why should I use it? How does it work? Demo How do I access it? Q&A
- 4. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. EMR Notebooks A managed analytics environment based on Jupyter Notebooks Amazon EMR clusters User S3 bucket AWS Management Console for EMR EMR-managed notebook based on Jupyter notebook users Customer VPC EMR VPC
- 5. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. Why should I use it? • Build applications, prepare and visualize data, collaborate with peers, and run interactive analysis through PySpark, Spark SQL, Spark R and Scala • Create multiple notebooks instantly from the console, attach them to a minimum 1-node EMR cluster and immediately start experimenting with Apache Spark. • Monitor the progress of your job from within the notebook with the integrated Spark monitor. • Visualize your results in rich graphical plots using the pre-installed open-source libraries from Anaconda. • Detach your notebook from clusters and re-attach to a different cluster suitable for your workload • Durably persist your work to Amazon S3 and share with others. Easily retrieve saved work from the console. • Easily set up a multi-tenant cluster for your data- scientists and developers and make the most use of your EMR cluster • No need to deploy, maintain, and upgrade software or notebook instances • Provide secure access to notebooks without providing multiuser access to the master node. • Safely terminate your cluster without fear of losing notebooks • Set up fine-grained access control to notebooks and clusters through IAM policies • Track and audit your notebook users by enforcing user impersonation As a Data Scientist or Developer As an IT Administrator
- 6. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.
- 7. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved. How do I access it? Log into the AWS Management Console for Amazon EMR Create a IAM user policy allowing permissions to use notebook Use the default service role for notebook with an AWS managed policy or provide your custom service role with custom policies for the notebook Launch or have a running cluster with Amazon EMR release 5.18.0 or later with Spark and Livy installed Provide an Amazon S3 path in the same region as your EMR cluster Click on create notebook button to open notebook UI
- 8. © 2018, Amazon Web Services, Inc. or its affiliates. All rights reserved.