Talk at Floor 28, Tel Aviv.
Infrastructure, tips to speed up training, hyperparameter optimization, model compilation, Amazon SageMaker Neo, cost optimization, Amazon Elastic Inference
Building Machine Learning Inference Pipelines at Scale (July 2019)Julien SIMON
Talk at OSCON, Portland, 18/07/2019
Real-life Machine Learning applications require more than a single model. Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. Predictions may need post-processing: filtering, sorting, combining, etc.
Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker)
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Building Machine Learning Inference Pipelines at Scale (July 2019)Julien SIMON
Talk at OSCON, Portland, 18/07/2019
Real-life Machine Learning applications require more than a single model. Data may need pre-processing: normalization, feature engineering, dimensionality reduction, etc. Predictions may need post-processing: filtering, sorting, combining, etc.
Our goal: build scalable ML pipelines with open source (Spark, Scikit-learn, XGBoost) and managed services (Amazon EMR, AWS Glue, Amazon SageMaker)
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
End to End Model Development to Deployment using SageMakerAmazon Web Services
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
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.
Using Amazon SageMaker to build, train, & deploy your ML ModelsAmazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build, Train, and Deploy ML Models Using SageMaker
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.
Level: 200-300
Speaker: Martin Schade - R&D Engineer, AWS Solutions Architecture
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
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.
Build Deep Learning Applications with TensorFlow & SageMakerAmazon Web Services
Build Deep Learning Applications with TensorFlow and SageMaker
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 SageMaker.
Level: 200-300
Speakers:
Martin Schade - R&D Engineer, AWS Solutions Architecture
Steve Sedlmeyer - Sr. Solutions Architect, World Wide Public Sector, AWS
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Optimize your Machine Learning workloads | AWS Summit Tel Aviv 2019AWS Summits
This session focuses on performance and cost optimization on Amazon SageMaker. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
End to End Model Development to Deployment using SageMakerAmazon Web Services
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
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.
Using Amazon SageMaker to build, train, & deploy your ML ModelsAmazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build, Train, and Deploy ML Models Using SageMaker
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.
Level: 200-300
Speaker: Martin Schade - R&D Engineer, AWS Solutions Architecture
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
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.
Build Deep Learning Applications with TensorFlow & SageMakerAmazon Web Services
Build Deep Learning Applications with TensorFlow and SageMaker
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 SageMaker.
Level: 200-300
Speakers:
Martin Schade - R&D Engineer, AWS Solutions Architecture
Steve Sedlmeyer - Sr. Solutions Architect, World Wide Public Sector, AWS
Amazon SageMaker is a fully-managed platform that lets developers and data scientists build and scale machine learning solutions. First, we'll show you how SageMaker Ground Truth helps you label large training datasets. Then, using Jupyter notebooks, we'll show you how to build, train and deploy models using built-in algorithms and frameworks (TensorFlow, Apache MXNet, etc). Finally, we'll show you how to use 3rd-party models from the AWS marketplace.
Optimize your Machine Learning workloads | AWS Summit Tel Aviv 2019AWS Summits
This session focuses on performance and cost optimization on Amazon SageMaker. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
Optimize your Machine Learning workloads | AWS Summit Tel Aviv 2019Amazon Web Services
This session focuses on performance and cost optimization on Amazon SageMaker. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
This session will build on the previous one, focusing on performance and cost optimization. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
This session will build on the previous one, focusing on performance and cost optimization. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
Amazon SageMaker를 통한 대용량 모델 훈련 방법 살펴보기 - 김대근 AWS AI/ML 스페셜리스트 솔루션즈 아키텍트 / 최영준...Amazon Web Services Korea
대량의 딥러닝 모델의 훈련을 위해 Amazon SageMaker에서는 새로운 분산 훈련 기능과 빠른 분산 훈련 환경을 제공하고 있습니다. 특히 기존 TensorFlow/PyTorch의 코드에 몇 줄만 추가하면 쉽게 Amazon SageMaker 환경으로 마이그레이션하여 훈련 속도를 단축할 수 있습니다. 또한 모니터링 기능으로 리소스 사용률을 제공하며, 훈련 속도 최적화에 활용이 가능합니다. 예제 코드와 데모를 통해 Amazon SageMaker 분산 훈련의 이점을 자세히 알려 드립니다.
Build, Train, and Deploy ML Models Quickly and Easily with Amazon SageMaker, ...Amazon Web Services
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. Amazon SageMaker takes away the heavy lifting of machine learning, thus removing the typical barriers associated with machine learning. In this session, we'll dive deep into the technical details of each of the modules of Amazon SageMaker to showcase the capabilities of the platform. We also discuss the practical deployments of Amazon SageMaker through real-world customer examples.
GluonCV는 컴퓨터 비전에 특화된 Apache MXNet의 딥러닝 툴킷입니다. 본 실습에서는 GluonCV가 제공하는 최신 컴퓨터 비전 알고리즘의 기(旣) 훈련(Pre-trained) 모델을 사용하여 이미지 인식, 객체 검출, 영역 구분 등의 다양한 문제를 해결합니다. GluonCV의 설치에서부터 모델 학습과 배포에 이르는 전과정을 따라해 볼 수 있습니다.
This presentation focusses on performance and cost optimization. First, we'll show you how to automatically tune hyper-parameters, and quickly converge to optimal models. Second, you'll learn how to use SageMaker Neo, a new service that optimizes models for the underlying hardware architecture. Third, we'll show you how Elastic Inference lets you attach GPU acceleration to EC2 and SageMaker instances at the fraction of the cost of a full-fledged GPU instance. Finally, we'll share additional cost optimization tips for SageMaker.
Save up to 90% on Big Data and Machine Learning Workloads with Spot Instances...Amazon Web Services
Learn how you can run your big data and machine learning models as many times as you want with Amazon EC2 Spot Instances. In this session, we discuss how to architect and run big data and machine learning workloads by combining common data processing applications with Spot Instances for scalable and cost-effective computing. We also highlight real-world customer examples with machine learning workloads running on Spot Instances.
Amazon SageMaker offers a broad and deep set of modular capabilities to build, train, and deploy machine learning models the way you are most comfortable with. With the majority of the ML production lifecycle spent on inference, Amazon SageMaker supports different approaches to optimizing your deployments for cost and efficiency. Come join us for a discussion on two of these approaches: Amazon Elastic Inference for low-cost GPU-powered acceleration, and Amazon SageMaker Neo for optimizing and compiling models for inference.
Build, Train & Deploy Your ML Application on Amazon SageMakerAmazon Web Services
This session covers a step by step walk through of a typical Machine Learning (ML) process: From asking the right questions, collecting the data, looking at the data, picking the right algorithms, training and evaluating ML models with Amazon SageMaker and bringing them live into production. A series of hands-on demos is included to illustrate these steps so that you can start building your first Machine Learning application right after this session.
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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.
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1/ The new Amazon EC2 P3dn instance
2/ With four-times the networking bandwidth and twice the GPU memory of the largest P3 instance, P3dn is ideal for large scale distributed training. No one else has anything close.
3/ P3dn.24xlarge instances offer 96vCPUs of Intel Skylake processors to reduce preprocessing time of data required for machine learning training.
3/ The enhanced networking of the P3n instance allows GPUs to be used more efficiently in multi-node configurations so training jobs complete faster.
4/ Finally, the extra GPU memory allows developers to easily handle more advanced machine learning models such as holding and processing multiple batches of 4k images for image classification and object detection systems
These performance and accuracy trade offs are felt most acutely at the edge.
1/ IoT applications are usually running on devices, out there in the real world. This means that the accuracy of models can be felt quickly, and immediately. Consumer IoT applications have a high expectation of accuracy - such as Alexa detecting the wake word reliably - the accuracy of that model really matters to the overall experience. In industrial IoT, devices are often responsible for monitoring and maintaining and core manufacturing processes, or safety. The accuracy of a model here is critical.
2/ Applications running on IoT devices at the edge are commonly very sensitive to latency; it’s part of the reason why customers are running the workload there in the first place, because they can’t afford the round trip to the cloud and back. So any increase in that latency can have a meaningful impact on the success of the device itself.
3/ IoT applications are often incredibly resource constrained, in a way which is much more acute than in the cloud. The devices are smaller, and have less memory and processing power, which is a real problem for machine learning models.
4/ In many cases, IoT applications need to run on very diverse hardware platforms, with a dizzying myriad of processor architectures. To get any sort of performance, developers have to optimize
5/ Finally, one of the key benefits of machine learning can get lost; the ability to continually improve the model. IoT applications are great data generators, and once that data is “ground-truthed”, it can be used to build more sophisticated models. However, if the effort to optimize those improved models for the constraints and diverse hardware at the edge is high, then it’s less likely to happen, and developers are leaving money on the table. A real missed opportunity.
6/ We don’t think that customers should have to choose between accuracy and performance. It’s a false choice, with a high cost.
So I’m excited to announce a new feature of SageMaker…