This document provides an overview of machine learning capabilities on AWS. It begins with introductions to machine learning concepts and the benefits of performing machine learning in the cloud. It then describes various AWS machine learning services like Amazon SageMaker for building, training, and deploying models. The rest of the document explores Amazon SageMaker in more detail, demonstrating how to train models using built-in algorithms or custom containers and deploy them for inference.
AWS Machine Learning Week SF: End to End Model Development Using SageMakerAmazon Web Services
AWS Machine Learning Week at the San Francisco Loft: End to End Model Development 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.
Presenter: Kris Skrinak
Optimizing training on Apache MXNet (January 2018)Julien SIMON
Techniques and tips to optimize training on Apache MXNet
Infrastructure performance: storage and I/O, GPU throughput, distributed training, CPU-based training, cost
Model performance: data augmentation, initializers, optimizers, etc.
Level 666: you should be familiar with Deep Learning and MXNet
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
AWS Machine Learning Week SF: End to End Model Development Using SageMakerAmazon Web Services
AWS Machine Learning Week at the San Francisco Loft: End to End Model Development 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.
Presenter: Kris Skrinak
Optimizing training on Apache MXNet (January 2018)Julien SIMON
Techniques and tips to optimize training on Apache MXNet
Infrastructure performance: storage and I/O, GPU throughput, distributed training, CPU-based training, cost
Model performance: data augmentation, initializers, optimizers, etc.
Level 666: you should be familiar with Deep Learning and MXNet
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Amazon SageMaker is a fully managed Machine Learning service which facilitates seamless adoption of #MachineLearning across various industries! Jayesh is walking us through details of SageMaker with demo in this talk!
Il Machine Learning può sembrare più difficile di quanto non lo sia perché il processo di sviluppo, training e deployment dei modelli in produzione è troppo complicato e lento. Amazon SageMaker è un servizio completamente gestito che consente a sviluppatori e data scientist di progettare, implementare e distribuire modelli di Machine Learning in qualsiasi scala. Amazon SageMaker offre una scelta di algoritmi di machine learning altamente performanti e framework preconfigurati come Apache MXNet, TensorFlow, PyTorch e Chainer; inoltre, è possibile utilizzare framework o algoritmi alternativi attraverso container Docker. In questa sessione approfondiremo l’utilizzo di Amazon SageMaker, anche attraverso alcuni pratici esempi.
Train ML Models Using Amazon SageMaker with TensorFlow - SRV336 - Chicago AWS...Amazon Web Services
Amazon SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy machine learning (ML) models in production applications easily and at scale. In this chalk talk, we dive deep into training an ML model based on the TensorFlow framework. We discuss the specifics of training a model through Amazon SageMaker by taking an algorithm and running it on a training cluster in an auto-scaling group. This session showcases the scalability of training that is possible with Amazon SageMaker, which reduces the time and cost of training runs.
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.
Setting up custom machine learning environments on AWS - AIM204 - Chicago AWS...Amazon Web Services
Sometimes, you might need to set up your own deep learning environments for domain-specific performance optimization and integration with custom applications. AWS offers prepackaged, optimized Amazon Machine Images (AMIs) and Docker container images that make it easy to quickly deploy these custom environments by letting you skip the complicated process of building and optimizing your environments from scratch. In this session, you learn about how to use AWS Deep Learning AMIs and AWS Deep Learning Containers to create custom machine learning environments with TensorFlow and Apache MXNet frameworks.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotAmazon Web Services
Amazon SageMaker AutoPilot è una funzionalità di Amazon SageMaker che crea automaticamente il miglior modello di apprendimento automatico per il tuo set di dati. Con SageMaker Autopilot, si fornisce un set di dati tabellare e si seleziona la variabile target da prevedere, che può essere numerica o categorica. SageMaker Autopilot esplorerà automaticamente diverse soluzioni per trovare il modello migliore. È quindi possibile distribuire direttamente il modello in produzione con un solo clic o esplorare le soluzioni consigliate con Amazon SageMaker Studio per migliorare ulteriormente la qualità del modello. In questo webinar approfondiremo questa capacità, con dimostrazioni pratiche su come utilizzare il servizio.
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.
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
Data Summer Conf 2018, “Build, train, and deploy machine learning models at s...Provectus
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 is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Apache MXNet and TensorFlow are pre-installed, and Amazon SageMaker offers a range of built-in, high-performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.
Amazon SageMaker is a fully managed Machine Learning service which facilitates seamless adoption of #MachineLearning across various industries! Jayesh is walking us through details of SageMaker with demo in this talk!
Il Machine Learning può sembrare più difficile di quanto non lo sia perché il processo di sviluppo, training e deployment dei modelli in produzione è troppo complicato e lento. Amazon SageMaker è un servizio completamente gestito che consente a sviluppatori e data scientist di progettare, implementare e distribuire modelli di Machine Learning in qualsiasi scala. Amazon SageMaker offre una scelta di algoritmi di machine learning altamente performanti e framework preconfigurati come Apache MXNet, TensorFlow, PyTorch e Chainer; inoltre, è possibile utilizzare framework o algoritmi alternativi attraverso container Docker. In questa sessione approfondiremo l’utilizzo di Amazon SageMaker, anche attraverso alcuni pratici esempi.
Train ML Models Using Amazon SageMaker with TensorFlow - SRV336 - Chicago AWS...Amazon Web Services
Amazon SageMaker is a fully managed platform that enables developers and data scientists to build, train, and deploy machine learning (ML) models in production applications easily and at scale. In this chalk talk, we dive deep into training an ML model based on the TensorFlow framework. We discuss the specifics of training a model through Amazon SageMaker by taking an algorithm and running it on a training cluster in an auto-scaling group. This session showcases the scalability of training that is possible with Amazon SageMaker, which reduces the time and cost of training runs.
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.
Setting up custom machine learning environments on AWS - AIM204 - Chicago AWS...Amazon Web Services
Sometimes, you might need to set up your own deep learning environments for domain-specific performance optimization and integration with custom applications. AWS offers prepackaged, optimized Amazon Machine Images (AMIs) and Docker container images that make it easy to quickly deploy these custom environments by letting you skip the complicated process of building and optimizing your environments from scratch. In this session, you learn about how to use AWS Deep Learning AMIs and AWS Deep Learning Containers to create custom machine learning environments with TensorFlow and Apache MXNet frameworks.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Costruisci modelli di Machine Learning con Amazon SageMaker AutopilotAmazon Web Services
Amazon SageMaker AutoPilot è una funzionalità di Amazon SageMaker che crea automaticamente il miglior modello di apprendimento automatico per il tuo set di dati. Con SageMaker Autopilot, si fornisce un set di dati tabellare e si seleziona la variabile target da prevedere, che può essere numerica o categorica. SageMaker Autopilot esplorerà automaticamente diverse soluzioni per trovare il modello migliore. È quindi possibile distribuire direttamente il modello in produzione con un solo clic o esplorare le soluzioni consigliate con Amazon SageMaker Studio per migliorare ulteriormente la qualità del modello. In questo webinar approfondiremo questa capacità, con dimostrazioni pratiche su come utilizzare il servizio.
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.
Strata CA 2019: From Jupyter to Production Manu MukerjiManu Mukerji
Proposed title
From Jupyter to production
Description of the presentation
Jupyter is very popular for data science, data exploration and visualization, this talk is about how to use it in for AI/ML in a production environment.
General Flow of talk:
How things can go wrong with QA, Production releases when using a notebook
Common Jupyter ML examples
Standard ML flow
Training in production
Model creation
Testing in production
Papermill and Jupyter
Production workflows with Sagemaker
Speaker
Manu Mukerji is senior director of data, machine learning, and analytics at 8×8. Manu’s background lies in cloud computing and big data, working on systems handling billions of transactions per day in real time. He enjoys building and architecting scalable, highly available data solutions and has extensive experience working in online advertising and social media.
Data Summer Conf 2018, “Build, train, and deploy machine learning models at s...Provectus
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 is a fully-managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. Apache MXNet and TensorFlow are pre-installed, and Amazon SageMaker offers a range of built-in, high-performance machine learning algorithms. If you want to train with an alternative framework or algorithm, you can bring your own in a Docker container.
Similar to Demystifying Machine Learning with AWS (ACD Mumbai) (20)
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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.
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
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.
8. Machine Learning in Cloud
● The cloud’s pay-per-use model
● Easy for enterprises to experiment with ML capabilities and scale
up as projects go into production and demand increases.
● The cloud makes intelligent capabilities accessible without
requiring advanced skills in artificial intelligence or data science.
● AWS, Microsoft Azure, and Google Cloud Platform offer many
machine learning options that don’t require deep knowledge of AI,
machine learning theory, or a team of data scientists.
9. AI & ML capabilities of AWS
ML Frameworks + Infrastructure ML Services AI Services
Frameworks
Interfaces
+
Infrastructure
Amazon Sagemaker
Build
+
Train
+
Deploy
Personalize Forecast Rekognition
Comprehend Textract Polly
Lex Translate Transcribe
10. Scenario 1:
1. You’re a developer with very less or
no knowledge of ML Looking to
integrate some sort of AI capabilities
in your application.
2. You have very general use case.
11. AI & ML capabilities of AWS
ML Frameworks + Infrastructure ML Services AI Services
Frameworks
Interfaces
+
Infrastructure
Amazon Sagemaker
Build
+
Train
+
Deploy
Personalize Forecast Rekognition
Comprehend Textract Polly
Lex Translate Transcribe
12. Scenario 2:
You’re a developer or data scientist and
you want the ability to build, train, and
deploy machine learning models quickly
without a hassle of choosing
frameworks, interfaces and configuring
infrastructure.
13. AI & ML capabilities of AWS
ML Frameworks + Infrastructure ML Services AI Services
Frameworks
Interfaces
+
Infrastructure
Amazon Sagemaker
Build
+
Train
+
Deploy
Personalize Forecast Rekognition
Comprehend Textract Polly
Lex Translate Transcribe
15. What is Sagemaker & What it provides?
● Fully managed machine learning service.
● Quickly and easily build and train machine learning models, and then
directly deploy them into a production-ready hosted environment.
● Integrated Jupyter authoring notebook instance
● Common machine learning algorithms that are optimized to run efficiently
against extremely large data in a distributed environment.
● Bring-your-own-algorithms and frameworks
● Flexible distributed training options that adjust to your specific workflows.
19. Build Train Deploy
Collect & prepare training data
Data labeling & pre-built
notebooks for common
problems
Set up & manage environments
for training
One-click training using Amazon
EC2 On-Demand or Spot
instances
Deploy model in production
One-click deployment
Choose & optimize your ML
algorithm
Built-in, high-performance
algorithms and hundreds of
ready to use algorithms in AWS
Marketplace
Train & tune model
Train once, run anywhere &
model optimization
Scale & manage the production
environment
Fully managed with auto-scaling
for 75% less
20.
21. Amazon SageMaker: Open Source Containers
● Customize them
● Run them locally for development and testing
● Run them on SageMaker for training and prediction at scale
https://github.com/aws/sagemaker-tensorflow-containers
https://github.com/aws/sagemaker-mxnet-containers
22. Amazon SageMaker: Bring Your Own Container
● Prepare the training code in Docker container
● Upload container image to Amazon Elastic Container Registry (ECR)
● Upload training dataset to Amazon S3/FSx/EFS
● Invoke Create Training Job API to execute a SageMaker training job
SageMaker training job pulls the container image from Amazon ECR, reads
the training data from the data source, configures the training job with
hyperparameter inputs, trains a model, and saves the model to model_dir so
that it can be deployed for inference later.
https://github.com/aws/sagemaker-container-support
23. Distributed Training At Scale on Amazon SageMaker
● Training on Amazon SageMaker can automatically distribute processing
across a number of nodes - including P3 instances
● You can choose from two data distribution types for training ML models
○ Fully Replicated - This will pass every file in the input to every
machine
○ Sharded S3 Key - This will separate and distribute the files in the
input across the training nodes
Overall, sharding can run faster but it depends on the algorithm
24. Amazon SageMaker: Local Mode Training
Enabling experimentation speed
● Train with local notebooks
● Train on notebook instances
● Iterate faster a small sample of the dataset locally no waiting for a new
● training cluster to be built each time
● Emulate CPU (single and multi-instance) and GPU (single instance) in local
mode
● Go distributed with a single line of code
25. Automatic Model Tuning on Amazon SageMaker
Hyperparameter Optimizer
● Amazon SageMaker automatic model tuning predicts hyperparameter
values, which might be most effective at improving fit.
● Automatic model tuning can be used with the Amazon SageMaker
○ Built-in algorithms,
○ Pre-built deep learning frameworks, and
○ Bring-your-own-algorithm containers
http://github.com/awslabs/amazon-sagemakerexamples/tree/master/hyperparameter tuning
26. Amazon SageMaker: Accelerating ML Training
Faster start times and training job execution time
● Two modes: File Mode and Pipe Mode
○ input mode parameter in sagemaker.estimator.estimator
● File Mode: S3 data source or file system data source
○ When using S3 as data source, training data set is downloaded to EBS volumes
○ Use file system data source (Amazon EFS or Amazon FSx for Lustre) for faster
training
○ startup and execution time. Different data formats supported: CSV, protobuf, JSON,
libsvm (check algo docs!)
● Pipe Mode streams the data set to training instances
○ This allows you to process large data sets and training starts faster
○ Dataset must be in recordio-encoded protobuf or csv format
27. Amazon SageMaker: Fully-Managed Spot Training
Reduce training costs at scale
● Managed Spot training on SageMaker to reduce training costs by up to 90%
● Managed Spot Training is available in all training configurations:
○ All instance types supported by Amazon SageMaker
○ All models: built-in algorithms, built-in frameworks, and custom models
○ All configurations: single instance training, distributed training, and
automatic model tuning.
● Setting it up is extremely simple
○ If you're using the console, just switch the feature on.
○ If you're working with the Amazon SageMaker SDK just set
train_use_spot_instances to true in the Estimator constructor.
28. Amazon SageMaker: Secure Machine Learning
● No retention of customers data
● SageMaker provides encryption in transit
● Encryption at rest everywhere
● Compute isolation - instances allocated for computation are never shared with
others
● Network isolation: all compute instances run inside private service managed
VPCs
● Secure, fully managed infrastructure: Amazon Sagemaker take care of patching
and keeping instances up-to-date
● Notebook security - Jupyter notebooks can be operated without internet access
and bound to secure customer VPCs
29. How To Train a Model With Amazon SageMaker
To train a model in Amazon SageMaker, you create a training job. The training job
includes the following information:
● The URL of the Amazon Simple Storage Service (Amazon S3) bucket or the file
● system id of the file system where you've stored the training data.
● The compute resources that you want Amazon SageMaker to use for model
training. Compute resources are ML compute instances that are managed by
Amazon SageMaker.
● The URL of the S3 bucket where you want to store the output of the job.
● The Amazon Elastic Container Registry path where the training code is stored.
30. Amazon SageMaker Training: Getting Started
To train a model in Amazon SageMaker, you will need the following:
● A dataset. Here we will use the MNIST (Modified National Institute of Standards and
Technology database) dataset. This dataset provides a training set of 50,000 example
images of handwritten single-digit numbers, a validation set of 10,000 images, and a test
dataset of 10,000 images.
● An algorithm. Here we will use the Linear Learner algorithm provided by Amazon
● An Amazon Simple Storage Service (Amazon S3) bucket to store the training data and the
model artifacts
● An Amazon SageMaker notebook instance to prepare and process data and to train and
deploy a machine learning model.
● A Jupyter notebook to use with the notebook instance
● For model training, deployment, and validation, I will use the high-level Amazon
SageMaker Python SDK
31. Amazon SageMaker Training: Getting Started
● Create the S3 bucket
● Create an Amazon SageMaker Notebook instance by going here:
https://console.aws.amazon.com/sagemaker/
● Choose Notebook instances, then choose Create notebook instance.
● On the Create notebook instance page, provide the Notebook instance name,
choose ml.t2.medium for instance type (least expensive instance) For IAM role,
choose Create a new role, then choose Create role.
● Choose Create notebook instance.
In a few minutes, Amazon SageMaker launches an ML compute instance
and attaches an ML storage volume to it. The notebook instance has a
preconfigured Jupyter notebook server and a set of Anaconda libraries.
32. Linear Learner with MNIST dataset example
● Provide the S3 bucket and prefix that you want to use for training and model
artifacts. This should be within the same region as the Notebook instance,
training, and hosting
● The IAM role arn used to give training and hosting access to your data
● Download the MNIST dataset
● Amazon SageMaker implementation of Linear Learner takes recordio wrapped
protobuf, where as the data we have is a pickle-ized numpy array on disk.
● This data conversion will be handled by the Amazon SageMaker Python SDK,
imported as sagemaker
33. Train the model
Create and Run a Training Job with Amazon SageMaker Python SDK
● To train a model in Amazon Sagemaker, you can use
○ Amazon SageMaker Python SDK or
○ AWS SDK for Python (Boto 3) or
○ AWS console
● For this exercise, I will use the notebook instance and the Python SDK
● The Amazon SageMaker Python SDK includes the
sagemaker.estimator.Estimator estimator, which can be used with any
algorithm.
● To run a model training job import the Amazon SageMaker Python SDK and get
the Linear Learner container
36. Scenario 3:
You’re a Machine Learning Expert and
want to develop your own pipeline on
high class infrastructure provided by
AWS.
37. AI & ML capabilities of AWS
ML Frameworks + Infrastructure ML Services AI Services
Frameworks
Interfaces
+
Infrastructure
Amazon Sagemaker
Build
+
Train
+
Deploy
Personalize Forecast Rekognition
Comprehend Textract Polly
Lex Translate Transcribe
38. Machine Learning end to end pipeline using AWS
Build
1. Pre-build algorithms
& notebooks
2. Data Labeling:
Ground Truth
3. AWS marketplace for
ML
Deploy
1. one-click deployment
and hosting
Train
1. One-click model
training and tuning
2. Sagemaker Neo
3. Sagemaker RL
03
01 02