The document discusses Amazon Web Services' (AWS) machine learning and artificial intelligence services. It provides an overview of AWS' application services like Amazon Rekognition, Amazon Polly, and Amazon Translate. It also discusses AWS' platform services like Amazon SageMaker, Amazon EMR, and the AWS Deep Learning AMI. The document emphasizes that more AI/ML is built on AWS than anywhere else and highlights several customer examples using AWS machine learning services.
AWS Machine Learning Language Services (May 2018)Julien SIMON
Talk at AWS Summit London, 10/05/2018
Amazon Transcribe
Automatic conversion of speech into accurate, grammatically correct text
Amazon Translate
Natural and fluent language translation
Amazon Polly
Turn text into lifelike speech using deep learning
Amazon Comprehend
Discover insights and relationships in text
Amazon Lex
Conversational interfaces for text-based and voice-based applications
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
Workshop: Using Amazon ML Services for Video Transcription and Translation Wo...Amazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
In this hands-on workshop, participants will use AWS ML services to generate transcripts from audio files, use NLP to analyze those transcripts, and produce subtitles in multiple languages. Using ML, you can keep pace with the proliferation of audio/video content across businesses. Asset managers can unlock hidden value in existing media libraries by finding precise moments when particular keywords or phrases are spoken; video publishers can benefit from subtitle and localized files for reaching global audiences; and IT organizations can utilize transcription data to improve organizational governance.
Workshop: Build an Image-Based Automatic Alert System with Amazon RekognitionAmazon Web Services
by Kashif Imran, Solutions Architect, AWS
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
AWS Machine Learning Language Services (May 2018)Julien SIMON
Talk at AWS Summit London, 10/05/2018
Amazon Transcribe
Automatic conversion of speech into accurate, grammatically correct text
Amazon Translate
Natural and fluent language translation
Amazon Polly
Turn text into lifelike speech using deep learning
Amazon Comprehend
Discover insights and relationships in text
Amazon Lex
Conversational interfaces for text-based and voice-based applications
Demystifying Machine Learning On AWS - AWS Summit Sydney 2018Amazon Web Services
Demystifying Machine Learning on AWS
Machine Learning is having a major impact in our society, but how can we simplify the build, train, and deploy process for all developers and data scientists? Understand how cloud-based machine learning frameworks can help turn your data into intelligence. We will introduce the general machine learning process utilising the AWS Deep Learning AMIs and hear from carsales.com.au about how they built the Cyclops, a Super Human Image Recognition Software on AWS. We will then discuss the new capabilities delivered by Amazon SageMaker and how this product will further reduce the undifferentiated heavy lifting; freeing you up to focus on your business and allow your developers to quickly and easily expand into the world of Machine Learning.
Jenny Davies, Solutions Architect, Amazon Web Services and Agustinus Nalwan, AI and Machine Learning Technical Development Manager, Carsales.com.au
Workshop: Using Amazon ML Services for Video Transcription and Translation Wo...Amazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
In this hands-on workshop, participants will use AWS ML services to generate transcripts from audio files, use NLP to analyze those transcripts, and produce subtitles in multiple languages. Using ML, you can keep pace with the proliferation of audio/video content across businesses. Asset managers can unlock hidden value in existing media libraries by finding precise moments when particular keywords or phrases are spoken; video publishers can benefit from subtitle and localized files for reaching global audiences; and IT organizations can utilize transcription data to improve organizational governance.
Workshop: Build an Image-Based Automatic Alert System with Amazon RekognitionAmazon Web Services
by Kashif Imran, Solutions Architect, AWS
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
AI & ML at Amazon: AWS Developer Workshop - Web Summit 2018Amazon Web Services
AI & Machine Learning at Amazon: AWS Developer Workshop - Web Summit 2018
Amazon has been applying machine learning to create artifical intelligence features within its products and services for over 20 years. Join this session and learn about the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and how you can use easily accessible services from AWS to enable you to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
re:Invent re:Cap - An overview of Artificial Intelligence and Machine Learnin...Adrian Hornsby
In this session, you will learn about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
Introduction to AI on AWS - AL/ML Hebrew WebinarBoaz Ziniman
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Build Text Analytics Solutions with Amazon Comprehend and Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Serve Your Customers with AI from the Cloud: AWS Developer Workshop - Web Sum...Amazon Web Services
Serve Your Customers with AI from Cloud: AWS Developer Workshop - Web Summit 2018
Conversational interfaces are the latest hot trend in human computer interaction. In this session we will Deep Dive into Amazon Lex, an AWS service that enables developers to embed conversational interfaces within their own applications or deploy intelligent chatbots onto a variety of chat platforms and social networks. You'll also be able to interact with a production chatbot live during this session, so be sure to bring a device with Facebook Messenger along!
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
Speaker: Herbert-John Kelly, AWS
Customer Speaker: Data Prophet
Level: 200
Join us to hear about our strategy for driving machine learning (ML) innovation for our customers and learn what's new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
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.
Sviluppare applicazioni voice-first con AWS e Amazon AlexaAmazon Web Services
Come possiamo sviluppare applicazioni che siano allo stesso tempo scalabili, manutenibili, cost-effective, intelligenti e voice-first? La suite di servizi AWS basati su Machine Learning e Deep Learning offre ad ogni sviluppatore la possibilità di integrare funzionalità avanzate di riconoscimento vocale, comprensione del linguaggio naturale, rendering audio e traduzione automatica.
In questo webinar, Alex ed Arianna discuteranno le tecniche e le best practice per implementare interfacce vocali tramite i servizi AWS. Arianna, technical evangelist per Amazon Alexa, introdurrà Alexa e mostrerà come sviluppare esperienze vocali per quest’ultima.
In this session we show how engineers, analysts and scientists can develop, train, tune and deploy machine learning models via Amazon SageMaker, a docker-based, framework-agnostic orchestrator dedicated to machine learning with strong primitives for automation, monitoring and deployment. We will cover the following topics:
- Amazon SageMaker architecture and key functionality
- Scaling and deploying open source frameworks on Amazon SageMaker (sklearn, MXNet, Keras, TensorFlow, pyTorch, R)
- Amazon SageMaker built-in Algorithm library: 18 state-of-the art algorithm covering broad use-cases, from computer vision to recommender systems
- ML model deployment
Amazon SageMaker Ground Truth: Build High-Quality and Accurate ML Training Da...Amazon Web Services
Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity using a machine learning technique called active learning. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, Ground Truth improves the ability to automatically label data resulting in high-quality training datasets.
Level: 300
Speaker: Kris Skrinak - Partner Solutions Architect, ML Global Lead, AWS
Workshop: Build a Virtual Assistant with Amazon Polly and Amazon Lex - "Pollexy"Amazon Web Services
by Niranjan Hira, Solutions Architect, AWS
Technology advances have enabled people with disabilities to communicate more meaningfully and participate more fully in their daily lives. In this workshop, we will show how voice technologies can empower this population by building a verbal assistant using Pollexy (Amazon Polly + Amazon Lex) with a Raspberry Pi. This verbal assistant lets caretakers schedule audio prompts and messages, both on a recurring schedule and on-demand.
Workshop: Build Deep Learning Applications with TensorFlow and SageMakerAmazon Web Services
by Ahmad Khan, Sr. Solutions Architect, AWS
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.
Translating Web Content Easily with Language Services from AWS (AIM348) - AWS...Amazon Web Services
Providing multilingual content represents a great opportunity for site owners. Although English is the dominant language of the web, native English speakers comprise only 26% of the total online audience. In this chalk talk, we discuss how you can make your web content more accessible with text-to-speech and machine translation. By offering written and audio versions of your content in multiple languages, you can meet the needs of a larger international audience.
Simon Poile, GM of AWS Digital User Engagement, presented at Digital Summit Seattle February 26, 2019. Over the last 10 years, the only thing that hasn’t changed in customer engagement is the value of a trusted relationship. At Amazon, we believe strongly in customer-driven innovation and are constantly striving to provide the best experience for our customers. In our experience, customers are always going to adopt new devices and channels, want personalized outreach, and demand timely and relevant communication on matters they care about the most. Marketers concerned about engaging their own customers must challenge themselves in the same way, evolving and innovating while never losing focus on the most important thing, your relationship with your customer.
Session attendee learned how to leverage innovative technology to:
–Learn more about their customers through a single view derived from disparate data sources
—Create highly personalized engagement experiences
—Better understand when and on which channel to engage their users
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.
Add Intelligence to Applications with AWS ML: Machine Learning Workshops SFAmazon Web Services
Machine Learning Workshops at the San Francisco Loft
Add Intelligence to Applications with AWS ML Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Level: 200
Speaker: Liam Morrison - Principal Solutions Architect, AWS
Workshop Build an Image-Based Automatic Alert System with Amazon Rekognition:...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build an Image-Based Automatic Alert System with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Level: 200-300
Speakers:
Wayne Davis - Solutions Architect, AWS
Tristan Li - Solutions Architect, AWS
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
AI & ML at Amazon: AWS Developer Workshop - Web Summit 2018Amazon Web Services
AI & Machine Learning at Amazon: AWS Developer Workshop - Web Summit 2018
Amazon has been applying machine learning to create artifical intelligence features within its products and services for over 20 years. Join this session and learn about the application of ML and AI within Amazon, from retail product recommendations to the latest in natural language understanding, and how you can use easily accessible services from AWS to enable you to include AI features within your applications or build your own custom ML models for your own specific AI use cases.
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
re:Invent re:Cap - An overview of Artificial Intelligence and Machine Learnin...Adrian Hornsby
In this session, you will learn about our strategy for driving machine learning innovation for our customers and learn what’s new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe, and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
Introduction to AI on AWS - AL/ML Hebrew WebinarBoaz Ziniman
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Build Text Analytics Solutions with Amazon Comprehend and Amazon TranslateAmazon Web Services
by Pratap Ramamurthy, Partner Solutions Architect, AWS
Natural language holds a wealth of information like user sentiment and conversational intent. In this session, we'll demonstrate the capabilities of Amazon Comprehend, a natural language processing (NLP) service that uses machine learning to find insights and relationships in text. We'll show you how to build a VOC (Voice of the Customer) application and integrate it with other AWS services including AWS Lambda, Amazon S3, Amazon Athena, Amazon QuickSight, and Amazon Translate. We’ll also show you additional methods for NLP available through Amazon Sagemaker.
Serve Your Customers with AI from the Cloud: AWS Developer Workshop - Web Sum...Amazon Web Services
Serve Your Customers with AI from Cloud: AWS Developer Workshop - Web Summit 2018
Conversational interfaces are the latest hot trend in human computer interaction. In this session we will Deep Dive into Amazon Lex, an AWS service that enables developers to embed conversational interfaces within their own applications or deploy intelligent chatbots onto a variety of chat platforms and social networks. You'll also be able to interact with a production chatbot live during this session, so be sure to bring a device with Facebook Messenger along!
Speaker: Ian Massingham - Director, Technical Evangelist, AWS
Speaker: Herbert-John Kelly, AWS
Customer Speaker: Data Prophet
Level: 200
Join us to hear about our strategy for driving machine learning (ML) innovation for our customers and learn what's new from AWS in the machine learning space. We will discuss and demonstrate the latest new services for ML on AWS: Amazon SageMaker, AWS DeepLens, Amazon Rekogntion Video, Amazon Translate, Amazon Transcribe and Amazon Comprehend. Attend this session to understand how to make the most of machine learning in the cloud.
Driving AI Innovation with Machine Learning powered by AWS. AI is opening up new insights and efficiencies in enterprises of every industry. Learn how enterprises are using AWS’ machine learning capabilities combined with its deep storage, compute, analytics, and security services to deliver intelligent applications today. Strategies to develop ML expertise within your org will also be discussed.
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.
Sviluppare applicazioni voice-first con AWS e Amazon AlexaAmazon Web Services
Come possiamo sviluppare applicazioni che siano allo stesso tempo scalabili, manutenibili, cost-effective, intelligenti e voice-first? La suite di servizi AWS basati su Machine Learning e Deep Learning offre ad ogni sviluppatore la possibilità di integrare funzionalità avanzate di riconoscimento vocale, comprensione del linguaggio naturale, rendering audio e traduzione automatica.
In questo webinar, Alex ed Arianna discuteranno le tecniche e le best practice per implementare interfacce vocali tramite i servizi AWS. Arianna, technical evangelist per Amazon Alexa, introdurrà Alexa e mostrerà come sviluppare esperienze vocali per quest’ultima.
In this session we show how engineers, analysts and scientists can develop, train, tune and deploy machine learning models via Amazon SageMaker, a docker-based, framework-agnostic orchestrator dedicated to machine learning with strong primitives for automation, monitoring and deployment. We will cover the following topics:
- Amazon SageMaker architecture and key functionality
- Scaling and deploying open source frameworks on Amazon SageMaker (sklearn, MXNet, Keras, TensorFlow, pyTorch, R)
- Amazon SageMaker built-in Algorithm library: 18 state-of-the art algorithm covering broad use-cases, from computer vision to recommender systems
- ML model deployment
Amazon SageMaker Ground Truth: Build High-Quality and Accurate ML Training Da...Amazon Web Services
Successful machine learning models are built on high-quality training datasets. Labeling raw data to get accurate training datasets involves a lot of time and effort because sophisticated models can require thousands of labeled examples to learn from, before they can produce good results. Typically, the task of labeling is distributed across a large number of humans, adding significant overhead and cost. Join us as we introduce Amazon SageMaker Ground Truth, a new service that provides an effective solution to reduce this cost and complexity using a machine learning technique called active learning. Active learning reduces the time and manual effort required to do data labeling, by continuously training machine learning algorithms based on labels from humans. By iterating through ambiguous data points, Ground Truth improves the ability to automatically label data resulting in high-quality training datasets.
Level: 300
Speaker: Kris Skrinak - Partner Solutions Architect, ML Global Lead, AWS
Workshop: Build a Virtual Assistant with Amazon Polly and Amazon Lex - "Pollexy"Amazon Web Services
by Niranjan Hira, Solutions Architect, AWS
Technology advances have enabled people with disabilities to communicate more meaningfully and participate more fully in their daily lives. In this workshop, we will show how voice technologies can empower this population by building a verbal assistant using Pollexy (Amazon Polly + Amazon Lex) with a Raspberry Pi. This verbal assistant lets caretakers schedule audio prompts and messages, both on a recurring schedule and on-demand.
Workshop: Build Deep Learning Applications with TensorFlow and SageMakerAmazon Web Services
by Ahmad Khan, Sr. Solutions Architect, AWS
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.
Translating Web Content Easily with Language Services from AWS (AIM348) - AWS...Amazon Web Services
Providing multilingual content represents a great opportunity for site owners. Although English is the dominant language of the web, native English speakers comprise only 26% of the total online audience. In this chalk talk, we discuss how you can make your web content more accessible with text-to-speech and machine translation. By offering written and audio versions of your content in multiple languages, you can meet the needs of a larger international audience.
Simon Poile, GM of AWS Digital User Engagement, presented at Digital Summit Seattle February 26, 2019. Over the last 10 years, the only thing that hasn’t changed in customer engagement is the value of a trusted relationship. At Amazon, we believe strongly in customer-driven innovation and are constantly striving to provide the best experience for our customers. In our experience, customers are always going to adopt new devices and channels, want personalized outreach, and demand timely and relevant communication on matters they care about the most. Marketers concerned about engaging their own customers must challenge themselves in the same way, evolving and innovating while never losing focus on the most important thing, your relationship with your customer.
Session attendee learned how to leverage innovative technology to:
–Learn more about their customers through a single view derived from disparate data sources
—Create highly personalized engagement experiences
—Better understand when and on which channel to engage their users
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.
Add Intelligence to Applications with AWS ML: Machine Learning Workshops SFAmazon Web Services
Machine Learning Workshops at the San Francisco Loft
Add Intelligence to Applications with AWS ML Services
Organizations are increasingly turning to machine learning to build intelligent applications and get more insights out of their data in real-time. In this session, you’ll learn about AWS Machine Learning APIs for computer vision and language, and how to get started with these pre-trained services: Amazon Rekognition, Amazon Comprehend, Amazon Transcribe, Amazon Translate, Amazon Polly, and Amazon Lex. We’ll also show how these services connect to AWS’s comprehensive data platform and services to drive the success of your machine learning projects.
Level: 200
Speaker: Liam Morrison - Principal Solutions Architect, AWS
Workshop Build an Image-Based Automatic Alert System with Amazon Rekognition:...Amazon Web Services
Machine Learning Workshops at the San Francisco Loft
Build an Image-Based Automatic Alert System with Amazon Rekognition
This hands-on workshop will walk through how to build a solution that listens and captures images from Twitter, and then compares those images against a reference image to automatically notify you about a new post featuring your favorite celebrity. Additionally, we will integrate sentiment analysis into this image-based automatic alert system in order to gauge whether the determined celebrities are happy, sad, etc. in the posted image.
Level: 200-300
Speakers:
Wayne Davis - Solutions Architect, AWS
Tristan Li - Solutions Architect, AWS
Artificial Intelligence (AI) services on the AWS cloud bring deep learning (DL) technologies like natural language understanding (NLU), automatic speech recognition (ASR), image recognition and computer vision (CV), text-to-speech (TTS), and machine learning (ML) within reach of every developer. In this session, you will be introduced to several new AI services: Amazon Lex, to build sophisticated text and voice chatbots; Amazon Rekognition, for deep learning-based image recognition; and Amazon Polly, for turning text into lifelike speech. The opportunities to apply one or more of these DL services are nearly boundless and this session will provide a number of examples and use cases to help you get started.
Getting Started with AWS AI Managed Services and SagemakerAmazon Web Services
Chan Sze-Lok, Startup Business Development Manager, AWS
Amazon.com uses Artificial Intelligence to improve customer experience, grow its business and optimize its operations. AWS AI managed services make this powerful AI technology available to every business in the form of simple-to-use services. Attendees will learn how their business can start using powerful AI such as facial recognition, chatbots and sentiment analysis. AWS AI managed services allow customers to get started without any data science expertise, benefiting from technology first tested in the scale and mission critical environment of Amazon.com.
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. You'll learn about 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.
Speaker: François-Xavier Gsell, Solutions Architect, Amazon Web Services
Breaking Language Barriers with AI - Web Summit 2018Boaz Ziniman
AI and machine learning allow developers to introduce new language capabilities in their apps and use Natural Language Processing and Natural Language Understanding to break language barriers, add new functionality and expand their target audience. This session will focus on several AWS AI services for developers, that allow you to add such functionality to your code with minimal effort. We'll build an automatic translator, interact with text to speech and try to extract sentiments from live text coming from different feeds.
Breaking Language Barriers with AI: AWS Developer Workshop - Web Summit 2018Amazon Web Services
Breaking Language Barriers with AI: AWS Developer Workshop - Web Summit 2018
AI and Machine learning allow developers to introduce new language capabilities in their apps and use Natural Language Processing and Natural Language Understanding to break language barriers, add new functionality and expand their target audience. This session will focus on several AWS AI services for developers, that allow you to add such functionality to your code with minimal effort. We will build an automatic translator, interact with text to speech and try to extract sentiments from live text coming from different feeds.
Speaker: Boaz Ziniman - Technical Evangelist, AWS
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Building the Organisation of the Future: Leveraging Artificial Intelligence a...Amazon Web Services
Artificial intelligence and machine learning are no longer the stuff of science fiction. Organisations of all sizes are using these tools to create innovative artificial intelligences applications – namely, Amazon.com's own retail experience. Join us for an inside look at how Amazon thinks about this technology, and hear from Skinvision on how they’re using machine learning for early skin-cancer detection. Through these stories, gain insight into a range of new machine learning services on AWS for use in your own business.
Breght Boschker, CTO, Skinvision
Miguel Rojo Rossi, Solutions Architect Lead, AWS
Introduction to AWS ML Application Services - BDA202 - Toronto AWS SummitAmazon Web Services
Amazon brings computer vision, natural language processing, speech recognition, text-to-speech, and machine translation within the reach of every developer. API-driven application services enable developers to easily plug in pre-built AI functionality into their applications and automate manual workflows. Join us to learn more about new language capabilities and text-in-image extraction. We also share how others are building the next generation of intelligent apps that can see, hear, speak, understand, and interact with the world around us.
For the Enterprise, Artificial Intelligence (AI) materializes into solutions that improve customers' experiences by optimizing, automating, and personalizing high-volume tasks while lowering cost and time to market, therefore accelerating innovation. Augmented Reality (AR) and Virtual Reality (VR) based solutions offer a promising future in the enterprise allowing engagement with data in innovative ways. In this session, we take a look at how AWS is democratizing AI, AR and VR to enable innovation in the enterprise by building applications quickly and easily without requiring specialized programming.
The Future of AI - AllCloud Best of reInventBoaz Ziniman
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Leadership Session: Digital Advertising - Customer Learning & the Road Ahead ...Amazon Web Services
In this session, learn how experienced leaders in digital advertising respond to the rapid evolution and sophistication of the advertising market driven by innovation and groundbreaking technology. Our customers share real-world applications they've leveraged in the cloud and how they see the media landscape changing as adoption of AI in the space becomes more widespread. Learn about existing and upcoming advancements and how they affect digital transformation in the years to come. Come away with ideas on how you can apply these learnings to your technology stack.
Artificial Intelligence (AI) services on the AWS cloud bring the power of deep learning within reach of every developer, allowing us to develop new tools and enrich our systems with new capabilities. In this session, we will look into the opportunities to apply one or more of these services provide a number of examples and use cases to help you get started.
Similar to AI & ML on AWS: State of the Union (20)
An introduction to computer vision with Hugging FaceJulien SIMON
In this code-level talk, Julien will show you how to quickly build and deploy computer vision applications based on Transformer models. Along the way, you'll learn about the portfolio of open source and commercial Hugging Face solutions, and how they can help you deliver high-quality solutions faster than ever before.
Starting your AI/ML project right (May 2020)Julien SIMON
In this talk, we’ll see how you can put your AI/ML project on the right track from the get-go. Applying common sense and proven best practices, we’ll discuss skills, tools, methods, and more. We’ll also look at several real-life projects built by AWS customers in different industries and startups.
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)
Optimize your Machine Learning Workloads on AWS (July 2019)Julien SIMON
Talk at Floor 28, Tel Aviv.
Infrastructure, tips to speed up training, hyperparameter optimization, model compilation, Amazon SageMaker Neo, cost optimization, Amazon Elastic Inference
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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!
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.