The document discusses Amazon SageMaker, a fully managed machine learning platform. It describes how SageMaker allows users to build, train, and deploy machine learning models at scale. Key features include pre-built algorithms and notebooks, tools for data labeling and preparation, one-click training and tuning of models, and deployment of trained models into production. The document also provides examples of using SageMaker for tasks like image classification and text analysis.
Machine Learning with Amazon SageMaker - Algorithms and Frameworks - BDA304 -...Amazon Web Services
Algorithms and frameworks form a fundamental part of machine learning (ML). These critical components enable developers and data scientists to easily and quickly build ML models with well-defined interfaces for a range of use cases. The most commonly used algorithms and frameworks, built-in with Amazon SageMaker, make ML easier to address these use cases. In this session, we discuss the built-in algorithms and frameworks and how you can leverage them for your ML models. We also discuss the flexibility of bringing your own algorithm into Amazon SageMaker depending on your needs.
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.
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Learn how to get started with Amazon SageMaker—our fully-managed service that spans the entire machine learning (ML) workflow—so you can build, train, and deploy models quickly. Use Amazon SageMaker to label and prepare your data, choose an algorithm, train, tune, and optimize it for deployment, make predictions, and take action. Get your models to production faster with Amazon SageMaker SDKs, builder tools, and APIs tailored to your programming language or platform. Also, discover how Amazon SageMaker Ground Truth can aid in the adoption of ML technology for your organization.
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
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.
Level: 300-400
Speaker: Zlatan Dzinic - Partner Solutions Architect, AWS
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
Automatic Labelling and Model Tuning with Amazon SageMaker - AWS Summit SydneyAmazon Web Services
Developing machine learning models requires a lot of effort which often needs to be repeated over time as data distributions change. In this session you will learn about some of the latest concepts in Automatic Machine Learning including how to apply them to speed up development and achieve robust models over time. You will learn how to run a custom labelling job using Amazon SageMaker Ground Truth to build a larger data set to fine-tune your model. You will also learn how to tune your model’s hyperparameters using Amazon SageMaker’s Automatic Model Tuning capabilities and understand the theory of how bayesian optimisation is automatically applied for more accurate results and faster tuning.
This workshop requires a laptop and administrative access to your own AWS account.
Machine Learning: From Inception to Inference - AWS Summit SydneyAmazon Web Services
A streamlined end-to-end machine learning process enables operational efficiency and improved productivity for the data scientists, developers, and data engineers building machine learning solutions. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. In this workshop, you will be able to ingest, explore, and process the data and then build, train, and deploy a machine learning model. Further, the workshop will also guide you to connect Amazon SageMaker endpoint server to the public Internet using AWS Lambda and Amazon API Gateway in order to effectively host the model to run inferences/predictions over a Single Page Application (SPA).
This workshop requires a laptop and administrative access to your own AWS account.
Using Machine Learning on AWS for Continuous Sentiment Analysis from Labeling...Amazon Web Services
by Zignal Labs
Today, machine learning solves a range of everyday business challenges. Companies are leveraging machine learning to understand how their brands are perceived in the marketplace across key stakeholder segments. How does the brand resonate with customers and the media? What product feedback and enhancements can be learned?
By harnessing the power of machine learning, Zignal monitors and analyzes – in real-time – brand conversations across social, broadcast, digital and traditional media channels. In this session, learn how Zignal leverages Amazon SageMaker, Amazon Mechanical Turk, AWS Code Pipeline and AWS Lambda to accurately measure the brand health of major enterprises such as NVIDIA and Airbnb. Zignal will dive deep into how Amazon SageMaker and these services work together on machine learning models in a real-time media environment.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes these barriers. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
Learn how to quickly build, train, and deploy machine learning models using Amazon SageMaker, an end-to-end machine learning platform. Amazon SageMaker simplifies machine learning with pre-built algorithms, support for popular deep learning frameworks, such as PyTorch, TensorFlow, and Apache MXNet, as well as one-click model training and deployment.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders can understand and support, and help create the right conditions for delivering successful ML-based solutions to your citizens. Understand AWS ML and AI services while relating to your specific requirements.
Speakers:
Manav Sehgal, Head of Solutions Architecture, AISPL
Atanu Roy, Specialist Solutions Architect, AISPL
Machine Learning with Amazon SageMaker - Algorithms and Frameworks - BDA304 -...Amazon Web Services
Algorithms and frameworks form a fundamental part of machine learning (ML). These critical components enable developers and data scientists to easily and quickly build ML models with well-defined interfaces for a range of use cases. The most commonly used algorithms and frameworks, built-in with Amazon SageMaker, make ML easier to address these use cases. In this session, we discuss the built-in algorithms and frameworks and how you can leverage them for your ML models. We also discuss the flexibility of bringing your own algorithm into Amazon SageMaker depending on your needs.
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.
Work with Machine Learning in Amazon SageMaker - BDA203 - Atlanta AWS SummitAmazon Web Services
Organizations are using machine learning (ML) to address a host of business challenges, from product recommendations to demand forecasting. Until recently, developing these ML models took considerable time and effort, and it required expertise. In this session, we dive deep into Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models quickly and easily. We walk through the features and benefits of Amazon SageMaker to get your ML models from concept to production.
Recommendation is one of the most popular applications in machine learning (ML). In this workshop, we’ll show you how to build a movie recommendation model based on factorization machines — one of the built-in algorithms of Amazon SageMaker — and the popular MovieLens dataset.
Learn how to get started with Amazon SageMaker—our fully-managed service that spans the entire machine learning (ML) workflow—so you can build, train, and deploy models quickly. Use Amazon SageMaker to label and prepare your data, choose an algorithm, train, tune, and optimize it for deployment, make predictions, and take action. Get your models to production faster with Amazon SageMaker SDKs, builder tools, and APIs tailored to your programming language or platform. Also, discover how Amazon SageMaker Ground Truth can aid in the adoption of ML technology for your organization.
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
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.
Level: 300-400
Speaker: Zlatan Dzinic - Partner Solutions Architect, AWS
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
Automatic Labelling and Model Tuning with Amazon SageMaker - AWS Summit SydneyAmazon Web Services
Developing machine learning models requires a lot of effort which often needs to be repeated over time as data distributions change. In this session you will learn about some of the latest concepts in Automatic Machine Learning including how to apply them to speed up development and achieve robust models over time. You will learn how to run a custom labelling job using Amazon SageMaker Ground Truth to build a larger data set to fine-tune your model. You will also learn how to tune your model’s hyperparameters using Amazon SageMaker’s Automatic Model Tuning capabilities and understand the theory of how bayesian optimisation is automatically applied for more accurate results and faster tuning.
This workshop requires a laptop and administrative access to your own AWS account.
Machine Learning: From Inception to Inference - AWS Summit SydneyAmazon Web Services
A streamlined end-to-end machine learning process enables operational efficiency and improved productivity for the data scientists, developers, and data engineers building machine learning solutions. Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. In this workshop, you will be able to ingest, explore, and process the data and then build, train, and deploy a machine learning model. Further, the workshop will also guide you to connect Amazon SageMaker endpoint server to the public Internet using AWS Lambda and Amazon API Gateway in order to effectively host the model to run inferences/predictions over a Single Page Application (SPA).
This workshop requires a laptop and administrative access to your own AWS account.
Using Machine Learning on AWS for Continuous Sentiment Analysis from Labeling...Amazon Web Services
by Zignal Labs
Today, machine learning solves a range of everyday business challenges. Companies are leveraging machine learning to understand how their brands are perceived in the marketplace across key stakeholder segments. How does the brand resonate with customers and the media? What product feedback and enhancements can be learned?
By harnessing the power of machine learning, Zignal monitors and analyzes – in real-time – brand conversations across social, broadcast, digital and traditional media channels. In this session, learn how Zignal leverages Amazon SageMaker, Amazon Mechanical Turk, AWS Code Pipeline and AWS Lambda to accurately measure the brand health of major enterprises such as NVIDIA and Airbnb. Zignal will dive deep into how Amazon SageMaker and these services work together on machine learning models in a real-time media environment.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes these barriers. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
Learn how to quickly build, train, and deploy machine learning models using Amazon SageMaker, an end-to-end machine learning platform. Amazon SageMaker simplifies machine learning with pre-built algorithms, support for popular deep learning frameworks, such as PyTorch, TensorFlow, and Apache MXNet, as well as one-click model training and deployment.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders can understand and support, and help create the right conditions for delivering successful ML-based solutions to your citizens. Understand AWS ML and AI services while relating to your specific requirements.
Speakers:
Manav Sehgal, Head of Solutions Architecture, AISPL
Atanu Roy, Specialist Solutions Architect, AISPL
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019AWS Summits
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Unleash the Power of ML with AWS | AWS Summit Tel Aviv 2019Amazon Web Services
How can we use Machine Learning to drive innovation?In this session, we present how to democratize ML and give every team the ability to use ML for innovation.We’ll demonstrate how we can use Sagemaker’s built in algorithms and distributed training to experiment more often and iterate faster. We’ll build a prediction of flights delay and integrate it to the product to increase the efficiency of the ground processes. In addition, we present the use of Amazon Forecast for predicting the number of flights that might be delayed in the next few days.
Amazon SageMaker - ML for every developer & data scientist ft. Workday - AIM2...Amazon Web Services
Machine learning (ML) provides innovation for every business. Until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker—a fully managed service that enables developers to build, train, and deploy ML models at scale—overcomes these barriers. We review its capabilities across data labeling, model building, model training, tuning, and production hosting. Additionally, Workday—leading provider of enterprise cloud applications for financial management, human capital management, and analytics—shares how it accelerated ML throughout its organization, the benefits gained, and why it standardized on Amazon SageMaker.
AWS Summit Singapore 2019 | Build, Train and Deploy Deep Learning Models on A...AWS Summits
Speaker: Pedro Paez, Specialist Solutions Architect, AWS
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.
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.
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.
Machine learning for developers & data scientists with Amazon SageMaker - AIM...Amazon Web Services
Machine learning (ML) offers innovation for every business. But until recently, developing ML models took time and effort, making it difficult for developers to get started. In this session, we demonstrate how Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy ML models at scale, overcomes those challenges. We review its capabilities, including data labeling, model building, model training, tuning, and production hosting.
Alexa transformed the smart home market segment and is now transforming how we interact with applications and technology at work. In this session, learn about how Alexa is an example of Conversational AI in Education.
AWS Summit Singapore 2019 | Accelerating ML Adoption with Our New AI servicesAmazon Web Services
Speaker: Ben Snively, Principal Solutions Architect - Data & Analytics, AWS
Note: This is part 2 of the deck.
Adding to the existing AI services, AWS continues to bridge the gap for developers to build ML solutions without the hurdle of having data science expertise. In this session learn about the new services announced at re: Invent (Forecast, Textract and Personalize) and get a preview of what to expect when building time series models, OCR and recommendation engines with little to no data science experience.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
Train once, deploy anywhere on the cloud and at the edge with Neo - AIM301 - ...Amazon Web Services
Developers spend much time and effort delivering machine learning (ML) models that can make fast and accurate predictions in real time. These models become even more critical for edge devices where memory and processing power are constrained. Amazon SageMaker Neo enables developers to run and develop models in the most optimized way. With Neo, developers can train ML models once and run them anywhere in the cloud and at the edge. In this chalk talk, we dive deep into Neo and show you how this capability of Amazon SageMaker automatically optimizes models built on TensorFlow, Apache MXNet, PYTorch, and ONNX.
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.
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
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
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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.
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. M L F R A M E W O R K S &
I N F R A S T R U C T U R E
The Amazon ML Stack: Broadest & Deepest Set of Capabilities
A I S E R V I C E S
R E K O G N I T I O N
I M A G E
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D
C O M P R E H E N D
M E D I C A L
L E XR E K O G N I T I O N
V I D E O
Vision Speech Chatbots
A M A Z O N S A G E M A K E R
B U I L D T R A I N
F O R E C A S TT E X T R A C T P E R S O N A L I Z E
D E P L O Y
Pre-built algorithms & notebooks
Data labeling (G R O U N D T R U T H )
One-click model training & tuning
Optimization ( N E O )
One-click deployment & hosting
M L S E R V I C E S
F r a m e w o r k s I n t e r f a c e s I n f r a s t r u c t u r e
E C 2 P 3
& P 3 d n
E C 2 C 5 F P G A s G R E E N G R A S S E L A S T I C
I N F E R E N C E
Models without training data (REINFORCEMENT LEARNING)
Algorithms & models ( A W S M A R K E T P L A C E )
Language Forecasting Recommendations
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NEW
NEW
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