The document discusses Amazon SageMaker, a fully managed service that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides pre-built algorithms, notebooks, and frameworks to simplify common ML tasks. Models can be trained using SageMaker's high-performance infrastructure and hyperparameter tuning capabilities. Trained models can then be deployed for prediction and scaled to production using SageMaker's hosting capabilities. The document highlights several SageMaker features including algorithms, compilation, inference pipelines, and customers.
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.
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.
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
Predicting the Future with Amazon SageMaker
Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this session you will learn how to use built-in, high performance machine learning algorithms for predictions and computer vision within your application. We will deploy machine learning models into production and start generating classifications with a few API calls using the SageMaker SDK. Additionally we will demonstrate how to run your custom trained machine learning model directly out of your web application to classify incoming user generated content.
Steve Shirkey, ASEAN Solutions Architect, Amazon Web Services
Workshop slides for the introduction to Amazon SageMaker, and integration of Amazon SageMaker with other tools within your AWS environment. Visit https://aws.amazon.com/sagemaker for more information.
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker - AWS Summit S...Amazon Web Services
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker
With IoT, machine learning is going everywhere. Using Amazon SageMaker it's never been easier to build Intelligent Things. In this session we look at how we can push intelligence from cloud-trained models to the edge using AWS Greengrass and explore how devices such as AWS DeepLens make it easy to bring intelligence to your things.
Jan Haak, Global Solutions Architect, Amazon Web Services
Building a Recommender System Using Amazon SageMaker's Factorization Machine ...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm
Factorization Machines are a powerful algorithm in the click prediction and recommendation space. Amazon SageMaker has a nearly infinitely scalable implementation that we'll show you how to use to build a recommender of your own.
Speaker: David Arpin - AI Platform Selections Leader, AI Platforms
[REPEAT] Deep Learning Applications Using TensorFlow (AIM401-R) - AWS re:Inve...Amazon Web Services
The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow.
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)
Supercharge Your ML Model with SageMaker - AWS Summit Sydney 2018Amazon Web Services
Supercharge Your Machine Learning Model with Amazon SageMaker
In this session you will learn how to use Amazon SageMaker to build, train, test, and deploy a machine learning model. We will use a real life use case to share the simplicity of building and deploying ML models on Amazon SageMaker.
Koorosh Lohrasbi, Solutions Architect, Amazon Web Services
Accelerate Machine Learning Workloads using Amazon EC2 P3 Instances - SRV201 ...Amazon Web Services
Organizations are tackling exponentially complex questions across advanced scientific, energy, high tech, and medical fields. Machine learning (ML) makes it possible to quickly explore a multitude of scenarios and generate the best answers, ranging from image, video, and speech recognition to autonomous vehicle systems and weather prediction. Learn how Amazon EC2 P3 instances can help data scientists, researchers, and developers significantly lower their time and cost to train ML models, speed up their development process, and bring innovations to market sooner.
Tensors for topic modeling and deep learning on AWS SagemakerAnima Anandkumar
Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. This session will present recently developed tensor algorithms for topic modeling and deep learning with vastly improved performance over existing methods.
Topic models enable automated categorization of large document corpora, without requiring labeled data for training. They go beyond simple clustering since they allow for documents to have multiple topics. Tensor methods provide a fast and a guaranteed method for training these models. They incorporate co-occurrence statistics of triplets of words in documents. We are releasing a fast and a robust implementation that vastly outperform existing solutions while providing significantly faster training times and better topic quality. Moreover, training and inference are decoupled in our algorithm, so the user can select the relevant part based on their requirements. We will present benchmarks across multiple datasets of different sizes and AWS instance types, and provide notebook examples.
Building Deep Learning Applications with TensorFlow and SageMaker on AWS - Te...Amazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we provide an overview of deep learning, focusing on getting started with the TensorFlow framework on AWS.
Amazon SageMaker è un servizio gestito per sviluppatori e data scientist che consente di progettare, addestrare e distribuire modelli di Machine Learning su larga scala. In questo webinar esploreremo le funzionalità di questo servizio, dalle istanze notebook Jupyter ai servizi di training e hosting, per poi discutere di aspetti come il labeling di dataset e l’ottimizzazione dei modelli. Successivamente, vedremo in modo pratico come utilizzare il servizio per implementare, addestrare e distribuire un modello di esempio.
Integrating Amazon SageMaker into your Enterprise - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Get an introduction to Amazon SageMaker
- Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment
- View a walkthrough of the machine learning lifecycle to cover best practices in the ML process
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, natural language processing, and more at scale. In this session, learn how to get started with Apache MXNet on the Amazon SageMaker machine learning platform. Chick-fil-A share how they got started with MXNet on Amazon SageMaker to measure waffle fry freshness and how they leverage AWS services to improve the Chick-fil-A guest experience.
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.
Predicting the Future with Amazon SageMaker - AWS Summit Sydney 2018Amazon Web Services
Predicting the Future with Amazon SageMaker
Amazon SageMaker removes all the barriers that typically slow down developers who want to use machine learning. In this session you will learn how to use built-in, high performance machine learning algorithms for predictions and computer vision within your application. We will deploy machine learning models into production and start generating classifications with a few API calls using the SageMaker SDK. Additionally we will demonstrate how to run your custom trained machine learning model directly out of your web application to classify incoming user generated content.
Steve Shirkey, ASEAN Solutions Architect, Amazon Web Services
Workshop slides for the introduction to Amazon SageMaker, and integration of Amazon SageMaker with other tools within your AWS environment. Visit https://aws.amazon.com/sagemaker for more information.
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker - AWS Summit S...Amazon Web Services
Intelligence of Things: IoT, AWS DeepLens and Amazon SageMaker
With IoT, machine learning is going everywhere. Using Amazon SageMaker it's never been easier to build Intelligent Things. In this session we look at how we can push intelligence from cloud-trained models to the edge using AWS Greengrass and explore how devices such as AWS DeepLens make it easy to bring intelligence to your things.
Jan Haak, Global Solutions Architect, Amazon Web Services
Building a Recommender System Using Amazon SageMaker's Factorization Machine ...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Building a Recommender System Using Amazon SageMaker's Factorization Machine Algorithm
Factorization Machines are a powerful algorithm in the click prediction and recommendation space. Amazon SageMaker has a nearly infinitely scalable implementation that we'll show you how to use to build a recommender of your own.
Speaker: David Arpin - AI Platform Selections Leader, AI Platforms
[REPEAT] Deep Learning Applications Using TensorFlow (AIM401-R) - AWS re:Inve...Amazon Web Services
The TensorFlow deep learning framework is used for developing diverse AI applications including computer vision, natural language, speech, and translation. In this session, learn how to use TensorFlow within the Amazon SageMaker machine learning platform. This code-level session also includes tutorials and examples using TensorFlow.
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)
Supercharge Your ML Model with SageMaker - AWS Summit Sydney 2018Amazon Web Services
Supercharge Your Machine Learning Model with Amazon SageMaker
In this session you will learn how to use Amazon SageMaker to build, train, test, and deploy a machine learning model. We will use a real life use case to share the simplicity of building and deploying ML models on Amazon SageMaker.
Koorosh Lohrasbi, Solutions Architect, Amazon Web Services
Accelerate Machine Learning Workloads using Amazon EC2 P3 Instances - SRV201 ...Amazon Web Services
Organizations are tackling exponentially complex questions across advanced scientific, energy, high tech, and medical fields. Machine learning (ML) makes it possible to quickly explore a multitude of scenarios and generate the best answers, ranging from image, video, and speech recognition to autonomous vehicle systems and weather prediction. Learn how Amazon EC2 P3 instances can help data scientists, researchers, and developers significantly lower their time and cost to train ML models, speed up their development process, and bring innovations to market sooner.
Tensors for topic modeling and deep learning on AWS SagemakerAnima Anandkumar
Tensors are higher order extensions of matrices that can incorporate multiple modalities and encode higher order relationships in data. This session will present recently developed tensor algorithms for topic modeling and deep learning with vastly improved performance over existing methods.
Topic models enable automated categorization of large document corpora, without requiring labeled data for training. They go beyond simple clustering since they allow for documents to have multiple topics. Tensor methods provide a fast and a guaranteed method for training these models. They incorporate co-occurrence statistics of triplets of words in documents. We are releasing a fast and a robust implementation that vastly outperform existing solutions while providing significantly faster training times and better topic quality. Moreover, training and inference are decoupled in our algorithm, so the user can select the relevant part based on their requirements. We will present benchmarks across multiple datasets of different sizes and AWS instance types, and provide notebook examples.
Building Deep Learning Applications with TensorFlow and SageMaker on AWS - Te...Amazon Web Services
Deep learning continues to push the state of the art in domains such as computer vision, natural language understanding, and recommendation engines. One of the key reasons for this progress is the availability of highly flexible and developer friendly deep learning frameworks. In this workshop, we provide an overview of deep learning, focusing on getting started with the TensorFlow framework on AWS.
Amazon SageMaker è un servizio gestito per sviluppatori e data scientist che consente di progettare, addestrare e distribuire modelli di Machine Learning su larga scala. In questo webinar esploreremo le funzionalità di questo servizio, dalle istanze notebook Jupyter ai servizi di training e hosting, per poi discutere di aspetti come il labeling di dataset e l’ottimizzazione dei modelli. Successivamente, vedremo in modo pratico come utilizzare il servizio per implementare, addestrare e distribuire un modello di esempio.
Integrating Amazon SageMaker into your Enterprise - AWS Online Tech TalksAmazon Web Services
Learning Objectives:
- Get an introduction to Amazon SageMaker
- Learn how to integrate Amazon SageMaker and other AWS Services within an Enterprise environment
- View a walkthrough of the machine learning lifecycle to cover best practices in the ML process
Build Deep Learning Applications Using Apache MXNet - Featuring Chick-fil-A (...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, natural language processing, and more at scale. In this session, learn how to get started with Apache MXNet on the Amazon SageMaker machine learning platform. Chick-fil-A share how they got started with MXNet on Amazon SageMaker to measure waffle fry freshness and how they leverage AWS services to improve the Chick-fil-A guest experience.
Build Deep Learning Applications Using Apache MXNet, Featuring Workday (AIM40...Amazon Web Services
The Apache MXNet deep learning framework is used for developing, training, and deploying diverse AI applications, including computer vision, speech recognition, and natural language processing at scale. In this session, learn how to get started with MXNet on the Amazon SageMaker machine learning platform. Hear from Workday about how they built computer vision and natural language processing (NLP) models using MXNet to automatically extract information from paper documents, such as expense receipts and populate data records. Workday also shares its experience using Sockeye, an MXNet toolkit for quickly prototyping sequence-to-sequence NLP models.
Build, Train, and Deploy Machine Learning for the Enterprise with Amazon Sage...Amazon Web Services
Machine learning (ML) is rapidly being adopted by enterprises, enabling them to be nimble and align technical solutions to solve real-world business problems. ML use cases include diagnosis and research in healthcare, financial fraud detection, natural language processing (NLU), and accurate statistics in sports. Amazon SageMaker is a fully managed platform that enables developers to build, train, and deploy enterprise-scale ML models quickly and easily. In this workshop, we build an ML model using Amazon SageMaker’s built-in algorithms and frameworks. We train the model to achieve a high level of accurate predictions, then we deploy the model in production to achieve best results. Gain an understanding of how Amazon SageMaker removes the complexity and barriers to use and deploy ML models.
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.
Building, Training, and Deploying fast.ai Models Using Amazon SageMaker (AIM4...Amazon Web Services
In a short space of time, fast.ai has become a popular Deep Learning library, driven by the success of the fast.ai online Massive Open Online Course (MOOC). It has allowed SW developers to achieve, in the span of a few weeks, state-of-the-art results in domains such as Computer Vision (CV), Natural Language Processing (NLP), and structured data machine learning. In this chalk talk, we go into the details of building, training, and deploying fast.ai-based models using Amazon SageMaker.
Perform Machine Learning at the IoT Edge using AWS Greengrass and Amazon Sage...Amazon Web Services
"Learning Objectives:
- Develop intelligent IoT edge solutions using AWS Greengrass
- Develop data science models in the cloud with Amazon SageMaker
- Learn how AWS Greengrass and Amazon SageMaker enable you to perform machine learning at the edge"
Building WhereML, an AI Powered Twitter Bot for Guessing Locations of Picture...Amazon Web Services
The WhereML Twitter bot is built on the LocationNet model which is trained with the Berkley Multimedia Commons public dataset of 33.9 million geotagged images from Flickr (and other sources). The model is based on a ResNet-101 architecture and adds a classification layer that splits the earth into ~15000 cells created with Google’s S2 spherical geometry library. This model is based on prior work completed at Berkley and Google.
In this session we’ll start by describing AI in general terms then diving into deep learning and the MXNet framework. We’ll describe the LocationNet model in detail and show how it is trained and created in Amazon SageMaker. Finally, we’ll talk about the Twitter Account Activity webhooks API and how to interact with it using an API Gateway and AWS Lambda function.
Attendees are encouraged to interact with the bot in real-time at whereml.bot or on twitter at @WhereML
All code used in this project is open source and was written live on twitch.tv/aws and attendees are encouraged to experiment with it.
[AWS Innovate 온라인 컨퍼런스] 간단한 Python 코드만으로 높은 성능의 기계 학습 모델 만들기 - 김무현, AWS Sr.데이...Amazon Web Services Korea
발표자료 다시보기: https://youtu.be/xnimaVNTWfc
여러분의 애플리케이션에 인공 지능 기능을 추가하는 방법 중 하나로, GluonCV 및 AutoGluon 라이브러리를 이용해서 간단한 Python 코드로 높은 성능의 기계 학습 모델을 만들고 이를 예측에 사용하는 방법을 소개합니다. 정형 데이터에 대한 분류 또는 수치 예측 모델 생성부터 이미지 분류, 객체 탐지, 세그먼테이션, 행동 인식 등의 모델을 기계 학습에 대한 전문 지식이 없이도 자동으로 만들고 활용하는 방법을 알아봅니다.
Machine Learning e Amazon SageMaker: Algoritmos, Modelos e Inferências - MCL...Amazon Web Services
Atualmente, as organizações estão usando machine learning (ML) para endereçar uma série de desafios nos negócios, desde recomenções de produtos e previsão de preços, até o rastreamento da progressão de doença e previsão de demanda. Até recentemente, desenvolver esses modelos de ML demorava um período significante de tempo e esforços, e exigia especialização nesse campo. Nesta sessão, apresentaremos o Amazon SageMaker, um seviço ML totalmente gerenciado que permite desenvolvedores e cientistas de dados desenvolver e implementar modelos de aprendizagem profunda com mais rapidez e facilidade. Analisaremos os recursos e os benefícios do Amazon SageMaker e discutiremos os algoritmos ML exclusivamente projetados que permitem treinamento otimizado do modelo, para levar você à rápida produtividade.
Work with Machine Learning in Amazon SageMaker - BDA203 - Toronto 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.
Supercharge your Machine Learning Solutions with Amazon SageMakerAmazon Web Services
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. You'll also hear how and why Intuit is using Amazon SageMaker on AWS for real-time fraud detection.
BDA301 Working with Machine Learning in Amazon SageMaker: Algorithms, Models,...Amazon Web Services
Today, organizations are using machine learning (ML) to address a host of business challenges, from product recommendations and pricing predictions, to tracking disease progression and demand forecasting. Until recently, developing these ML models took a significant amount of time and effort, and it required expertise in this field. In this session, we introduce you to Amazon SageMaker, a fully managed ML service that enables developers and data scientists to develop and deploy deep learning models more quickly and easily. We walk through the features and benefits of Amazon SageMaker and discuss the uniquely designed ML algorithms that allow for optimized model training, to get you to production fast.
Similar to Julien Simon, Principal Technical Evangelist at Amazon - Machine Learning: From notebook to Production with Amazon SageMaker (20)
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
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.
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!
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...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.
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.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
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.