Models are designed to help decision making through predictions, so they're only useful when deployed and available for an application to consume. In this module learn how to deploy models for real-time inferencing, and for batch inferencing.
Design patterns and plan for developing high available azure applicationsHimanshu Sahu
1. Design Patterns High Availability of Azure Applications
2. Practical Demo on points to take care for High Availability from Infrastructure point of view(the points we discussed in last seminar)
3. Different Patterns for High Availability
3.1 Health Endpoint Monitoring Pattern
3.2 Queue-based Load Leveling Pattern
3.2 Throttling Pattern
3.3 Retry Pattern
3.4 Multiple Datacenter Deployment Guidance
4. Architecture for High Availability of Azure Applications
5. best practices for developing High Available Azure Applications
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
Openstack Cloud Management and Automation Using Red Hat Cloudforms 4.0Prasad Mukhedkar
To success in Private Cloud, You have to have develop management and automation
strategy. Cloudforms is CPM (Cloud Platform Management) software that provides framework to develop management and automation strategy with its flaxible automation
module
Design patterns and plan for developing high available azure applicationsHimanshu Sahu
1. Design Patterns High Availability of Azure Applications
2. Practical Demo on points to take care for High Availability from Infrastructure point of view(the points we discussed in last seminar)
3. Different Patterns for High Availability
3.1 Health Endpoint Monitoring Pattern
3.2 Queue-based Load Leveling Pattern
3.2 Throttling Pattern
3.3 Retry Pattern
3.4 Multiple Datacenter Deployment Guidance
4. Architecture for High Availability of Azure Applications
5. best practices for developing High Available Azure Applications
The breath and depth of Azure products that fall under the AI and ML umbrella can be difficult to follow. In this presentation I’ll first define exactly what AI, ML, and deep learning is, and then go over the various Microsoft AI and ML products and their use cases.
Openstack Cloud Management and Automation Using Red Hat Cloudforms 4.0Prasad Mukhedkar
To success in Private Cloud, You have to have develop management and automation
strategy. Cloudforms is CPM (Cloud Platform Management) software that provides framework to develop management and automation strategy with its flaxible automation
module
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals
Tour de France Azure PaaS 2/7 Exécuter une applicationAlex Danvy
Il existe de nombreuses possibilités pour exécuter une application ou du code dans Azure. Nous examinerons les différentes options afin de les positionner les unes par rapport aux autres : Machines virtuelles, conteneurs, services, serverless.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
MLops on Vertex AI Presentation (AI/ML).pptxKnoldus Inc.
During this session, our focus will be on Google's Vertex AI suite, a comprehensive tool designed to facilitate MLOps within our machine learning workflow. Exploring its capabilities, we aim to understand how Vertex AI enhances the efficiency and management of our machine-learning operations.
Today, the large public Clouds - Azure and AWS - deploy at high-speed a diversity of services and features. Between Azure Functions, Lambda, Event Grid, Simple Workflow Service or Logic Apps, what to choose? Shall I go on Microservices? Event-Driven? Lambda Architecture? Deploy on Serverless? Containers? Modern Compute? Let's put a bit of order in all that. Enter the Modern Architecture, the foundation of all the new wave of Cloud services and not only. Session focused on application and infrastructure architecture, live examples based on Cloud, perspectives and roadmap of the corresponding services at Microsoft.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
Unleashing Apache Kafka and TensorFlow in the Cloud Kai Wähner
How can you leverage the flexibility and extreme scale in the public cloud combined with your Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures, which span multiple public clouds or bridge your on-premise data centre to cloud?
This talk will discuss and demo how you can leverage machine learning technologies such as TensorFlow with your Kafka deployments in public cloud to build a scalable, mission-critical machine learning infrastructure for data preprocessing and ingestion, and model training, deployment and monitoring.
The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, combination of different Deep Learning frameworks, data replication between data centres, intelligent real time microservices running on Kubernetes, and local deployment of analytic models for offline predictions.
Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data.:
I built a KSQL UDF for sensor analytics. It leverages the new API features of KSQL to build UDF / UDAF functions easily with Java to do continuous stream processing on incoming events.
Use Case: Connected Cars - Real Time Streaming Analytics using Deep Learning
Continuously process millions of events from connected devices (sensors of cars in this example).
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Cloud-Native DevOps: Simplifying application lifecycle management with AWS | ...Amazon Web Services
Organizations are migrating to the cloud in order to increase their agility and eliminate undifferentiated heavy lifting. At the same time, they’re embracing DevOps principles in order to deliver functionality faster and improve operational performance. Taken together, it’s possible to deliver agile, reliable applications with less overhead than ever before. However, it’s not always optimal to emulate traditional approaches to DevOps and configuration management in the cloud. No matter where you are in your DevOps journey, join us in this session to learn how to use AWS application lifecycle management services to focus on your mission, not your tooling.
Microservice message routing on KubernetesFrans van Buul
Slides related to a presentation done at GOTO Amsterdam in June 2018. How to split a given application into a microservices system, considerations regarding message routing between those microservices, and how to deploy everything: using the Axon stack, and running on Kubernetes
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Efficiently Removing Duplicates from a Sorted ArrayEng Teong Cheah
The RemoveDuplicates method efficiently removes duplicates from a sorted array in-place using a two-pointer technique, ensuring a time complexity of O(n) and a space complexity of O(1). This approach maintains the order of elements and requires no additional data structures.
After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes tech- niques for monitoring models and their data.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals
Tour de France Azure PaaS 2/7 Exécuter une applicationAlex Danvy
Il existe de nombreuses possibilités pour exécuter une application ou du code dans Azure. Nous examinerons les différentes options afin de les positionner les unes par rapport aux autres : Machines virtuelles, conteneurs, services, serverless.
MLOps and Reproducible ML on AWS with Kubeflow and SageMakerProvectus
Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
Estimating the Total Costs of Your Cloud Analytics PlatformDATAVERSITY
Organizations today need a broad set of enterprise data cloud services with key data functionality to modernize applications and utilize machine learning. They need a platform designed to address multi-faceted needs by offering multi-function Data Management and analytics to solve the enterprise’s most pressing data and analytic challenges in a streamlined fashion. They need a worry-free experience with the architecture and its components.
MLops on Vertex AI Presentation (AI/ML).pptxKnoldus Inc.
During this session, our focus will be on Google's Vertex AI suite, a comprehensive tool designed to facilitate MLOps within our machine learning workflow. Exploring its capabilities, we aim to understand how Vertex AI enhances the efficiency and management of our machine-learning operations.
Today, the large public Clouds - Azure and AWS - deploy at high-speed a diversity of services and features. Between Azure Functions, Lambda, Event Grid, Simple Workflow Service or Logic Apps, what to choose? Shall I go on Microservices? Event-Driven? Lambda Architecture? Deploy on Serverless? Containers? Modern Compute? Let's put a bit of order in all that. Enter the Modern Architecture, the foundation of all the new wave of Cloud services and not only. Session focused on application and infrastructure architecture, live examples based on Cloud, perspectives and roadmap of the corresponding services at Microsoft.
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
Unleashing Apache Kafka and TensorFlow in the Cloud Kai Wähner
How can you leverage the flexibility and extreme scale in the public cloud combined with your Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures, which span multiple public clouds or bridge your on-premise data centre to cloud?
This talk will discuss and demo how you can leverage machine learning technologies such as TensorFlow with your Kafka deployments in public cloud to build a scalable, mission-critical machine learning infrastructure for data preprocessing and ingestion, and model training, deployment and monitoring.
The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, combination of different Deep Learning frameworks, data replication between data centres, intelligent real time microservices running on Kubernetes, and local deployment of analytic models for offline predictions.
Deep Learning UDF for KSQL for Streaming Anomaly Detection of MQTT IoT Sensor Data.:
I built a KSQL UDF for sensor analytics. It leverages the new API features of KSQL to build UDF / UDAF functions easily with Java to do continuous stream processing on incoming events.
Use Case: Connected Cars - Real Time Streaming Analytics using Deep Learning
Continuously process millions of events from connected devices (sensors of cars in this example).
Big Data Adavnced Analytics on Microsoft AzureMark Tabladillo
This presentation provides a survey of the advanced analytics strengths of Microsoft Azure from an enterprise perspective (with these organizations being the bulk of big data users) based on the Team Data Science Process. The talk also covers the range of analytics and advanced analytics solutions available for developers using data science and artificial intelligence from Microsoft Azure.
Cloud-Native DevOps: Simplifying application lifecycle management with AWS | ...Amazon Web Services
Organizations are migrating to the cloud in order to increase their agility and eliminate undifferentiated heavy lifting. At the same time, they’re embracing DevOps principles in order to deliver functionality faster and improve operational performance. Taken together, it’s possible to deliver agile, reliable applications with less overhead than ever before. However, it’s not always optimal to emulate traditional approaches to DevOps and configuration management in the cloud. No matter where you are in your DevOps journey, join us in this session to learn how to use AWS application lifecycle management services to focus on your mission, not your tooling.
Microservice message routing on KubernetesFrans van Buul
Slides related to a presentation done at GOTO Amsterdam in June 2018. How to split a given application into a microservices system, considerations regarding message routing between those microservices, and how to deploy everything: using the Axon stack, and running on Kubernetes
Platforming the Major Analytic Use Cases for Modern EngineeringDATAVERSITY
We’ll describe some use cases as examples of a broad range of modern use cases that need a platform. We will describe some popular valid technology stacks that enterprises use in accomplishing these modern use cases of customer churn, predictive analytics, fraud detection, and supply chain management.
In many industries, to achieve top-line growth, it is imperative that companies get the most out of existing customer relationships. Customer churn use cases are about generating high levels of profitable customer satisfaction through the use of knowledge generated from corporate and external data to help drive a more positive customer experience (CX).
Many organizations are turning to predictive analytics to increase their bottom line and efficiency and, therefore, competitive advantage. It can make the difference between business success or failure.
Fraudulent activity detection is exponentially more effective when risk actions are taken immediately (i.e., stop the fraudulent transaction), instead of after the fact. Fast digestion of a wide network of risk exposures across the network is required in order to minimize adverse outcomes.
Supply chain leaders are under constant pressure to reduce overall supply chain management (SCM) costs while maintaining a flexible and diverse supplier ecosystem. They will leverage IoT, sensors, cameras, and blockchain. Major investments in advanced analytics, warehouse relocation, and automation, both in distribution centers and stores, will be essential for survival.
Dynamic resource allocation using virtual machines for cloud computing enviro...IEEEFINALYEARPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
Efficiently Removing Duplicates from a Sorted ArrayEng Teong Cheah
The RemoveDuplicates method efficiently removes duplicates from a sorted array in-place using a two-pointer technique, ensuring a time complexity of O(n) and a space complexity of O(1). This approach maintains the order of elements and requires no additional data structures.
After a model has been deployed, it's important to understand how the model is being used in production, and to detect any degradation in its effectiveness due to data drift. This module describes tech- niques for monitoring models and their data.
Data scientists have a duty to ensure they analyze data and train machine learning models responsibly; respecting individual privacy, mitigating bias, and ensuring transparency. This module explores some considerations and techniques for applying responsible machine learning principles.
By this stage of the course, you've learned the end-to-end process for training, deploying, and consum- ing machine learning models; but how do you ensure your model produces the best predictive outputs for your data? In this module, you'll explore how you can use the azure Machine Learning SDK to apply hyperparameter tuning and automated machine learning, and find the best model for your data.
Now that you understand the basics of running workloads as experiments that leverage data assets and compute resources, it's time to learn how to orchestrate these workloads as pipelines of connected steps. Pipelines are key to implementing an effective Machine Learning Operationalization (ML Ops) solution in Azure, so you'll explore how to define and run them in this session.
One of the key benefits of the cloud is the ability to leverage compute resources on demand, and use them to scale machine learning processes to an extent that would be infeasible on your own hardware.
Data is a fundamental element in any machine learning workload, so in this module, you will learn how to create and manage datastores and datasets in an Azure Machine Learning workspace, and how to use them in model training experiments.
This module introduces the Automated Machine Learning and Designer visual tools, which you can use to train, evaluate, and deploy machine learning models without writing any code.
You will learn how to provision an Azure Machine Learning workspace and use it to manage machine learning assets such as data, compute, model training code, logged metrics, and trained models. You will learn how to use the web-based Azure Machine Learning studio interface as well as the Azure Machine Learning SDK and developer tools like Visual Studio Code and Jupyter Notebooks to work with the assets in your workspace.
The mechanism that Docker and several other container runtimes use is known as a UnionFS. To best understand a union file system, consider a set of clear pieces of transparent paper.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
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.
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.
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
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
3. Inferencing?
In machine learning, inferencing refers to the use of a trained model to predict labels for
new data on which the model has not been trained. Often, the model is deployed as part
of a service that enables applications to request immediate, or real-time, predictions for
individual, or small numbers of data observations.
In Azure Machine Learning, you can create real-time inferencing solutions by deploying a
model as a service, hosted in a containerized platform, such as Azure Kubernetes Services
(AKS).
5. Machine learning inference during deployment
When deploying your AI model during production, you need to consider how it will make
predictions. The two main processes for AI models are:
•Batch inference: An asynchronous process that bases its predictions on a batch of
observations. The predictions are stored as files or in a database for end users or business
applications.
•Real-time (or interactive) inference: Frees the model to make predictions at any time
and trigger an immediate response. This pattern can be used to analyze streaming and
interactive application data.
6. Machine learning inference during deployment
Consider the following questions to evaluate your model, compare the two processes,
and select the one that suits your model:
•How often should predictions be generated?
•How soon are the results needed?
•Should predictions be generated individually, in small batches, or in large batches?
•Is latency to be expected from the model?
•How much compute power is needed to execute the model?
•Are there operational implications and costs to maintain the model?
7. Batch inference
Batch inference, sometimes called offline inference, is a simpler inference process that
helps models to run in timed intervals and business applications to store predictions.
Consider the following best practices for batch inference:
•Trigger batch scoring: Use Azure Machine Learning pipelines and
the ParallelRunStep feature in Azure Machine Learning to set up a schedule or event-
based automation.
•Compute options for batch inference: Since batch inference processes don't run
continuously, it's recommended to automatically start, stop, and scale reusable clusters
that can handle a range of workloads. Different models require different environments,
and your solution needs to be able to deploy a specific environment and remove it when
inference is over for the compute to be available for the next model.
8. Real-time inference
Real-time, or interactive, inference is architecture where model inference can be triggered
at any time, and an immediate response is expected. This pattern can be used to analyze
streaming data, interactive application data, and more. This mode allows you to take
advantage of your machine learning model in real time and resolves the cold-start
problem outlined above in batch inference.
The following considerations and best practices are available if real-time inference is right
for your model:
•The challenges of real-time inference: Latency and performance requirements make
real-time inference architecture more complex for your model. A system might need to
respond in 100 milliseconds or less, during which it needs to retrieve the data, perform
inference, validate and store the model results, run any required business logic, and
return the results to the system or application.
9. Real-time inference
•Compute options for real-time inference: The best way to implement real-time
inference is to deploy the model in a container form to Docker or Azure Kubernetes
Service (AKS) cluster and expose it as a web service with a REST API. This way, the model
runs in its own isolated environment and can be managed like any other web service.
Docker and AKS capabilities can then be used for management, monitoring, scaling, and
more. The model can be deployed on-premises, in the cloud, or on the edge. The
preceding compute decision outlines real-time inference.
10. Real-time inference
•Multiregional deployment and high availability: Regional deployment and high
availability architectures need to be considered in real-time inference scenarios, as latency
and the model's performance will be critical to resolve. To reduce latency in multiregional
deployments, it's recommended to locate the model as close as possible to the
consumption point. The model and supporting infrastructure should follow the business'
high availability and DR principles and strategy.
11. Create a real-time
inference service
https://ceteongvanness.wordpress.com/2022/11/01/
create-a-real-time-inference-service/