GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in Open Source

This document discusses the role of data, algorithms, and people in driving transformation. It emphasizes that code and software are changing the world, and that data ecosystems and alliances will be important going forward. Open source is presented as a way to defend community through code and products. The document also stresses the importance of building ecosystems rather than just products, treating data science as a team sport, and using storytelling in conjunction with data.

AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address different use cases and needs. This deck will help you to gain insight into practical use cases for Amazon Lex, Amazon Polly, and Amazon Rekognition, and learn about newly announced services Amazon Rekognition Video, Amazon Comprehend, Amazon Translate, and Amazon Transcribe. This presentation took place in Australia and New Zealand as part of the AWS Learning Series in 2018.

Materi ttg artificial intelligence

En esta reunión virtual, damos una introducción a la plataforma de aprendizaje automático de código abierto número 1, H2O-3 y te mostramos cómo puedes usarla para desarrollar modelos para resolver diferentes casos de uso.

Ομιλία- Παρουσίαση: Ανδρέας Τσαγκάρης, VP & Chief Technology Officer, Performance Technologies Τίτλος Παρουσίασης: “Big Data on Linux on Power Systems”

My presentation at the BBC2019 conference. While the current AI fascination is fueled by Machine Learning, the architecture and application landscape is being redesigned around Microservices and APIs. These technologies are combining forces to affect many facets of business, creating a paradigm shift all around you. Do you know how to take advantage of the tsunami created by these technologies? In this session, we will explain these technologies and how to extract business value from them. We will demonstrate how line of business people can integrate machine learning into business decisions that are explainable, auditable, and traceable and how they can easily assemble business automations that orchestrate a series of microservices via modern API platforms. With this knowledge in hand, you will be ready to face the next wave of technologies that are hitting your organization.

This document provides an overview of Google's generative AI offerings. It discusses large language models (LLMs) and what is possible with generative AI on Google Cloud, including Google's offerings like Vertex AI, Generative AI App Builder, and Foundation Models. It also discusses how enterprises can access, customize and deploy large models through Google Cloud to build innovative applications.

Slides of 1 hour session of Martin Kaltenböck (CFO and Managing Partner of Semantic Web Company / PoolParty Software Ltd) on 19 March 2019 in Boston, US at the Enterprise Data World 2019, with its title: Benefiting from Semantic AI along the data life cycle.

In today’s context, the big data market is rapidly undergoing contortions that define market maturity, such as consolidation. Big data refers to large volumes of data. This can be both structured and unstructured data. Big data is data that is huge in size and grows exponentially with time. As the data is too large and complex, traditional data management tools are not sufficient for storing or processing it efficiently. But analyzing big data is crucial to know the patterns and trends to be adopted to improve your business.

Modern Thinking: Cómo el Big Data y Cognitive están cambiando la estrategia de Marketing Por: Ismael Yuste, Strategic Cloud Engineer Google Cloud Presentación: Introducción a las soluciones Big Data de Google

The document discusses trends driving the need for a new content management approach, including the growth of digital content, mobility, cloud computing and more interconnected digital experiences. It notes the limitations of legacy content management systems in addressing these trends. The presentation outlines Alfresco's approach to a digital platform, including smart process applications, RESTful APIs, scalability, and cloud-native design. This enables flexible content and process management to drive digital transformation.

Introducing MindsLab company overview, products & services and success stories and use case based on maum.ai AIaaS platform.

1) Real-time machine learning with Redis and Apache Spark allows training models with Spark and serving them through Redis for fast, scalable predictions. 2) Databricks provides a unified analytics platform using Spark and Redis that simplifies data integration, experimentation, machine learning and deployment for data scientists, engineers and analysts. 3) Their platform includes Spark for training models from big data, Redis for serving the trained models in production with high performance, scalability and availability.

This document discusses how Perficient, an IT consulting firm, can help clients integrate big data into their organizations at lower total costs. It provides an overview of Perficient's services and solutions expertise in areas like business intelligence, customer experience, enterprise resource planning, and mobile platforms. The document also profiles Perficient with details on its history, locations, colleagues, and partnership model. Finally, it outlines an agenda for an event on balancing innovation and costs with big data, including discussions on PowerCenter Big Data Edition and what customers are doing with Informatica and big data.

Paul discusses how APIs are touching every facet of our society and the underlying trends that are going to generate nearly 1 billion APIs in the coming years.

Data Orchestration Summit 2020 organized by Alluxio https://www.alluxio.io/data-orchestration-summit-2020/ Achieving Massive Concurrency & Sub-second Query Latency on Cloud Warehouses & Data Lakes with Kyligence Cloud George Demarest, Head of Marketing, Kyligence About Alluxio: alluxio.io Engage with the open source community on slack: alluxio.io/slack

¿Cómo puede llevar el aprendizaje automático a las masas? Los proyectos de Machine Learning con la búsqueda de talento, el tiempo para construir e implementar modelos y confiar en los modelos que se construyen. ¿Cómo puede tener varios equipos en su organización para crear modelos de ML precisos sin ser expertos en ciencia de datos o aprendizaje automático? ¿Se pregunta sobre los diferentes sabores de AutoML? H2O Driverless AI emplea las técnicas de científicos expertos en datos en una aplicación fácil de usar que ayuda a escalar sus esfuerzos de ciencia de datos. La inteligencia artificial Driverless permite a los científicos de datos trabajar en proyectos más rápido utilizando la automatización y la potencia de computación de vanguardia de las GPU para realizar tareas en minutos que solían tomar meses. Con H2O Driverless AI, todos, incluyendo expertos y científicos de datos junior, científicos de dominio e ingenieros de datos pueden desarrollar modelos confiables de aprendizaje automático. Esta plataforma de aprendizaje automático de última generación ofrece una funcionalidad única y avanzada para la visualización de datos, la ingeniería de características, la interpretabilidad del modelo y la implementación de baja latencia. H2O Driverless AI hace: * Visualización automática de datos * Ingeniería automática de funciones a nivel de Grandmaster * Selección automática del modelo * Ajuste y capacitación automáticos del modelo * Paralelización automática utilizando múltiples CPU o GPU * Ensamblaje automático del modelo *automática del Interpretaciónaprendizaje automático (MLI) * Generación automática de código de puntuación ¿Quieres probarlo tú mismo? Puede obtener una prueba gratuita aquí: H2O Driverless AI trial. Venga a esta sesión y descubra cómo comenzar con el Aprendizaje automático automático con AI sin conductor H2O, y cree modelos potentes con solo unos pocos clics. ¡Te veo pronto! Acerca de H2O.ai H2O.ai es una empresa visionaria de software de código abierto de Silicon Valley que creó y reimaginó lo que es posible. Somos una empresa de fabricantes que trajeron al mercado nuevas plataformas y tecnologías para impulsar el movimiento de inteligencia artificial. Somos los creadores de, H2O, la principal plataforma de aprendizaje de ciencia de datos de fuente abierta y de aprendizaje automático utilizada por casi la mitad de Fortune 500 y en la que confían más de 14,000 organizaciones y cientos de miles de científicos de datos de todo el mundo.

Here are some key points about benchmarking and evaluating generative AI models like large language models: - Foundation models require large, diverse datasets to be trained on in order to learn broad language skills and knowledge. Fine-tuning can then improve performance on specific tasks. - Popular benchmarks evaluate models on tasks involving things like commonsense reasoning, mathematics, science questions, generating truthful vs false responses, and more. This helps identify model capabilities and limitations. - Custom benchmarks can also be designed using tools like Eval Studio to systematically test models on specific applications or scenarios. Both automated and human evaluations are important. - Leaderboards like HELM aggregate benchmark results to compare how different models perform across a wide range of tests and metrics.

Pritika Mehta, Co-Founder, Butternut.ai H2O Open Source GenAI World SF 2023

The document discusses LLMOps (Large Language Model Operations) compared to traditional MLOps. Some key points: - LLMOps and MLOps face similar challenges across the development lifecycle, but LLMOps requires more GPU resources and integration is faster due to more models in each application. Evaluation is also less clear. - The LLMOps field is around the 5th generation of models, with debates around proprietary vs open source models, and balancing privacy, cost and control. - LLMOps platforms are emerging to provide solutions for tasks like prompting, embedding databases, evaluation, and governance, similar to how MLOps platforms have evolved.

The document discusses optimizing question answering systems called RAG (Retrieve-and-Generate) stacks. It outlines challenges with naive RAG approaches and proposes solutions like improved data representations, advanced retrieval techniques, and fine-tuning large language models. Table stakes optimizations include tuning chunk sizes, prompt engineering, and customizing LLMs. More advanced techniques involve small-to-big retrieval, multi-document agents, embedding fine-tuning, and LLM fine-tuning.

Sandeep Singh, Head of Applied AI Computer Vision, Beans.ai H2O Open Source GenAI World SF 2023 In the modern era of machine learning, leveraging both open-source and closed-source solutions has become paramount for achieving cutting-edge results. This talk delves into the intricacies of seamlessly integrating open-source Large Language Model (LLM) solutions like Vicuna, Falcon, and Llama with industry giants such as ChatGPT and Google's Palm. As the demand for fine-tuned and specialized datasets grows, it is imperative to understand the synergy between these tools. Attendees will gain insights into best practices for building and enriching datasets tailored for fine-tuning tasks, ensuring that their LLM projects are both robust and efficient. Through real-world examples and hands-on demonstrations, this talk will equip attendees with the knowledge to harness the power of both open and closed-source tools in a coherent and effective manner.

Patrick Hall, Professor, AI Risk Management, The George Washington University H2O Open Source GenAI World SF 2023 Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you!

Dr. Alexy Khrabrov, Open Source Science Community Director, IBM H2O Open Source GenAI World SF 2023 In this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.

The document announces the launch of the H2O GenAI App Store, which provides a collection of applications that make it easier for average users to leverage large language models through custom interfaces for specific tasks like getting gardening advice or feedback on code. The app store is designed to accelerate the development of these GenAI apps using the H2O Wave platform and provides access to H2OGPTE for retrieval augmented generation and language model calls. Developers can also contribute their own apps through the GitHub repository listed.

Megan Kurka, Vice President, Customer Data Scientist, H2O.ai H2O Open Source GenAI World SF 2023 Discover the transformative power of Applied Gen AI. Learn how the H2O team builds customized applications and workflows that integrate capabilities of Gen AI and AutoML specifically designed to address and enhance financial use cases. Explore real world examples, learn best practices, and witness firsthand how our innovative solutions are reshaping the landscape of finance technology.

This document discusses techniques for improving language models (LLMs) discussed in recent papers. It describes building blocks of LLMs like fine-tuning, foundation training, memory, and databases. Specific techniques covered include LIMA which uses 1,000 carefully curated examples, instruction backtranslation to generate question-answer pairs, fine-tuning models on API examples like Gorilla, and reducing false answers through techniques like not agreeing with incorrect user opinions. The goal is to discuss cutting edge tricks to build better LLMs.

Pascal Pfeiffer, Principal Data Scientist, H2O.ai H2O Open Source GenAI World SF 2023 This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.

- Jon McKinney, Director of Research, H2O.ai - Arno Candel, Chief Technology Officer, H2O.ai H2O Open Source GenAI World SF 2023

This document discusses using large language models (LLMs) for text classification tasks. It begins by describing how LLMs are commonly used for text generation and question answering. For classification, models are usually trained supervised on labeled data. The document then explores using LLMs for zero-shot classification without training, and techniques like fine-tuning LLMs on tasks to improve performance. It provides an example of fine-tuning an LLM on a financial sentiment dataset. The document concludes by describing H2O.ai's LLM Studio tool for fine-tuning and a few Kaggle competitions where LLMs achieved success in text classification.

1) Generative AI (GenAI) enables the creation of novel content by learning patterns in unstructured data rather than labeling outputs like traditional AI. 2) Both traditional and generative AI models lack transparency and may contain biases, but generative models can additionally hallucinate or leak private information. 3) To interpret generative models, researchers evaluate accuracy globally by checking for hallucinations or undesirable content, and locally by confirming the quality of individual responses.

Luiz Pizzato, Executive Manager Artificial Intelligence, Commonwealth Bank H2O Open Source GenAI World SF 2023

Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto. In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.

In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization. To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g Speaker: Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)

The chances of successfully implementing AI strategies within an organization significantly improve when you can recognize where your organization is on the maturity scale. Over this course, you will learn the keys to unlocking value with AI which include asking the right questions about the problems you are solving and ensuring you have the right cross-section of talent, tools, and resources. By the end of this module, you should be able to recognize where your organization is on the AI transformation spectrum and identify some strategies that can get you to the next stage in your journey. To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course To find the Youtube video about this presentation: https://youtu.be/PJgr2epM6qs Speakers: Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist) Ingrid Burton (H2O.ai - CMO)

Machine Learning Model Deployment and Scoring on the Edge with Automatic Machine Learning and Data Flow YouTube Video URL: https://youtu.be/gB0bTH-L6DE Deploying Machine Learning models to the edge can present significant ML/IoT challenges centered around the need for low latency and accurate scoring on minimal resource environments. H2O.ai's Driverless AI AutoML and Cloudera Data Flow work nicely together to solve this challenge. Driverless AI automates the building of accurate Machine Learning models, which are deployed as light footprint and low latency Java or C++ artifacts, also known as a MOJO (Model Optimized). And Cloudera Data Flow leverage Apache NiFi that offers an innovative data flow framework to host MOJOs to make predictions on data moving on the edge.

This presentation was made on June 30th, 2020. Recording of the presentation is available here: https://youtu.be/9LajqAL_CU8 As enterprises “make their own AI”, a new set of challenges emerge. Maintaining reproducibility, traceability, and verifiability of machine learning models, as well as recording experiments, tracking insights, and reproducing results, are key. Collaboration between teams is also necessary as “model factories” are created for enterprise-wide model data science efforts. Additionally, monitoring of models ensures that drift or performance degradation is addressed with either retraining or model updates. Finally, data and model lineage in case of rollbacks or addressing regulatory compliance is necessary. H2O ModelOps delivers centralized catalog and management, deployment, monitoring, collaboration, and administration of machine learning models. In this webinar, we learn how H2O can assist with operationalizing, scaling and managing production deployments. Speaker's Bio: Felix is a part of the Customer Success team in Asia Pacific at H2O.ai. An engineer and an IIM alumni, Felix has held prominent positions in the data science industry.

Kief Morris rethinks the infrastructure code delivery lifecycle, advocating for a shift towards composable infrastructure systems. We should shift to designing around deployable components rather than code modules, use more useful levels of abstraction, and drive design and deployment from applications rather than bottom-up, monolithic architecture and delivery.

These fighter aircraft have uses outside of traditional combat situations. They are essential in defending India's territorial integrity, averting dangers, and delivering aid to those in need during natural calamities. Additionally, the IAF improves its interoperability and fortifies international military alliances by working together and conducting joint exercises with other air forces.

This presentation explores the practical application of image description techniques. Familiar guidelines will be demonstrated in practice, and descriptions will be developed “live”! If you have learned a lot about the theory of image description techniques but want to feel more confident putting them into practice, this is the presentation for you. There will be useful, actionable information for everyone, whether you are working with authors, colleagues, alone, or leveraging AI as a collaborator. Link to presentation recording and transcript: https://bnctechforum.ca/sessions/details-of-description-part-ii-describing-images-in-practice/ Presented by BookNet Canada on June 25, 2024, with support from the Department of Canadian Heritage.

Your comprehensive guide to RPA in healthcare for 2024. Explore the benefits, use cases, and emerging trends of robotic process automation. Understand the challenges and prepare for the future of healthcare automation

The integration of programming into civil engineering is transforming the industry. We can design complex infrastructure projects and analyse large datasets. Imagine revolutionizing the way we build our cities and infrastructure, all by the power of coding. Programming skills are no longer just a bonus—they’re a game changer in this era. Technology is revolutionizing civil engineering by integrating advanced tools and techniques. Programming allows for the automation of repetitive tasks, enhancing the accuracy of designs, simulations, and analyses. With the advent of artificial intelligence and machine learning, engineers can now predict structural behaviors under various conditions, optimize material usage, and improve project planning.

This is a powerpoint that features Microsoft Teams Devices and everything that is new including updates to its software and devices for May 2024

We are honored to launch and host this event for our UiPath Polish Community, with the help of our partners - Proservartner! We certainly hope we have managed to spike your interest in the subjects to be presented and the incredible networking opportunities at hand, too! Check out our proposed agenda below 👇👇 08:30 ☕ Welcome coffee (30') 09:00 Opening note/ Intro to UiPath Community (10') Cristina Vidu, Global Manager, Marketing Community @UiPath Dawid Kot, Digital Transformation Lead @Proservartner 09:10 Cloud migration - Proservartner & DOVISTA case study (30') Marcin Drozdowski, Automation CoE Manager @DOVISTA Pawel Kamiński, RPA developer @DOVISTA Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner 09:40 From bottlenecks to breakthroughs: Citizen Development in action (25') Pawel Poplawski, Director, Improvement and Automation @McCormick & Company Michał Cieślak, Senior Manager, Automation Programs @McCormick & Company 10:05 Next-level bots: API integration in UiPath Studio (30') Mikolaj Zielinski, UiPath MVP, Senior Solutions Engineer @Proservartner 10:35 ☕ Coffee Break (15') 10:50 Document Understanding with my RPA Companion (45') Ewa Gruszka, Enterprise Sales Specialist, AI & ML @UiPath 11:35 Power up your Robots: GenAI and GPT in REFramework (45') Krzysztof Karaszewski, Global RPA Product Manager 12:20 🍕 Lunch Break (1hr) 13:20 From Concept to Quality: UiPath Test Suite for AI-powered Knowledge Bots (30') Kamil Miśko, UiPath MVP, Senior RPA Developer @Zurich Insurance 13:50 Communications Mining - focus on AI capabilities (30') Thomasz Wierzbicki, Business Analyst @Office Samurai 14:20 Polish MVP panel: Insights on MVP award achievements and career profiling

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Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr "A Systematic Survey of Prompt Engineering on Vision-Language Foundation Models" arXiv2023 https://arxiv.org/abs/2307.12980

Is your patent a vanity piece of paper for your office wall? Or is it a reliable, defendable, assertable, property right? The difference is often quality. Is your patent simply a transactional cost and a large pile of legal bills for your startup? Or is it a leverageable asset worthy of attracting precious investment dollars, worth its cost in multiples of valuation? The difference is often quality. Is your patent application only good enough to get through the examination process? Or has it been crafted to stand the tests of time and varied audiences if you later need to assert that document against an infringer, find yourself litigating with it in an Article 3 Court at the hands of a judge and jury, God forbid, end up having to defend its validity at the PTAB, or even needing to use it to block pirated imports at the International Trade Commission? The difference is often quality. Quality will be our focus for a good chunk of the remainder of this season. What goes into a quality patent, and where possible, how do you get it without breaking the bank? ** Episode Overview ** In this first episode of our quality series, Kristen Hansen and the panel discuss: ⦿ What do we mean when we say patent quality? ⦿ Why is patent quality important? ⦿ How to balance quality and budget ⦿ The importance of searching, continuations, and draftsperson domain expertise ⦿ Very practical tips, tricks, examples, and Kristen’s Musts for drafting quality applications https://www.aurorapatents.com/patently-strategic-podcast.html

In the modern digital era, social media platforms have become integral to our daily lives. These platforms, including Facebook, Instagram, WhatsApp, and Snapchat, offer countless ways to connect, share, and communicate.

The presentation showcases the diverse real-world applications of Fused Deposition Modeling (FDM) across multiple industries: 1. **Manufacturing**: FDM is utilized in manufacturing for rapid prototyping, creating custom tools and fixtures, and producing functional end-use parts. Companies leverage its cost-effectiveness and flexibility to streamline production processes. 2. **Medical**: In the medical field, FDM is used to create patient-specific anatomical models, surgical guides, and prosthetics. Its ability to produce precise and biocompatible parts supports advancements in personalized healthcare solutions. 3. **Education**: FDM plays a crucial role in education by enabling students to learn about design and engineering through hands-on 3D printing projects. It promotes innovation and practical skill development in STEM disciplines. 4. **Science**: Researchers use FDM to prototype equipment for scientific experiments, build custom laboratory tools, and create models for visualization and testing purposes. It facilitates rapid iteration and customization in scientific endeavors. 5. **Automotive**: Automotive manufacturers employ FDM for prototyping vehicle components, tooling for assembly lines, and customized parts. It speeds up the design validation process and enhances efficiency in automotive engineering. 6. **Consumer Electronics**: FDM is utilized in consumer electronics for designing and prototyping product enclosures, casings, and internal components. It enables rapid iteration and customization to meet evolving consumer demands. 7. **Robotics**: Robotics engineers leverage FDM to prototype robot parts, create lightweight and durable components, and customize robot designs for specific applications. It supports innovation and optimization in robotic systems. 8. **Aerospace**: In aerospace, FDM is used to manufacture lightweight parts, complex geometries, and prototypes of aircraft components. It contributes to cost reduction, faster production cycles, and weight savings in aerospace engineering. 9. **Architecture**: Architects utilize FDM for creating detailed architectural models, prototypes of building components, and intricate designs. It aids in visualizing concepts, testing structural integrity, and communicating design ideas effectively. Each industry example demonstrates how FDM enhances innovation, accelerates product development, and addresses specific challenges through advanced manufacturing capabilities.

How do we build an IoT product, and make it profitable? Talk from the IoT meetup in March 2024. https://www.meetup.com/iot-sweden/events/299487375/

Widya Salim and Victor Ma will outline the causal impact analysis, framework, and key learnings used to quantify the impact of reducing Twitter's network latency.

Sustainability requires ingenuity and stewardship. Did you know Pigging Solutions pigging systems help you achieve your sustainable manufacturing goals AND provide rapid return on investment. How? Our systems recover over 99% of product in transfer piping. Recovering trapped product from transfer lines that would otherwise become flush-waste, means you can increase batch yields and eliminate flush waste. From raw materials to finished product, if you can pump it, we can pig it.

YOUR RELIABLE WEB DESIGN & DEVELOPMENT TEAM — FOR LASTING SUCCESS WPRiders is a web development company specialized in WordPress and WooCommerce websites and plugins for customers around the world. The company is headquartered in Bucharest, Romania, but our team members are located all over the world. Our customers are primarily from the US and Western Europe, but we have clients from Australia, Canada and other areas as well. Some facts about WPRiders and why we are one of the best firms around: More than 700 five-star reviews! You can check them here. 1500 WordPress projects delivered. We respond 80% faster than other firms! Data provided by Freshdesk. We’ve been in business since 2015. We are located in 7 countries and have 22 team members. With so many projects delivered, our team knows what works and what doesn’t when it comes to WordPress and WooCommerce. Our team members are: - highly experienced developers (employees & contractors with 5 -10+ years of experience), - great designers with an eye for UX/UI with 10+ years of experience - project managers with development background who speak both tech and non-tech - QA specialists - Conversion Rate Optimisation - CRO experts They are all working together to provide you with the best possible service. We are passionate about WordPress, and we love creating custom solutions that help our clients achieve their goals. At WPRiders, we are committed to building long-term relationships with our clients. We believe in accountability, in doing the right thing, as well as in transparency and open communication. You can read more about WPRiders on the About us page.

Slide of the tutorial entitled "Paradigm Shifts in User Modeling: A Journey from Historical Foundations to Emerging Trends" held at UMAP'24: 32nd ACM Conference on User Modeling, Adaptation and Personalization (July 1, 2024 | Cagliari, Italy)

Manual Method of Product Research | Helium10 | MBS RETRIEVER

Everything that I found interesting about machines behaving intelligently during June 2024

Password Rotation in 2024 is still Relevant