Declarative JavaScript concepts and implemetationOm Shankar
Declarative style JavaScripting and the curious case of underscore.js.
- Presented at BangaloreJS Eleventh meetup: http://bangalorejs.org/eleventh.html
This document describes Mario, an asynchronous library that allows synchronous code to be executed asynchronously. It provides thread-safe asynchronous processing of messages by inheriting from a Handler class and implementing a processMsg function. The library uses background threads to consume messages put into a Mario instance, allowing the code to quickly return while the work continues in the background. It supports different storage engines like memory and files.
Virtualize and automate your development environment for fun and profitAndreas Heim
The document discusses using Vagrant to virtualize and automate development environments. Vagrant allows developers to create identical virtual environments that match production. This ensures environments are the same across operating systems and developers. Vagrant uses automation tools like Chef and Puppet to configure environments. It addresses challenges like different dependency versions and allows quick resets. It advocates treating environments as code to make them documented, versioned and easily shared.
This document discusses improvements to the framework and speed and memory usage. It proposes better utilizing PostgreSQL functionalities like fillfactor and tablespace definitions. It also suggests splitting some multi-model tables into their own schemas. Improvements to the ORM are outlined, including better documentation, datetime fields instead of strings, and delegating logic to fields. Other proposed changes include server logs configurable from the client, high-level functions, better crash reports, and handling multiple inheritance models. The document also discusses improving sequences, cron jobs, and cron logging.
W3C HTML5 KIG-How to write low garbage real-time javascriptChanghwan Yi
This document summarizes techniques for writing low-garbage real-time JavaScript code. It discusses how to avoid object allocation using syntax like {} and [] instead of the new keyword. It also recommends reusing objects by wiping their properties instead of creating new ones. Functions should be created at startup instead of during runtime. Vector objects should be returned as individual values instead of vector objects. While avoiding garbage entirely is difficult, these techniques can help craft responsive real-time JavaScript with minimal garbage collector overhead.
Webassembly is a new low-level compilation target that runs sandboxed in web browsers and other environments. It uses a stack-based virtual machine and can be compiled from various programming languages. Modules contain data like global variables and linear memory as well as functions with parameters, return values, local variables, and instructions. Common data types include integers and floats, and calculations can be performed on the stack. Conditionals, functions, and other control flows are also supported.
Rubinius and RubySpec talk at the contributing to Open Source track at Red Dirt Ruby Conference 2011. These are the full slides. The talk used an abbreviated selection of these slides.
This document discusses techniques for debugging iOS UIs without recompiling such as using breakpoints, the p command to print values, custom breakpoints, and LLDB commands like caflush, thread return, and wivar. It recommends the Chisel library and getting help within LLDB for additional debugging options.
Declarative JavaScript concepts and implemetationOm Shankar
Declarative style JavaScripting and the curious case of underscore.js.
- Presented at BangaloreJS Eleventh meetup: http://bangalorejs.org/eleventh.html
This document describes Mario, an asynchronous library that allows synchronous code to be executed asynchronously. It provides thread-safe asynchronous processing of messages by inheriting from a Handler class and implementing a processMsg function. The library uses background threads to consume messages put into a Mario instance, allowing the code to quickly return while the work continues in the background. It supports different storage engines like memory and files.
Virtualize and automate your development environment for fun and profitAndreas Heim
The document discusses using Vagrant to virtualize and automate development environments. Vagrant allows developers to create identical virtual environments that match production. This ensures environments are the same across operating systems and developers. Vagrant uses automation tools like Chef and Puppet to configure environments. It addresses challenges like different dependency versions and allows quick resets. It advocates treating environments as code to make them documented, versioned and easily shared.
This document discusses improvements to the framework and speed and memory usage. It proposes better utilizing PostgreSQL functionalities like fillfactor and tablespace definitions. It also suggests splitting some multi-model tables into their own schemas. Improvements to the ORM are outlined, including better documentation, datetime fields instead of strings, and delegating logic to fields. Other proposed changes include server logs configurable from the client, high-level functions, better crash reports, and handling multiple inheritance models. The document also discusses improving sequences, cron jobs, and cron logging.
W3C HTML5 KIG-How to write low garbage real-time javascriptChanghwan Yi
This document summarizes techniques for writing low-garbage real-time JavaScript code. It discusses how to avoid object allocation using syntax like {} and [] instead of the new keyword. It also recommends reusing objects by wiping their properties instead of creating new ones. Functions should be created at startup instead of during runtime. Vector objects should be returned as individual values instead of vector objects. While avoiding garbage entirely is difficult, these techniques can help craft responsive real-time JavaScript with minimal garbage collector overhead.
Webassembly is a new low-level compilation target that runs sandboxed in web browsers and other environments. It uses a stack-based virtual machine and can be compiled from various programming languages. Modules contain data like global variables and linear memory as well as functions with parameters, return values, local variables, and instructions. Common data types include integers and floats, and calculations can be performed on the stack. Conditionals, functions, and other control flows are also supported.
Rubinius and RubySpec talk at the contributing to Open Source track at Red Dirt Ruby Conference 2011. These are the full slides. The talk used an abbreviated selection of these slides.
This document discusses techniques for debugging iOS UIs without recompiling such as using breakpoints, the p command to print values, custom breakpoints, and LLDB commands like caflush, thread return, and wivar. It recommends the Chisel library and getting help within LLDB for additional debugging options.
This document outlines a 3-day Ruby on Rails bootcamp that will introduce participants to the Rails toolset, teach them how to build full Rails applications, and provide resources for continued learning after the bootcamp. Each day covers different Rails topics like models, views, and controllers, and includes explanations, demonstrations, and hands-on coding exercises. The goal is for participants to learn the basics of Rails and common patterns and practices so they can experiment with the framework on their own.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Jenkins Job Builder is a tool to represent Jenkins jobs in the YAML format. This presentation is about using it to manage a large amount of build and test jobs for executing tests which require complex environment
The document discusses using XMPP and Rails for real-time web applications. It describes how XMPP allows for server push through BOSH and components, and can be used to build features like multi-party negotiations. Examples are given of using an XMPP component to handle events from a Rails application and push updates to clients in real-time. Pros of the approach include powerful XMPP toolkits, built-in server push, and scalability, while some administrative work is required.
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks
Description
Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
This document provides an overview of OpenERP/Odoo, an open source suite of business applications. It describes OpenERP's modular, Python-based architecture and Rapid Application Development (RAD) framework. The document then discusses how to build custom modules in OpenERP, including the structure of modules and how to define business objects and fields using the integrated Object-Relational Mapping (ORM) service.
Ruby is a dynamic, reflective, general-purpose programming language while Rails is a model-view-controller (MVC) web application framework built on Ruby. Rails was designed to optimize programmer happiness and promote conventions over configurations. It provides full-stack capabilities including object-relational mapping and tools for database management, frontend interface development, and testing. Rails aims to make web development faster and easier.
Deep AutoViML For Tensorflow Models and MLOps WorkflowsBill Liu
deep_autoviml is a powerful new deep learning library with a very simple design goal: Make it as easy as possible for novices and experts alike to experiment with and build tensorflow.keras preprocessing pipelines and models in as few lines of code as possible.
deep_autoviml will enable data scientists, ML engineers and data engineers to fast prototype tensorflow models and data pipelines for MLOps workflows using the latest TF 2.4+ and keras preprocessing layers. You can now upload your saved model to any Cloud provider and make predictions out of the box since all the data preprocessing layers are attached to the model itself!
In this webinar, we will discuss the problems that deep_AutoViML can solve, its architecture design and demo how to build powerful TF.Keras models on structured data, NLP and Image data domains.
https://www.aicamp.ai/event/eventdetails/W2021080918
The document discusses OpenObject, an open-source Rapid Application Development (RAD) framework written in Python. OpenObject features include an Object-Relationship Mapping (ORM) layer, template-based Model-View-Controller (MVC) interfaces, and report generation. The document also provides instructions on installing OpenObject, describes its typical module structure, and explains how to define business objects using the ORM.
This document discusses the evolution of bootstrapping Rails applications from the initial Rails generation to modular templates called App Lego. It outlines how initial Rails apps took a lot of time to set up, then skeletons and templates sped things up but lacked modularity. App Lego used templates in a modular way, allowing developers to choose which features or modules to include. Each module is its own template that can be included. The document acknowledges some downsides like long command lines but outlines future plans to improve the user experience.
Why is dev ops for machine learning so differentRyan Dawson
DevOps instincts tend to be shaped by what has worked well before. Instincts derived from mainstream software development projects get challenged when we turn to enabling machine learning projects. The key reasons are that the development/delivery workflow is different and the kind of software artefacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.
This document provides an overview of the OpenERP/Odoo rapid application development (RAD) framework. It discusses how OpenERP uses a modular, Python-based architecture with integrated object-relational mapping (ORM) to allow developers to quickly build business applications. The document also provides examples of defining business objects with the ORM and constructing an OpenERP module to contain application features.
The document describes a 3-day bootcamp for learning Ruby on Rails. Day 1 will cover fundamental Rails tools and components. Students will learn to set up their development environment, use version control with Git, and explore core Rails structures like models, views and controllers. Day 2 focuses on additional Rails techniques like scaffolding, internationalization and testing. Day 3 presents more advanced topics and full application examples to reinforce skills learned. The bootcamp aims to give students a working knowledge of Rails and resources for continuing their learning after the course.
PMML (Predictive Model Markup Language) provides a standard way to represent data mining models so that these can be shared between different statistical applications. Not only PMML can represent a wide range of statistical techniques, but it can also be used to represent the data transformations necessary to transform raw data into meaningful feature detectors. In this way, PMML offers a standard to represent data manipulation and modeling in a single and concise way.
Code That Writes Code : Automatic Programming for NHibernateDeepak Sahu
This white paper explores the power of automatic programming and its application on NHibernate Technology, allowing human-programmers to write their code on a higher level of abstraction ensuring homogenous and Error-Free code.
Tracking down redundant code and implementing a generic algorithm that generates such code is the Key factor in Automatic-Programming.
It takes sole discretion and independent judgement of the Developer to trace similar code-patterns in their application and making all efforts in reducing the overall Project-Development-Time by automating such process.
Stefan Richter - Writing simple, readable and robust code: Examples in Java, ...AboutYouGmbH
Stefan Richter gave a presentation on writing simple, readable, and robust code using examples in Java, Clojure, and Go. He discussed his programming experience and showed how Martin Fowler used Java to parse a fixed-length file format into objects. Richter then demonstrated how the same task could be accomplished more concisely in Common Lisp and Clojure using macros to define domain-specific languages. He argued that macros are a powerful feature of Lisp-like languages.
RestfulX “The RESTful Way to develop Adobe Flex and AIR applications”elliando dias
This document introduces RestfulX, an open-source framework that allows developers to build Adobe Flex and AIR applications that interact with Rails backends via RESTful web services. It discusses how RestfulX streamlines the development of Flex and AIR applications on Rails by automating common CRUD operations. The document provides a demo of generating a sample Pomodo task management application using RestfulX and connecting a Flex front-end to the Rails backend to perform CRUD operations both online and offline.
Apache SystemML Architecture by Niketan PanesarArvind Surve
This deck will present high level Apache SystemML design and architecture containing language, compiler and runtime modules. It will describe how compilation chain gets generated and variable analysis done. It will show HOPs and runtime plan for sample use case. It will show how to get statistics, and some diagnostic tools can be used.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
This document outlines a 3-day Ruby on Rails bootcamp that will introduce participants to the Rails toolset, teach them how to build full Rails applications, and provide resources for continued learning after the bootcamp. Each day covers different Rails topics like models, views, and controllers, and includes explanations, demonstrations, and hands-on coding exercises. The goal is for participants to learn the basics of Rails and common patterns and practices so they can experiment with the framework on their own.
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Jenkins Job Builder is a tool to represent Jenkins jobs in the YAML format. This presentation is about using it to manage a large amount of build and test jobs for executing tests which require complex environment
The document discusses using XMPP and Rails for real-time web applications. It describes how XMPP allows for server push through BOSH and components, and can be used to build features like multi-party negotiations. Examples are given of using an XMPP component to handle events from a Rails application and push updates to clients in real-time. Pros of the approach include powerful XMPP toolkits, built-in server push, and scalability, while some administrative work is required.
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks
Description
Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
This document provides an overview of OpenERP/Odoo, an open source suite of business applications. It describes OpenERP's modular, Python-based architecture and Rapid Application Development (RAD) framework. The document then discusses how to build custom modules in OpenERP, including the structure of modules and how to define business objects and fields using the integrated Object-Relational Mapping (ORM) service.
Ruby is a dynamic, reflective, general-purpose programming language while Rails is a model-view-controller (MVC) web application framework built on Ruby. Rails was designed to optimize programmer happiness and promote conventions over configurations. It provides full-stack capabilities including object-relational mapping and tools for database management, frontend interface development, and testing. Rails aims to make web development faster and easier.
Deep AutoViML For Tensorflow Models and MLOps WorkflowsBill Liu
deep_autoviml is a powerful new deep learning library with a very simple design goal: Make it as easy as possible for novices and experts alike to experiment with and build tensorflow.keras preprocessing pipelines and models in as few lines of code as possible.
deep_autoviml will enable data scientists, ML engineers and data engineers to fast prototype tensorflow models and data pipelines for MLOps workflows using the latest TF 2.4+ and keras preprocessing layers. You can now upload your saved model to any Cloud provider and make predictions out of the box since all the data preprocessing layers are attached to the model itself!
In this webinar, we will discuss the problems that deep_AutoViML can solve, its architecture design and demo how to build powerful TF.Keras models on structured data, NLP and Image data domains.
https://www.aicamp.ai/event/eventdetails/W2021080918
The document discusses OpenObject, an open-source Rapid Application Development (RAD) framework written in Python. OpenObject features include an Object-Relationship Mapping (ORM) layer, template-based Model-View-Controller (MVC) interfaces, and report generation. The document also provides instructions on installing OpenObject, describes its typical module structure, and explains how to define business objects using the ORM.
This document discusses the evolution of bootstrapping Rails applications from the initial Rails generation to modular templates called App Lego. It outlines how initial Rails apps took a lot of time to set up, then skeletons and templates sped things up but lacked modularity. App Lego used templates in a modular way, allowing developers to choose which features or modules to include. Each module is its own template that can be included. The document acknowledges some downsides like long command lines but outlines future plans to improve the user experience.
Why is dev ops for machine learning so differentRyan Dawson
DevOps instincts tend to be shaped by what has worked well before. Instincts derived from mainstream software development projects get challenged when we turn to enabling machine learning projects. The key reasons are that the development/delivery workflow is different and the kind of software artefacts involved are different. We will explore the differences and look at emerging open source projects in order to appreciate why the DevOps for machine learning space is growing and the needs that it addresses.
This document provides an overview of the OpenERP/Odoo rapid application development (RAD) framework. It discusses how OpenERP uses a modular, Python-based architecture with integrated object-relational mapping (ORM) to allow developers to quickly build business applications. The document also provides examples of defining business objects with the ORM and constructing an OpenERP module to contain application features.
The document describes a 3-day bootcamp for learning Ruby on Rails. Day 1 will cover fundamental Rails tools and components. Students will learn to set up their development environment, use version control with Git, and explore core Rails structures like models, views and controllers. Day 2 focuses on additional Rails techniques like scaffolding, internationalization and testing. Day 3 presents more advanced topics and full application examples to reinforce skills learned. The bootcamp aims to give students a working knowledge of Rails and resources for continuing their learning after the course.
PMML (Predictive Model Markup Language) provides a standard way to represent data mining models so that these can be shared between different statistical applications. Not only PMML can represent a wide range of statistical techniques, but it can also be used to represent the data transformations necessary to transform raw data into meaningful feature detectors. In this way, PMML offers a standard to represent data manipulation and modeling in a single and concise way.
Code That Writes Code : Automatic Programming for NHibernateDeepak Sahu
This white paper explores the power of automatic programming and its application on NHibernate Technology, allowing human-programmers to write their code on a higher level of abstraction ensuring homogenous and Error-Free code.
Tracking down redundant code and implementing a generic algorithm that generates such code is the Key factor in Automatic-Programming.
It takes sole discretion and independent judgement of the Developer to trace similar code-patterns in their application and making all efforts in reducing the overall Project-Development-Time by automating such process.
Stefan Richter - Writing simple, readable and robust code: Examples in Java, ...AboutYouGmbH
Stefan Richter gave a presentation on writing simple, readable, and robust code using examples in Java, Clojure, and Go. He discussed his programming experience and showed how Martin Fowler used Java to parse a fixed-length file format into objects. Richter then demonstrated how the same task could be accomplished more concisely in Common Lisp and Clojure using macros to define domain-specific languages. He argued that macros are a powerful feature of Lisp-like languages.
RestfulX “The RESTful Way to develop Adobe Flex and AIR applications”elliando dias
This document introduces RestfulX, an open-source framework that allows developers to build Adobe Flex and AIR applications that interact with Rails backends via RESTful web services. It discusses how RestfulX streamlines the development of Flex and AIR applications on Rails by automating common CRUD operations. The document provides a demo of generating a sample Pomodo task management application using RestfulX and connecting a Flex front-end to the Rails backend to perform CRUD operations both online and offline.
Apache SystemML Architecture by Niketan PanesarArvind Surve
This deck will present high level Apache SystemML design and architecture containing language, compiler and runtime modules. It will describe how compilation chain gets generated and variable analysis done. It will show HOPs and runtime plan for sample use case. It will show how to get statistics, and some diagnostic tools can be used.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
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.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
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.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
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.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
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.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen