This document discusses machine learning for startups. It outlines standard machine learning processes like defining problems, preparing data, choosing algorithms, and deploying models. It also discusses some startup projects using machine learning for smart cities, human behavior analysis, food analysis, and detecting social media manipulation. The document cautions against proprietary claims and focuses on using open source software and avoiding technical debt. It emphasizes that machine learning should be only one component of products and concludes by advising exploring diverse applications and avoiding hype.
Swift & Fika talk discussing an overview of machine learning tools for Apple platforms, covering short examples using Vision, Create ML, Turi Create, & Core ML.
Learn to Build an App to Find Similar Images using Deep Learning- Piotr TeterwakPyData
This document discusses using deep learning and deep features to build an app that finds similar images. It begins with an overview of deep learning and how neural networks can learn complex patterns in data. The document then discusses how pre-trained neural networks can be used as feature extractors for other domains through transfer learning. This reduces data and tuning requirements compared to training new deep learning models. The rest of the document focuses on building an image similarity service using these techniques, including training a model with GraphLab Create and deploying it as a web service with Dato Predictive Services.
This document discusses upgrading modules to Drupal 8. It covers changes like using plugins instead of hooks, config entities to manage configuration, and the new cache and routing systems. The presenters pledge two of their modules will support Drupal 8 on release day. They demonstrate upgrading a module, highlighting differences in forms, blocks, and testing between the versions. Attendees are encouraged to join #D8CX and help others with upgrades.
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...Databricks
A long time ago, there was Caffe and Theano, then came Torch and CNTK and Tensorflow, Keras and MXNet and Pytorch and Caffe2….a sea of Deep learning tools but none for Spark developers to dip into. Finally, there was BigDL, a deep learning library for Apache Spark. While BigDL is integrated into Spark and extends its capabilities to address the challenges of Big Data developers, will a library alone be enough to simplify and accelerate the deployment of ML/DL workloads on production clusters? From high level pipeline API support to feature transformers to pre-defined models and reference use cases, a rich repository of easy to use tools are now available with the ‘Analytics Zoo’. We’ll unpack the production challenges and opportunities with ML/DL on Spark and what the Zoo can do
In our last paper we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances
This document discusses machine learning for startups. It outlines standard machine learning processes like defining problems, preparing data, choosing algorithms, and deploying models. It also discusses some startup projects using machine learning for smart cities, human behavior analysis, food analysis, and detecting social media manipulation. The document cautions against proprietary claims and focuses on using open source software and avoiding technical debt. It emphasizes that machine learning should be only one component of products and concludes by advising exploring diverse applications and avoiding hype.
Swift & Fika talk discussing an overview of machine learning tools for Apple platforms, covering short examples using Vision, Create ML, Turi Create, & Core ML.
Learn to Build an App to Find Similar Images using Deep Learning- Piotr TeterwakPyData
This document discusses using deep learning and deep features to build an app that finds similar images. It begins with an overview of deep learning and how neural networks can learn complex patterns in data. The document then discusses how pre-trained neural networks can be used as feature extractors for other domains through transfer learning. This reduces data and tuning requirements compared to training new deep learning models. The rest of the document focuses on building an image similarity service using these techniques, including training a model with GraphLab Create and deploying it as a web service with Dato Predictive Services.
This document discusses upgrading modules to Drupal 8. It covers changes like using plugins instead of hooks, config entities to manage configuration, and the new cache and routing systems. The presenters pledge two of their modules will support Drupal 8 on release day. They demonstrate upgrading a module, highlighting differences in forms, blocks, and testing between the versions. Attendees are encouraged to join #D8CX and help others with upgrades.
Analytics Zoo: Building Analytics and AI Pipeline for Apache Spark and BigDL ...Databricks
A long time ago, there was Caffe and Theano, then came Torch and CNTK and Tensorflow, Keras and MXNet and Pytorch and Caffe2….a sea of Deep learning tools but none for Spark developers to dip into. Finally, there was BigDL, a deep learning library for Apache Spark. While BigDL is integrated into Spark and extends its capabilities to address the challenges of Big Data developers, will a library alone be enough to simplify and accelerate the deployment of ML/DL workloads on production clusters? From high level pipeline API support to feature transformers to pre-defined models and reference use cases, a rich repository of easy to use tools are now available with the ‘Analytics Zoo’. We’ll unpack the production challenges and opportunities with ML/DL on Spark and what the Zoo can do
In our last paper we built the Load statements and encryption to enable our building project to learn about Secant Theory in both Digital and advancing to Ark Mode. We have begun to devise the New Schema to move into Ark Mode and the crossover requirements. We demonstrate what the move from one secant wheel accomplished for Product Media in metric terms, and in V2 will examine Secant Multi-Wheel Applications and in V3 learn to drive Instances
This document provides an overview of deep learning and its applications. It discusses how deep learning can be used for image classification and how neural networks learn hierarchical representations from data. The document highlights some of the challenges of deep learning, such as the large amounts of data and computation required. It also covers how deep learning models can be deployed in production using services like Amazon Web Services to ensure low latency, high availability, and continuous learning.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Thesis Defense (Gwendal DANIEL) - Nov 2017Gwendal Daniel
This document summarizes Gwendal Daniel's PhD thesis on efficient persistence, querying, and transformation of large models. It presents four main contributions:
1. NeoEMF, a scalable model persistence framework that allows storing models across multiple databases for improved performance and memory usage.
2. PrefetchML, a model prefetching and caching component that uses declarative rules to efficiently load related model elements from the database.
3. Mogwaï, an approach to generate efficient graph database queries from OCL expressions to compute model queries without overhead from modeling frameworks.
4. Gremlin-ATL, an extension of Mogwaï to generate Gremlin traversals from ATL
Aspect-based sentiment analysis is a text analysis technique that breaks down text into aspects (attributes or components of a product or service), and then scores the sentiment level (positive, negative or neutral) of each aspect. In this talk we'll walk through a production pipeline for training large Aspect Based Sentiment Analysis model in python with the Intel NLP Architect package based on the following open sourced code https://github.com/microsoft/nlp-recipes/tree/master/examples/sentiment_analysis/absa
This document describes a scientific simulation platform called GPUDigitalLab created by Oleg Gubanov using Microsoft DirectCompute. The platform uses a computational kernel to parallelize simulations across GPUs. It splits complex tasks into independent computational agents and uses a framework to control agent behavior and update results. Simulations can program 3D animations. The platform is intended for use in scientific modeling, simulations, and data analysis applications across various domains.
By testing a modelling approach that utilizes minimal rules and constraints against an explicit exhaustive mixed integer programming method the research presents an alternative approach. Trade offs of time, effort, compliance, configuration and usability are considered and analyzed. By utilizing approaches from Hegde et al. 2015, Ljubić et al. 2006, and Teitz et al. 1963 much of the traditionally manual process can be automated. Further we demonstrate that a hybrid approach can enhance the productivity and usability of network planning software for telecommunications.
Machine and Deep Learning Application.
Applying big data learning techniques for a malware classification problem.
Code:
https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0
Acknowledgments:
Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS).
Data Science Challenge presentation given to the CinBITools Meetup GroupDoug Needham
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires social network analysis to recommend users to follow on a social media platform based on click data. The document discusses the approaches, tools, and algorithms used to solve each problem at scale using Apache Spark and Hadoop technologies.
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires analyzing a social network graph to recommend users to follow. The document discusses the approaches, tools, and results for each problem.
The primary focus of this presentation is approaching the migration of a large, legacy data store into a new schema built with Django. Includes discussion of how to structure a migration script so that it will run efficiently and scale. Learn how to recognize and evaluate trouble spots.
Also discusses some general tips and tricks for working with data and establishing a productive workflow.
The document discusses strategies for migrating large amounts of legacy data from an old database into a new Django application. Some key points:
- Migrating data in batches and minimizing database queries per row processed can improve performance for large datasets.
- Tools like SQLAlchemy and Maatkit can help optimize the migration process.
- It's important to profile queries, enable logging/debugging, and design migrations that can resume/restart after failures or pause for maintenance.
- Preserving some legacy metadata like IDs on the new models allows mapping data between the systems. Declarative and modular code helps scale the migration tasks.
Large-scale Recommendation Systems on Just a PCAapo Kyrölä
Aapo Kyrölä presented on running large-scale recommender systems on a single PC using GraphChi, a framework for graph computation on disk. GraphChi uses parallel sliding windows to efficiently process graphs that do not fit in memory by only loading subsets of the graph into RAM at a time. Kyrölä demonstrated training recommender models like ALS matrix factorization and item-based collaborative filtering on large graphs like Twitter using GraphChi on a single laptop. He concluded that very large recommender algorithms can now be run on a single machine and that GraphChi and similar frameworks hide the low-level optimizations needed for efficient single machine graph computation.
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...Rodney Joyce
Number 2 in the Data Science for Dummies series - We'll predict Titanic survival with Databricks, python and MLSpark.
These are the slides only (excuse the Powerpoint animation issues) - check out the actual tech talk on YouTube: https://rodneyjoyce.home.blog/2019/05/03/data-science-for-dummies-machine-learning-with-databricks-python-sparkml-tech-talk-1-of-7/)
If you have not used Databricks before check out the first talk - Databricks for Dummies.
Here's the rest of the series: https://rodneyjoyce.home.blog/tag/data-science-for-dummies/
1) Data Science overview with Databricks
2) Titanic survival prediction with Azure Machine Learning Studio + Kaggle
3) Data Engineering with Titanic dataset + Databricks + Python
4) Titanic with Databricks + Spark ML
5) Titanic with Databricks + Azure Machine Learning Service
6) Titanic with Databricks + MLS + AutoML
7) Titanic with Databricks + MLFlow
8) Titanic with .NET Core + ML.NET
9) Deployment, DevOps/MLOps and Productionisation
Identifying Auxiliary Web Images Using Combinations of AnalysesTewson Seeoun
This document summarizes a study that aimed to identify auxiliary web images using a combination of analyses. The methodology involved analyzing features of web pages like image position, dimensions and number of images using the DOM, and analyzing image-level features like number of colors and faces using tools like PIL and OpenCV. A SVM classifier was trained on these features from a sample of pages to recognize auxiliary images with an average accuracy of 93.17% after optimization. While the current approach performed well, future work could involve context analysis, weighted features and adaptive page analysis to improve the system.
Learning Predictive Modeling with TSA and KaggleYvonne K. Matos
This document summarizes Yvonne Matos' presentation on learning predictive modeling by participating in Kaggle challenges using TSA passenger screening data.
The key points are:
1) Matos started with a small subset of 120 images from one body zone to build initial neural network models and address challenges of large data sizes and compute requirements.
2) Through iterative tuning, her best model achieved good performance identifying non-threat images but had a high false negative rate for threats.
3) Her next steps were to reduce the false negative rate, run models on Google Cloud to handle full data sizes, and prepare the best model for real-world use.
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...inside-BigData.com
In this deck from FOSDEM 2020, Frank McQuillan from Pivotal presents: Efficient Model Selection for Deep Neural Networks on Massively Parallel Processing Databases.
"In this session we will present an efficient way to train many deep learning model configurations at the same time with Greenplum, a free and open source massively parallel database based on PostgreSQL. The implementation involves distributing data to the workers that have GPUs available and hopping model state between those workers, without sacrificing reproducibility or accuracy. Then we apply optimization algorithms to generate and prune the set of model configurations to try.
Deep neural networks are revolutionizing many machine learning applications, but hundreds of trials may be needed to generate a good model architecture and associated hyperparameters. This is the challenge of model selection. It is time consuming and expensive, especially if you are only training one model at a time.
Massively parallel processing databases can have hundreds of workers, so can you use this parallel compute architecture to address the challenge of model selection for deep nets, in order to make it faster and cheaper?
It’s possible!
We will demonstrate results from this project using a version of Hyperband, which is a well known hyperparameter optimization algorithm, and the deep learning frameworks Keras and TensorFlow, all running on Greenplum database using Apache MADlib. Other topics will include architecture, scalability results and bright opportunities for the future."
Watch the video: https://wp.me/p3RLHQ-lsQ
Learn more: https://fosdem.org/2020/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Web Performance Part 4 "Client-side performance"Binary Studio
The presentation is devoted to client side performance of a web app. All 4 presentations will help you reduce latency, enrich optimization of javascript code, discover tricky parts when working with API browser, see best practices of networking and learn lots of other important and interesting things. Enjoy! =)
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
This document provides an overview of deep learning and its applications. It discusses how deep learning can be used for image classification and how neural networks learn hierarchical representations from data. The document highlights some of the challenges of deep learning, such as the large amounts of data and computation required. It also covers how deep learning models can be deployed in production using services like Amazon Web Services to ensure low latency, high availability, and continuous learning.
This slide deck gives an overview of the Azure Machine Learning Service. It highlights benefits of Azure Machine Learning Workspace, Automated Machine Learning and integration Notebook scripts
Thesis Defense (Gwendal DANIEL) - Nov 2017Gwendal Daniel
This document summarizes Gwendal Daniel's PhD thesis on efficient persistence, querying, and transformation of large models. It presents four main contributions:
1. NeoEMF, a scalable model persistence framework that allows storing models across multiple databases for improved performance and memory usage.
2. PrefetchML, a model prefetching and caching component that uses declarative rules to efficiently load related model elements from the database.
3. Mogwaï, an approach to generate efficient graph database queries from OCL expressions to compute model queries without overhead from modeling frameworks.
4. Gremlin-ATL, an extension of Mogwaï to generate Gremlin traversals from ATL
Aspect-based sentiment analysis is a text analysis technique that breaks down text into aspects (attributes or components of a product or service), and then scores the sentiment level (positive, negative or neutral) of each aspect. In this talk we'll walk through a production pipeline for training large Aspect Based Sentiment Analysis model in python with the Intel NLP Architect package based on the following open sourced code https://github.com/microsoft/nlp-recipes/tree/master/examples/sentiment_analysis/absa
This document describes a scientific simulation platform called GPUDigitalLab created by Oleg Gubanov using Microsoft DirectCompute. The platform uses a computational kernel to parallelize simulations across GPUs. It splits complex tasks into independent computational agents and uses a framework to control agent behavior and update results. Simulations can program 3D animations. The platform is intended for use in scientific modeling, simulations, and data analysis applications across various domains.
By testing a modelling approach that utilizes minimal rules and constraints against an explicit exhaustive mixed integer programming method the research presents an alternative approach. Trade offs of time, effort, compliance, configuration and usability are considered and analyzed. By utilizing approaches from Hegde et al. 2015, Ljubić et al. 2006, and Teitz et al. 1963 much of the traditionally manual process can be automated. Further we demonstrate that a hybrid approach can enhance the productivity and usability of network planning software for telecommunications.
Machine and Deep Learning Application.
Applying big data learning techniques for a malware classification problem.
Code:
https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0
Acknowledgments:
Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS).
Data Science Challenge presentation given to the CinBITools Meetup GroupDoug Needham
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires social network analysis to recommend users to follow on a social media platform based on click data. The document discusses the approaches, tools, and algorithms used to solve each problem at scale using Apache Spark and Hadoop technologies.
The document describes the Cloudera Data Science Challenge, which involves solving three data science problems using large datasets. For the first problem, Smartfly, the goal is to predict flight delays using historical flight data and machine learning algorithms like logistic regression and SVM. The second problem, Almost Famous, involves statistical analysis of web log data and filtering for spam. The third problem, Winklr, requires analyzing a social network graph to recommend users to follow. The document discusses the approaches, tools, and results for each problem.
The primary focus of this presentation is approaching the migration of a large, legacy data store into a new schema built with Django. Includes discussion of how to structure a migration script so that it will run efficiently and scale. Learn how to recognize and evaluate trouble spots.
Also discusses some general tips and tricks for working with data and establishing a productive workflow.
The document discusses strategies for migrating large amounts of legacy data from an old database into a new Django application. Some key points:
- Migrating data in batches and minimizing database queries per row processed can improve performance for large datasets.
- Tools like SQLAlchemy and Maatkit can help optimize the migration process.
- It's important to profile queries, enable logging/debugging, and design migrations that can resume/restart after failures or pause for maintenance.
- Preserving some legacy metadata like IDs on the new models allows mapping data between the systems. Declarative and modular code helps scale the migration tasks.
Large-scale Recommendation Systems on Just a PCAapo Kyrölä
Aapo Kyrölä presented on running large-scale recommender systems on a single PC using GraphChi, a framework for graph computation on disk. GraphChi uses parallel sliding windows to efficiently process graphs that do not fit in memory by only loading subsets of the graph into RAM at a time. Kyrölä demonstrated training recommender models like ALS matrix factorization and item-based collaborative filtering on large graphs like Twitter using GraphChi on a single laptop. He concluded that very large recommender algorithms can now be run on a single machine and that GraphChi and similar frameworks hide the low-level optimizations needed for efficient single machine graph computation.
Data Science for Dummies - Data Engineering with Titanic dataset + Databricks...Rodney Joyce
Number 2 in the Data Science for Dummies series - We'll predict Titanic survival with Databricks, python and MLSpark.
These are the slides only (excuse the Powerpoint animation issues) - check out the actual tech talk on YouTube: https://rodneyjoyce.home.blog/2019/05/03/data-science-for-dummies-machine-learning-with-databricks-python-sparkml-tech-talk-1-of-7/)
If you have not used Databricks before check out the first talk - Databricks for Dummies.
Here's the rest of the series: https://rodneyjoyce.home.blog/tag/data-science-for-dummies/
1) Data Science overview with Databricks
2) Titanic survival prediction with Azure Machine Learning Studio + Kaggle
3) Data Engineering with Titanic dataset + Databricks + Python
4) Titanic with Databricks + Spark ML
5) Titanic with Databricks + Azure Machine Learning Service
6) Titanic with Databricks + MLS + AutoML
7) Titanic with Databricks + MLFlow
8) Titanic with .NET Core + ML.NET
9) Deployment, DevOps/MLOps and Productionisation
Identifying Auxiliary Web Images Using Combinations of AnalysesTewson Seeoun
This document summarizes a study that aimed to identify auxiliary web images using a combination of analyses. The methodology involved analyzing features of web pages like image position, dimensions and number of images using the DOM, and analyzing image-level features like number of colors and faces using tools like PIL and OpenCV. A SVM classifier was trained on these features from a sample of pages to recognize auxiliary images with an average accuracy of 93.17% after optimization. While the current approach performed well, future work could involve context analysis, weighted features and adaptive page analysis to improve the system.
Learning Predictive Modeling with TSA and KaggleYvonne K. Matos
This document summarizes Yvonne Matos' presentation on learning predictive modeling by participating in Kaggle challenges using TSA passenger screening data.
The key points are:
1) Matos started with a small subset of 120 images from one body zone to build initial neural network models and address challenges of large data sizes and compute requirements.
2) Through iterative tuning, her best model achieved good performance identifying non-threat images but had a high false negative rate for threats.
3) Her next steps were to reduce the false negative rate, run models on Google Cloud to handle full data sizes, and prepare the best model for real-world use.
Efficient Model Selection for Deep Neural Networks on Massively Parallel Proc...inside-BigData.com
In this deck from FOSDEM 2020, Frank McQuillan from Pivotal presents: Efficient Model Selection for Deep Neural Networks on Massively Parallel Processing Databases.
"In this session we will present an efficient way to train many deep learning model configurations at the same time with Greenplum, a free and open source massively parallel database based on PostgreSQL. The implementation involves distributing data to the workers that have GPUs available and hopping model state between those workers, without sacrificing reproducibility or accuracy. Then we apply optimization algorithms to generate and prune the set of model configurations to try.
Deep neural networks are revolutionizing many machine learning applications, but hundreds of trials may be needed to generate a good model architecture and associated hyperparameters. This is the challenge of model selection. It is time consuming and expensive, especially if you are only training one model at a time.
Massively parallel processing databases can have hundreds of workers, so can you use this parallel compute architecture to address the challenge of model selection for deep nets, in order to make it faster and cheaper?
It’s possible!
We will demonstrate results from this project using a version of Hyperband, which is a well known hyperparameter optimization algorithm, and the deep learning frameworks Keras and TensorFlow, all running on Greenplum database using Apache MADlib. Other topics will include architecture, scalability results and bright opportunities for the future."
Watch the video: https://wp.me/p3RLHQ-lsQ
Learn more: https://fosdem.org/2020/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Web Performance Part 4 "Client-side performance"Binary Studio
The presentation is devoted to client side performance of a web app. All 4 presentations will help you reduce latency, enrich optimization of javascript code, discover tricky parts when working with API browser, see best practices of networking and learn lots of other important and interesting things. Enjoy! =)
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
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
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Webinar: Designing a schema for a Data WarehouseFederico Razzoli
Are you new to data warehouses (DWH)? Do you need to check whether your data warehouse follows the best practices for a good design? In both cases, this webinar is for you.
A data warehouse is a central relational database that contains all measurements about a business or an organisation. This data comes from a variety of heterogeneous data sources, which includes databases of any type that back the applications used by the company, data files exported by some applications, or APIs provided by internal or external services.
But designing a data warehouse correctly is a hard task, which requires gathering information about the business processes that need to be analysed in the first place. These processes must be translated into so-called star schemas, which means, denormalised databases where each table represents a dimension or facts.
We will discuss these topics:
- How to gather information about a business;
- Understanding dictionaries and how to identify business entities;
- Dimensions and facts;
- Setting a table granularity;
- Types of facts;
- Types of dimensions;
- Snowflakes and how to avoid them;
- Expanding existing dimensions and facts.
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!
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
12. 1. Define problem
2. Prepare data
a. Collection
b. Cleaning
c. Transformation
d. Feature Engineering
3. Choose learning algorithm
4. Train candidate model
5. Evaluate performance
6. Improve results
7. Deploy chosen model
8. Feedback loop
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13. 1. Define problem (object classification: pixels -> object class)
2. Prepare data
a. Collection (web scraping)
b. Cleaning (remove images missing labels)
c. Transformation (reduce resolution)
d. Feature Engineering (normalize pixel values)
3. Choose learning algorithm (convolutional neural network)
4. Train candidate model (gradient descent)
5. Evaluate performance (loss/accuracy)
6. Improve results (hyperparameter optimization)
7. Deploy chosen model (Google Cloud Platform)
8. Feedback loop (keep prototyping with alternative variations of the model)
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