The technology of introducing new algorithms from data as well as past experience to accomplish tasks without human involvement. Ask me about detais - mailto mkavaliova@ibagroup.eu
This document outlines a roadmap for developing a business intelligence project through 16 steps organized into 6 stages: justification, planning, business analysis, design, construction, and deployment. The stages involve assessing business needs, planning the project, analyzing requirements and data, designing databases and applications, building extract/transform/load processes and a metadata repository, implementing the system, and evaluating results to improve future releases. The goal is to provide effective decision support and business analytics through a well-planned and executed intelligence initiative.
Ticketing Systems | Ticketing Systems framework | online ticketing systemAdmin iLink
Link’s Ticketing Systems Framework is an open, flexible and scalable solution designed to enable real-time visibility into the current state of operations.
Data warehouse generally contains both types of data i.e. historical & current data from various data sources. Data warehouse in world of computing can be defined as system created for analysis and reporting of these both types of data. These analysis report is then used by an organization to make decisions which helps them in their growth. Construction of data warehouse appears to be simple, collection of data from data sources into one place (after extraction, transform and loading). But construction involves several issues such as inconsistent data, logic conflicts, user acceptance, cost, quality, security, stake holder’s contradictions, REST alignment etc. These issues need to be overcome otherwise will lead to unfortunate consequences affecting the organization growth. Proposed model tries to solve these issues such as REST alignment, stake holder’s contradiction etc. by involving experts of various domains such as technical, analytical, decision makers, management representatives etc. during initialization phase to better understand the requirements and mapping these requirements to data sources during design phase of data warehouse.
This document summarizes a presentation on IT governance in banks. It discusses four revolutions currently impacting banks: 1) banks becoming online retailers, 2) new regulatory regimes enforcing low margins, 3) core banking becoming a commodity, and 4) customers becoming disloyal. It argues that banks need a new architecture to support real-time operations and that shared infrastructure will be the cheapest way to run core banking systems by 2020. The presentation also discusses how banking value chains will splinter, with different players handling the front, middle, and back office functions. It envisions banks establishing marketplaces and using APIs to integrate different providers in the future.
Simhachalam Mutyala has over 6 years of experience as a MicroStrategy consultant focusing on business analysis, requirements, development, testing, administration, and implementation of BI solutions. He has extensive experience using MicroStrategy products like Desktop, Architect, Object Manager, and Web. He has experience designing schemas, creating reports and dashboards, administering security roles and user privileges, and performance tuning MicroStrategy projects. His experience includes projects for clients such as Saudi Telecom, Vodafone India, BSNL, SABMiller, and Tatasky.
DIRECTING INTELLIGENCE, a high technology company specialized in big data analytics, active in knowledge creation and management since 2000, designs and creates adaptive, collaborative, Open Architecture Platforms like DATACTIF suite of BIG DATA ANALYTICS, based on machine learning methodology and algorithms. DIRECTING's Intelligent Solutions process transactional data from all sources regardless systems (DB2, Oracle, SQL Server, etc..) and data from the Web, structured as well as unstructured data, allowing real time and on line, substantive assessment of enterprise corporate knowledge.
DATACTIF® Big Data Mining Suite, uses machine learning methodology and algorithms (neural network, fuzzy systems, genetic algorithms, etc…), processes transactional data from operating systems allowing substantive assessment of enterprise knowledge.
The document describes an assignment involving questions about database management systems. It provides details about a case study involving Laxmi Bank transitioning to a centralized database management system from individual systems at each branch. Key points include:
1) Laxmi Bank faced challenges with an inefficient, fragmented IT infrastructure and decided to implement a robust centralized database system managed by consulting firm AKPY.
2) AKPY divided the bank's operations into retail and corporate banking and established two centralized data centers removing individual branch servers. This improved security, reduced costs, and allowed real-time processing of half a million transactions per month.
3) Transitioning to a distributed system would require data fragmentation, replication, and allocation techniques to distribute
This document outlines a roadmap for developing a business intelligence project through 16 steps organized into 6 stages: justification, planning, business analysis, design, construction, and deployment. The stages involve assessing business needs, planning the project, analyzing requirements and data, designing databases and applications, building extract/transform/load processes and a metadata repository, implementing the system, and evaluating results to improve future releases. The goal is to provide effective decision support and business analytics through a well-planned and executed intelligence initiative.
Ticketing Systems | Ticketing Systems framework | online ticketing systemAdmin iLink
Link’s Ticketing Systems Framework is an open, flexible and scalable solution designed to enable real-time visibility into the current state of operations.
Data warehouse generally contains both types of data i.e. historical & current data from various data sources. Data warehouse in world of computing can be defined as system created for analysis and reporting of these both types of data. These analysis report is then used by an organization to make decisions which helps them in their growth. Construction of data warehouse appears to be simple, collection of data from data sources into one place (after extraction, transform and loading). But construction involves several issues such as inconsistent data, logic conflicts, user acceptance, cost, quality, security, stake holder’s contradictions, REST alignment etc. These issues need to be overcome otherwise will lead to unfortunate consequences affecting the organization growth. Proposed model tries to solve these issues such as REST alignment, stake holder’s contradiction etc. by involving experts of various domains such as technical, analytical, decision makers, management representatives etc. during initialization phase to better understand the requirements and mapping these requirements to data sources during design phase of data warehouse.
This document summarizes a presentation on IT governance in banks. It discusses four revolutions currently impacting banks: 1) banks becoming online retailers, 2) new regulatory regimes enforcing low margins, 3) core banking becoming a commodity, and 4) customers becoming disloyal. It argues that banks need a new architecture to support real-time operations and that shared infrastructure will be the cheapest way to run core banking systems by 2020. The presentation also discusses how banking value chains will splinter, with different players handling the front, middle, and back office functions. It envisions banks establishing marketplaces and using APIs to integrate different providers in the future.
Simhachalam Mutyala has over 6 years of experience as a MicroStrategy consultant focusing on business analysis, requirements, development, testing, administration, and implementation of BI solutions. He has extensive experience using MicroStrategy products like Desktop, Architect, Object Manager, and Web. He has experience designing schemas, creating reports and dashboards, administering security roles and user privileges, and performance tuning MicroStrategy projects. His experience includes projects for clients such as Saudi Telecom, Vodafone India, BSNL, SABMiller, and Tatasky.
DIRECTING INTELLIGENCE, a high technology company specialized in big data analytics, active in knowledge creation and management since 2000, designs and creates adaptive, collaborative, Open Architecture Platforms like DATACTIF suite of BIG DATA ANALYTICS, based on machine learning methodology and algorithms. DIRECTING's Intelligent Solutions process transactional data from all sources regardless systems (DB2, Oracle, SQL Server, etc..) and data from the Web, structured as well as unstructured data, allowing real time and on line, substantive assessment of enterprise corporate knowledge.
DATACTIF® Big Data Mining Suite, uses machine learning methodology and algorithms (neural network, fuzzy systems, genetic algorithms, etc…), processes transactional data from operating systems allowing substantive assessment of enterprise knowledge.
The document describes an assignment involving questions about database management systems. It provides details about a case study involving Laxmi Bank transitioning to a centralized database management system from individual systems at each branch. Key points include:
1) Laxmi Bank faced challenges with an inefficient, fragmented IT infrastructure and decided to implement a robust centralized database system managed by consulting firm AKPY.
2) AKPY divided the bank's operations into retail and corporate banking and established two centralized data centers removing individual branch servers. This improved security, reduced costs, and allowed real-time processing of half a million transactions per month.
3) Transitioning to a distributed system would require data fragmentation, replication, and allocation techniques to distribute
Machine Learning Solutions benefits for business!Baharika Sopori
In this presentation, what is machine learning and how machine learning solutions can benefit your business is presented. It will give you a thorough idea how machine learning solutions is proving to be one of the best option for the growth of business.
Cloud-Based IoT Analytics and Machine LearningSatyaKVivek
Among the IT developments that have made it to the forefront in recent times, machine learning and IoT certainly stand out. As with most such technologies, integrating the two can help develop powerful IoT solutions and tackle complex challenges. More specifically speaking, machine learning can be leveraged in cloud based IoT analytics.
AI cloud is a promising domain that has gained prominence for uses like data storage, processing, and software development. AI helps develop self-learning systems using machine learning algorithms trained on large datasets without requiring human programming. These AI clouds have been used in domains like self-driving cars, medical diagnosis, and speech recognition. Machine learning as a service (MLaaS) offers machine learning tools and APIs through cloud computing services, with computation handled by the provider's data centers. Popular MLaaS platforms offer services for natural language processing, computer vision, predictive analytics, and more.
Next Gen AI Powered SIAM- Use Cases by Anurag Fuloriaanuragfuloria1
Next-Gen AI Powered SIAM model uses artificial intelligence to optimize IT service management for customers and suppliers in three key ways:
1) It automates routine ITSM tasks like incident logging and routing to improve efficiency and reduce costs.
2) It analyzes large amounts of ITSM data to predict issues, optimize resource allocation, and improve customer service.
3) It has the potential to transform many ITSM processes like incident management, problem management, and knowledge management through techniques like natural language processing, predictive analytics, and knowledge base augmentation.
10 Points On AI & ML in Enterprise Product Development.pdfCoreView Systems
AI and ML can help optimize every stage of product development by generating new ideas, automating testing and tasks, and providing insights from data analysis. ML models can identify patterns in product data to understand customer needs and preferences, while AI can assist in creating more efficient designs. During maintenance, AI tools can detect and diagnose problems in real-time to reduce downtime. AI also automates repetitive tasks, improves data visualization, and enhances security, personalization, and customer service. With these capabilities, AI helps drive business growth through optimized products, marketing, and sales strategies.
User Experience of AI - How to marry the two for ultimate success?Koru UX Design
Want UX and AI to mean more than just buzz words? Download our whitepaper on how to combine the two to create scalable enterprise products at a fast pace. Learn from real-life examples on how smart adoption solved crucial business challenges across various industries. Download now using this link, https://www.koruux.com/uxfreebies/ux-of-ai/
Power of AI and Machine Learning: Driving Innovation and Efficiencyas3338806
IPCS GLOBAL KOTTAYAM Institute: Empowering Minds in AI & Machine Learning. Equipping students with the expertise to drive innovation and efficiency in the era of intelligent automation."
Emerging Trends in Accounting 08 Digital Transformation of Accounting-Big Data Analytics in Accounting-Cloud Computing in accounting- - Green Accounting-Human Resource Accounting, Inflation Accounting, Database Accounting
It is the presentation of my project .In this ppt we tell you about our project . In inventory management system we handled the management of my shop . It is best in your helping material . So download our ppt and take rest .
This document discusses using Microsoft Excel 2013 and Microsoft Access to create an offers bank decision support system (DSS). It proposes a 4 phase approach: 1) Create a database and star schema using Access, 2) Fill the database with data by defining dimensions and measures and retrieving data in Excel, 3) Create a dashboard in Excel, 4) Analyze past trends and predict future trends using data mining. The document also provides background on business intelligence solutions and reviews literature on using BI to turn raw data into meaningful business insights.
10 Amazing Benefits of Machine Learning You Should Be Aware Of!Kavika Roy
https://www.datatobiz.com/blog/advantages-of-machine-learning/
ML aims to derive meaningful information from an immense amount of raw data. If implemented correctly, ML can act as a remedy to a variety of problems of market challenges and anticipate complicated consumer behaviors. We’ve already seen some of the significant technology companies coming up with their Cloud Machine Learning solutions, such as Google, Amazon, Microsoft, etc. Here are some of the critical ways ML can support your company:
Machine Learning: The First Salvo of the AI Business RevolutionCognizant
Machine learning (ML), a branch of artificial intelligence (AI), is coming into its own as a force in the business landscape, performing a variety of innovative and highly skilled activities that enhance customer experience and offer market advantages. This is a brief guide to getting started with ML, the thinking, tools and frameworks to make it a powerful business tool.
1. Artificial intelligence can be used to automate and enhance complex analytical tasks for optimizing business processes. The document discusses a general application schema that uses various AI methods like neural networks and optimization tools to optimize business processes.
2. The schema includes intelligent predictive models to forecast processes, intelligent optimization tools to find optimal process decisions, and intelligent analysis tools to detect unexpected process behaviors.
3. An example of applying the schema is a cash management system for banks that uses AI techniques like neural networks and genetic algorithms to optimize cash logistics and reduce costs.
This document provides details on the design of a real estate application that utilizes SMAC (social, mobile, analytics, and cloud) technologies. It begins with an introduction to the problem and objectives of optimizing real estate search and filtering using a user's online activities. It then describes the feasibility study, including economic, technical, and behavioral considerations. The remainder of the document provides details on the system analysis, design, and technologies used, including Salesforce, Visualforce, Apex, and various UML diagrams.
A forecast by Gartner in 2021 stated that artificial intelligence will help increase
customer satisfaction in organizations by 25 per cent by 2023. Artificial Intelligence (AI)
as we know; was first studied in 1956. But significant progress towards developing an AI
system and turning it into a technological reality needed decades of work.
Large-scale information that can be learned from and handled by AI technology can
successfully improve and revolutionise operations in various industries. AI technology
can anticipate needs and make deft, pertinent decisions after learning and
comprehension. AI makes decisions after analysing data, unlike business intelligence
technology, which analyses data and leaves the decision-making to humans
IMPLEMENTATION OF A DECISION SUPPORT SYSTEM AND BUSINESS INTELLIGENCE ALGORIT...ijaia
Data processing is crucial in the insurance industry, due to the important information that is contained in
the data. Business Intelligence (BI) allows to better manage the various activities as for companies
working in the insurance sector. Business Intelligence based on the Decision Support System (DSS), makes
it possible to improve the efficiency of decisions and processes, by improving them to the individual
characteristics of the agents. In this direction, Key Performance Indicators (KPIs) are valid tools that help
insurance companies to understand the current market and to anticipate future trends. The purpose of the
present paper is to discuss a case study, which was developed within the research project "DSS / BI
HUMAN RESOURCES", related to the implementation of an intelligent platform for the automated
management of agents' activities. The platform includes BI, DSS, and KPIs. Specifically, the platform
integrates Data Mining (DM) algorithms for agent scoring, K-means algorithms for customer clustering,
and a Long Short-Term Memory (LSTM) artificial neural network for the prediction of agents KPIs. The
LSTM model is validated by the Artificial Records (AR) approach, which allows to feed the training dataset
in data-poor situations as in many practical cases using Artificial Intelligence (AI) algorithms. Using the
LSTM-AR method, an analysis of the performance of the artificial neural network is carried out by
changing the number of records in the dataset. More precisely, as the number of records increases, the
accuracy increases up to a value equal to 0.9987.
The document describes a Driverless ML API that was created to automate machine learning workflows including feature engineering, model validation, tuning, selection, and deployment. The API uses machine learning interpretability techniques to provide visualizations and explanations of models. It aims to help scale data science efforts and enable both expert and junior data scientists to more quickly develop accurate, production-ready models. Key capabilities of the API include automated exploratory data analysis, feature selection and engineering, model selection and hyperparameter tuning using GPUs for faster training, and model interpretability visualizations.
Improve the efficiency, performance, and availability of DB2 workloads. Identify and prevent potential delays, outages, and performance problems. Simplify Db2 workload management. Reduce organizational costs and risk levels. Proactively solve performance problems before they occur.
Machine Learning Solutions benefits for business!Baharika Sopori
In this presentation, what is machine learning and how machine learning solutions can benefit your business is presented. It will give you a thorough idea how machine learning solutions is proving to be one of the best option for the growth of business.
Cloud-Based IoT Analytics and Machine LearningSatyaKVivek
Among the IT developments that have made it to the forefront in recent times, machine learning and IoT certainly stand out. As with most such technologies, integrating the two can help develop powerful IoT solutions and tackle complex challenges. More specifically speaking, machine learning can be leveraged in cloud based IoT analytics.
AI cloud is a promising domain that has gained prominence for uses like data storage, processing, and software development. AI helps develop self-learning systems using machine learning algorithms trained on large datasets without requiring human programming. These AI clouds have been used in domains like self-driving cars, medical diagnosis, and speech recognition. Machine learning as a service (MLaaS) offers machine learning tools and APIs through cloud computing services, with computation handled by the provider's data centers. Popular MLaaS platforms offer services for natural language processing, computer vision, predictive analytics, and more.
Next Gen AI Powered SIAM- Use Cases by Anurag Fuloriaanuragfuloria1
Next-Gen AI Powered SIAM model uses artificial intelligence to optimize IT service management for customers and suppliers in three key ways:
1) It automates routine ITSM tasks like incident logging and routing to improve efficiency and reduce costs.
2) It analyzes large amounts of ITSM data to predict issues, optimize resource allocation, and improve customer service.
3) It has the potential to transform many ITSM processes like incident management, problem management, and knowledge management through techniques like natural language processing, predictive analytics, and knowledge base augmentation.
10 Points On AI & ML in Enterprise Product Development.pdfCoreView Systems
AI and ML can help optimize every stage of product development by generating new ideas, automating testing and tasks, and providing insights from data analysis. ML models can identify patterns in product data to understand customer needs and preferences, while AI can assist in creating more efficient designs. During maintenance, AI tools can detect and diagnose problems in real-time to reduce downtime. AI also automates repetitive tasks, improves data visualization, and enhances security, personalization, and customer service. With these capabilities, AI helps drive business growth through optimized products, marketing, and sales strategies.
User Experience of AI - How to marry the two for ultimate success?Koru UX Design
Want UX and AI to mean more than just buzz words? Download our whitepaper on how to combine the two to create scalable enterprise products at a fast pace. Learn from real-life examples on how smart adoption solved crucial business challenges across various industries. Download now using this link, https://www.koruux.com/uxfreebies/ux-of-ai/
Power of AI and Machine Learning: Driving Innovation and Efficiencyas3338806
IPCS GLOBAL KOTTAYAM Institute: Empowering Minds in AI & Machine Learning. Equipping students with the expertise to drive innovation and efficiency in the era of intelligent automation."
Emerging Trends in Accounting 08 Digital Transformation of Accounting-Big Data Analytics in Accounting-Cloud Computing in accounting- - Green Accounting-Human Resource Accounting, Inflation Accounting, Database Accounting
It is the presentation of my project .In this ppt we tell you about our project . In inventory management system we handled the management of my shop . It is best in your helping material . So download our ppt and take rest .
This document discusses using Microsoft Excel 2013 and Microsoft Access to create an offers bank decision support system (DSS). It proposes a 4 phase approach: 1) Create a database and star schema using Access, 2) Fill the database with data by defining dimensions and measures and retrieving data in Excel, 3) Create a dashboard in Excel, 4) Analyze past trends and predict future trends using data mining. The document also provides background on business intelligence solutions and reviews literature on using BI to turn raw data into meaningful business insights.
10 Amazing Benefits of Machine Learning You Should Be Aware Of!Kavika Roy
https://www.datatobiz.com/blog/advantages-of-machine-learning/
ML aims to derive meaningful information from an immense amount of raw data. If implemented correctly, ML can act as a remedy to a variety of problems of market challenges and anticipate complicated consumer behaviors. We’ve already seen some of the significant technology companies coming up with their Cloud Machine Learning solutions, such as Google, Amazon, Microsoft, etc. Here are some of the critical ways ML can support your company:
Machine Learning: The First Salvo of the AI Business RevolutionCognizant
Machine learning (ML), a branch of artificial intelligence (AI), is coming into its own as a force in the business landscape, performing a variety of innovative and highly skilled activities that enhance customer experience and offer market advantages. This is a brief guide to getting started with ML, the thinking, tools and frameworks to make it a powerful business tool.
1. Artificial intelligence can be used to automate and enhance complex analytical tasks for optimizing business processes. The document discusses a general application schema that uses various AI methods like neural networks and optimization tools to optimize business processes.
2. The schema includes intelligent predictive models to forecast processes, intelligent optimization tools to find optimal process decisions, and intelligent analysis tools to detect unexpected process behaviors.
3. An example of applying the schema is a cash management system for banks that uses AI techniques like neural networks and genetic algorithms to optimize cash logistics and reduce costs.
This document provides details on the design of a real estate application that utilizes SMAC (social, mobile, analytics, and cloud) technologies. It begins with an introduction to the problem and objectives of optimizing real estate search and filtering using a user's online activities. It then describes the feasibility study, including economic, technical, and behavioral considerations. The remainder of the document provides details on the system analysis, design, and technologies used, including Salesforce, Visualforce, Apex, and various UML diagrams.
A forecast by Gartner in 2021 stated that artificial intelligence will help increase
customer satisfaction in organizations by 25 per cent by 2023. Artificial Intelligence (AI)
as we know; was first studied in 1956. But significant progress towards developing an AI
system and turning it into a technological reality needed decades of work.
Large-scale information that can be learned from and handled by AI technology can
successfully improve and revolutionise operations in various industries. AI technology
can anticipate needs and make deft, pertinent decisions after learning and
comprehension. AI makes decisions after analysing data, unlike business intelligence
technology, which analyses data and leaves the decision-making to humans
IMPLEMENTATION OF A DECISION SUPPORT SYSTEM AND BUSINESS INTELLIGENCE ALGORIT...ijaia
Data processing is crucial in the insurance industry, due to the important information that is contained in
the data. Business Intelligence (BI) allows to better manage the various activities as for companies
working in the insurance sector. Business Intelligence based on the Decision Support System (DSS), makes
it possible to improve the efficiency of decisions and processes, by improving them to the individual
characteristics of the agents. In this direction, Key Performance Indicators (KPIs) are valid tools that help
insurance companies to understand the current market and to anticipate future trends. The purpose of the
present paper is to discuss a case study, which was developed within the research project "DSS / BI
HUMAN RESOURCES", related to the implementation of an intelligent platform for the automated
management of agents' activities. The platform includes BI, DSS, and KPIs. Specifically, the platform
integrates Data Mining (DM) algorithms for agent scoring, K-means algorithms for customer clustering,
and a Long Short-Term Memory (LSTM) artificial neural network for the prediction of agents KPIs. The
LSTM model is validated by the Artificial Records (AR) approach, which allows to feed the training dataset
in data-poor situations as in many practical cases using Artificial Intelligence (AI) algorithms. Using the
LSTM-AR method, an analysis of the performance of the artificial neural network is carried out by
changing the number of records in the dataset. More precisely, as the number of records increases, the
accuracy increases up to a value equal to 0.9987.
The document describes a Driverless ML API that was created to automate machine learning workflows including feature engineering, model validation, tuning, selection, and deployment. The API uses machine learning interpretability techniques to provide visualizations and explanations of models. It aims to help scale data science efforts and enable both expert and junior data scientists to more quickly develop accurate, production-ready models. Key capabilities of the API include automated exploratory data analysis, feature selection and engineering, model selection and hyperparameter tuning using GPUs for faster training, and model interpretability visualizations.
Improve the efficiency, performance, and availability of DB2 workloads. Identify and prevent potential delays, outages, and performance problems. Simplify Db2 workload management. Reduce organizational costs and risk levels. Proactively solve performance problems before they occur.
APPULSE is a centralized support platform for business applications that run on z/OS servers.
Designed for Level 1 and Level 2 support, APPULSE provides uninterrupted operation of critical business applications, resulting from proactive problem identification and resolution.
С помощью Индустрии 4.0 предприятия создают цифровую модель текущей ситуации в компании, объясняют причины происходящих событий, прогнозируют будущие сценарии и планируют необходимые изменения.
91% промышленных предприятий уже инвестируют в цифровые фабрики.
With the Industry 4.0 technologies, enterprises build digital models of the ongoing processes in the companies, reveal the causes of the current situation, predict future scenarios, and plan changes to be adopted.
91% of industrial enterprises have been already investing in digital factories.
This document discusses migrating to SAP S/4HANA. It outlines some issues with older SAP systems like large databases and slow reports. The goal of migrating is to have a reliable and scalable system that provides instant insights. There are three migration options - a new implementation if the current system is outdated, a system conversion if the existing system is well-maintained, or a landscape transform for large enterprises. The case study discusses selecting data to move gradually while taking advantage of S/4HANA innovations. Implementation involves analyzing gaps, upgrading systems, migrating data, customizing, testing and support. A team of over 100 SAP specialists work on the migrations. Partners include SAP and certified experts.
Technical assessment before migration to S/4HANAIBA Group
Assessment results can help you cut down on migration budget and terms.
More datailes here https://ibagroupit.com/services/s4hana-migration-assessment/
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.
Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.
Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentations
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
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.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
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
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
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.
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.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
1. Machine Learning
The technology of introducing new algorithms from data as well as
past experience to accomplish tasks without human involvement
August
2019
2. As data sources multiply and the information becomes more
sophisticated and unstructured, the traditional approach to data
analysis and structuring turns out to be very resource-
consuming. Machine Learning and neural networks are the next
steps in data management and a basis for the systems that
perform without human intervention.
According to BCG, 85% of executives believe that AI will allow
their companies to obtain or sustain a competitive advantage.
«Is your business ready for artificial intelligence?»
Boston Consulting Group
What is going on in the
industries?
2
3. Machine Learning (ML) is a sphere that studies and
develops algorithms for computer systems so they can
learn and perform from experience without being
explicitly programmed. The algorithms rely on patterns
and are trained to build prediction models, form
personalized recommendations, recognize objects from
pictures, videos, etc.
What is Machine
Learning?
3
4. ML application in different spheres
Banks use ML to confirm credits, detect fraud, assess risks and provide underwriting. Self-training chatbots help advise clients
on bank’s products and services and reduce call center operating costs.
Logistics companies optimize supply chains, manage schedule and plan routes.Logistics
Banking and
Financial Services
Government State bodies use ML to enable data mining, increase programme implementation efficiency and save money.
Manufacturing
Healthcare
Manufacturing enterprises use computer pattern recognition to ensure product quality and predictive maintenance control.
ML helps analyze data received from sensors to assess patient health condition in real time and predict its further changes.
4
5. What are the benefits?
5
_ 01 Data mining
Machine Learning analyzes large unstructured data for valuable
insights.
_02 Predicative analytics
ML-based solutions analyze data, determine their relevance and help
with decision-making.
_03 Adaptation to fast-changing market conditions
ML algorithms rapidly process historical data, analyze new
information in real time and correct the results. Executives receive
actual data to empower decision-making.
6. Which business
processes ML can
enhance?
_1 Manufacturing
_2 Asset management
_3 Project management
_4 Help desk
_5 Human resource management
_6 Marketing
6
7. Manufacturing_1
To predict production line malfunctioning and product defects
The solution trains AI to supervise product quality throughout the whole production cycle. The system uses data received from
production line sensors to build predictive models of equipment failures and faulty product release. Due to early prediction
enterprises reduce the number and cost of the defects and failures.
ML-based control of production lines
Solution highlights
Business challenge
IBA Group solution
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8. Asset Management_2
To analyze power lines condition
The solution receives video from cameras installed on drones. The system analyzes videos and photos, recognizes power line
breakages, and notifies the staff of them.
Power Line Monitoring System based on computer vision.
Solution highlights
Business challenge
IBA Group solution
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9. Asset Management_2
To estimate brand visibility
The solution searches sites for tire images, recognizes the company’s logo, extracts information about tire parameters: brand,
model, size, type, and season. AI helps to assess, which tire types are the most popular.
Logo Recognition System in images
Solution highlights
Business challenge
IBA Group solution
9
10. Project Management_3
To predict project risks
The system analyzes the current tasks and uses historical data to estimate time for accomplishing a task. The system detects
weak points of the project – tasks that are either underestimated or overestimated.
Time risk prediction in projects
Solution highlights
Business challenge
IBA Group solution
10
11. Help Desk_4
To improve customer service, reduce call center operating costs and facilitate the operator’s work.
Chatbot implementation reduces the workload on the personnel of a call center, as it processes similar-type routine messages,
allowing operators to concentrate on non-standard requests.
A client receives a consultation at any time and doesn’t wait for a human operator, who might be busy at the moment.
Chatbots
Solution highlights
Business challenge
IBA Group solution
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12. Human Resource Management_5
To unify resumes and boost HR department performance
The company receives hundreds of resumes written by candidates in no particular format. Bringing all these resumes to a single
format is a time-consuming task for an HR manager.
The application helps the HR manager to extract candidate’s contact telephone number, name, personal information and other
data from resume files. The solution automatically uploads the data into corporate systems in pdf, doc or RTF formats.
Application for data extraction from resumes
Solution highlights
Business challenge
IBA Group solution
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13. How to prepare your data
for Machine Learning?
13
IBA Group team has developed a tool kit that helps prepare
data for ML.
_ Marker is a data markup tool
The tool helps to mark up the data that will be used in ML
model training. The data are securely stored in the IBA
Group data center.
The access to demo is available upon request.
_ML-Tag is a built-in tool for ML model training
The instrument provides for text classification, clustering, and
data extraction.
For a start, users upload documents into the instrument and
highlight the relevant clusters, then upload processed
documents into the ML tool.
14. What services we offer?
We develop solutions that recognize
texts, extract the required
information and process it.
Computer vision
We design solutions that retrieve
and analyze information from
images and videos, recognize
emotions.
Text mining
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Predictive analytics
We develop systems for data
mining and predictive modelling.
We analyze customer’s business processes, develop and test ML algorithms, customize third-party solutions, and integrate solutions
with customer’s IT infrastructure.
Machine Learning in
RPA
We create robots that are trained to
analyze and process information
with varying structure.
15. 5 facts about IBA Group
_1 26 years in IT
_2 2 600+ IT and business specialists
_3 20+ branches in 12 countries with headquarters in Prague, the Czech Republic
_4 Partner for leading IT service providers
_5 Developer of in-house solutions and products
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