A modern day data management platform driven by the evolved thought process and focus,
- From Data to Metadata engineering and Ontologies
- From Data Swamps to Data Products
- From Data for AI to AI for Data
- From Tech Debts to Data Monetization
Learn how you can generate revenue, reduce risk and save costs by monetizing your organisations data. Apply advanced machine learning and AI techniques to get insights and generate actions out of your data. Build the robust IT systems to get started with the data and AI.
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.
Building the Artificially Intelligent EnterpriseDatabricks
This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise.
How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise?
•Data and analytics – Where are we?
•Why is the journey only half-way done?
•2021 and beyond – The new era of AI usage and not just build
•The requirement – event-driven, on-demand and automated analytics
•Operationalising what you build – DataOps, MLOps and RPA
•Mobilising the masses to integrate AI into processes – what needs to be done?
•Business strategy alignment – the guiding light to AI utilisation for high reward
•Agility step change – the shift to no-code integration of AI by citizen developers
•Recording decisions, and analysing business impact
•Reinforcement-learning – transitioning to continuous reward
The 10 Best Data Analytics And BI Platforms And Tools In 2020Bernard Marr
Data has become a vital asset to all companies, big or small, and across all sectors. In order to extract value from that data businesses need the right analytics or BI (Business Intelligence) tools to make it happen. Here we look at the top 10 analytics and BI tools available today.
Learn how you can generate revenue, reduce risk and save costs by monetizing your organisations data. Apply advanced machine learning and AI techniques to get insights and generate actions out of your data. Build the robust IT systems to get started with the data and AI.
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.
Building the Artificially Intelligent EnterpriseDatabricks
This session looks at where we are today with data and analytics and what is needed to transition to the Artificially Intelligent Enterprise.
How do you mobilise developers to exploit what data scientists and business analysts have built? How do you align it all with business strategy to maximise business outcomes? How do you combine BI, predictive and prescriptive analytics, automation and reinforcement learning to get maximum value across the enterprise? What is the blueprint for building the artificially intelligent enterprise?
•Data and analytics – Where are we?
•Why is the journey only half-way done?
•2021 and beyond – The new era of AI usage and not just build
•The requirement – event-driven, on-demand and automated analytics
•Operationalising what you build – DataOps, MLOps and RPA
•Mobilising the masses to integrate AI into processes – what needs to be done?
•Business strategy alignment – the guiding light to AI utilisation for high reward
•Agility step change – the shift to no-code integration of AI by citizen developers
•Recording decisions, and analysing business impact
•Reinforcement-learning – transitioning to continuous reward
The 10 Best Data Analytics And BI Platforms And Tools In 2020Bernard Marr
Data has become a vital asset to all companies, big or small, and across all sectors. In order to extract value from that data businesses need the right analytics or BI (Business Intelligence) tools to make it happen. Here we look at the top 10 analytics and BI tools available today.
This presentation was made on June 16, 2020.
A recording of the presentation can be viewed here: https://youtu.be/khjW1t0gtSA
AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business.
H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation.
Tune into this virtual meetup to learn how companies are transforming their business with the power of AI and where to start.
About Parul Pandey:
Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science , evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.
Digital revolution is disrupting businesses like never before! Ability to extract actionable insight from a large amount of disparate data has become the determining factor of competitive advantage! Everyday new business models are created around data and forcing the incumbents to reinvent themselves to be relevant. Consumer facing businesses felt this pressure early on but eventually every business need to be data driven. But what is the best strategy to address this digital disruption? Our experience says the core data infrastructure modernization is the logical starting point! In this session, we will share trends, strategies and our experience on rejuvenating data integration landscape to address digital disruptions.
[DSC Adria 23] Thomas Miebach A modern, business focused data strategy with C...DataScienceConferenc1
In this session we’ll highlight some key challenges with the “old” data world, and how to overcome them with a modern data strategy focused on the business outcomes. We’ll illustrate how Collibra fits into this strategy and how Collibra supports it.
Top 20 artificial intelligence companies to watch out in 2022Kavika Roy
Artificial intelligence is fast becoming an intrinsic part of every industry.
It’s estimated that the global AI market will grow at a rate of 40.2% CAGR (Compound Annual Growth Rate) from the year 2021 to 2028. While the top names spend on research, the smaller organizations rely on offshore AI companies to embrace artificial intelligence and machine learning technology and integrate them into their business processes.
Working with the right AI company can help streamline the business operations, optimize the resources, and increase returns by changing the way management and employees perform their day-to-day activities at work.
Here are the top 20 artificial intelligence companies to watch out for in 2022:-
https://www.datatobiz.com/blog/top-artificial-intelligence-companies/
How businesses can align data initiatives with future goals. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences
Where the Warehouse Ends: A New Age of Information AccessInside Analysis
The Briefing Room with Barry Devlin and Composite Software
Live Webcast May 21, 2013
All good things must come to an end, and even though the data warehouse will remain a prominent force in the information age, the handwriting is all over the enterprise: the center of gravity is moving. Whether due to Big Data or real-time demands, Cloud computing or globalization, today's leading organizations have analytical needs that the warehouse simply cannot accommodate. That's why data virtualization continues to attract attention.
Register for this episode of The Briefing Room to hear veteran Analyst Barry Devlin explain why the traditional model for data warehousing is being outmoded by a range of more flexible methods for accessing and analyzing information assets. He'll be briefed by David Besemer of Composite Software who will discuss how his company's data virtualization platform can be used to provide access to all manner of information sources, including data warehouses, Big Data silos, as well as partner and public data sources on demand.
Visit: http://www.insideanalysis.com
Delivering Analytics at The Speed of Transactions with Data FabricDenodo
Watch full webinar here: https://bit.ly/3aAMTDD
It is no more an argument that data is the most critical asset for any business to succeed. While 85% of organizations want to improve their use of data insights in their decision making, according to a Forrester Survey, 91% of the respondents report that improving the use of data insights in decision making is challenging. To make data driven decision, organizations often turn to the data lakes, data lakehouses, cloud data warehouse etc. as their single source data repository. But the hard reality is that data is and will be spread across various repositories across cloud and regional boundaries.
Learn from renowned Forrester analyst and VP at Forrester, Noel Yuhanna:
- Why Data Fabric Is the best way to unify distributed data
- How Data Fabric be leveraged for data discovery, predictive analytics, data science and more
- Why data virtualization technology is key in building an Enterprise Data Fabric
Challenges in AI LLMs adoption in the EnterpriseGeorge Bara
The presentation "ITDays_2023_GeorgeBara" discusses challenges in adopting AI large language models (LLMs) in enterprise settings.
The presentation covers:
1. **Challenges in AI LLMs adoption**: It highlights the noise in the current AI landscape and questions the practical use of AI in real businesses.
2. **The DNA of an Enterprise**: Defines enterprise sizes and discusses the new solutions adoption process, emphasizing effective integration and minimizing disruption.
3. **Enterprise-Grade**: Lists qualities like robustness, reliability, scalability, performance, security, and support that are essential for enterprise-grade solutions.
4. **What are LLMs?**: Describes the pre-ChatGPT era with BERT, a model used for language understanding, and details its enterprise applications.
5. **LLM use-cases before ChatGPT**: Focuses on data triage, process automation, knowledge management, and the augmentation of business operations.
6. **EU Digital Decade Report**: Points out that AI adoption in Europe is slow and might not meet the 2030 targets.
7. **Adoption Challenges**: Addresses top challenges such as data security, predictability, performance, control, regulatory compliance, ethics, sustainability, and ROI.
8. **Conclusion**: Reflects on the slow adoption of AI in enterprises, suggesting that a surge might occur once the technology matures and is ready for enterprise use.
The presenter concludes by stating that despite the hype around technologies like ChatGPT, enterprises are cautious and will adopt new technologies at their own pace. He anticipates a gradual then sudden adoption pattern once LLMs are proven to be enterprise-ready.
Agile Mumbai 2022
Real-Time Insights and AI for better Products, Customer experience and Resilient Platform
Balvinder Kaur
Principal Consultant, Thoughtworks
Sushant Joshi
Product Manager, Thoughtworks
The 4 Biggest Trends In Big Data and Analytics Right For 2021Bernard Marr
Big Data is a term that’s come to be used to describe the technology and practice of working with data that’s not only large in volume but also fast and comes in many different forms. For every Elon Musk with a self-driving car to sell, or Jeff Bezos with a cashier-less convenience store, there is a sophisticated Big Data operation and an army of clever data scientists who’ve turned a vision into reality.
Designing a Successful Governed Citizen Data Science StrategyDATAVERSITY
To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don't know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents.
In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.
Katpro Technologies is an advanced technology firm offering specialized services in Microsoft Azure, Microsoft Sharepoint Consulting, Microsoft .NET based IT development and mobile app developments.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
This presentation was made on June 16, 2020.
A recording of the presentation can be viewed here: https://youtu.be/khjW1t0gtSA
AI is unlocking new potential for every enterprise. Organizations are using AI and machine learning technology to inform business decisions, predict potential issues, and provide more efficient, customized customer experiences. The results can enable a competitive edge for the business.
H2O.ai is a visionary leader in AI and machine learning and is on a mission to democratize AI for everyone. We believe that every company can become an AI company, not just the AI Superpowers. We are empowering companies with our leading AI and Machine Learning platforms, our expertise, experience and training to embark on their own AI journey to become AI companies themselves. All companies in all industries can participate in this AI Transformation.
Tune into this virtual meetup to learn how companies are transforming their business with the power of AI and where to start.
About Parul Pandey:
Parul is a Data Science Evangelist here at H2O.ai. She combines Data Science , evangelism and community in her work. Her emphasis is to spread the information about H2O and Driverless AI to as many people as possible, She is also an active writer and has contributed towards various national and international publications.
Digital revolution is disrupting businesses like never before! Ability to extract actionable insight from a large amount of disparate data has become the determining factor of competitive advantage! Everyday new business models are created around data and forcing the incumbents to reinvent themselves to be relevant. Consumer facing businesses felt this pressure early on but eventually every business need to be data driven. But what is the best strategy to address this digital disruption? Our experience says the core data infrastructure modernization is the logical starting point! In this session, we will share trends, strategies and our experience on rejuvenating data integration landscape to address digital disruptions.
[DSC Adria 23] Thomas Miebach A modern, business focused data strategy with C...DataScienceConferenc1
In this session we’ll highlight some key challenges with the “old” data world, and how to overcome them with a modern data strategy focused on the business outcomes. We’ll illustrate how Collibra fits into this strategy and how Collibra supports it.
Top 20 artificial intelligence companies to watch out in 2022Kavika Roy
Artificial intelligence is fast becoming an intrinsic part of every industry.
It’s estimated that the global AI market will grow at a rate of 40.2% CAGR (Compound Annual Growth Rate) from the year 2021 to 2028. While the top names spend on research, the smaller organizations rely on offshore AI companies to embrace artificial intelligence and machine learning technology and integrate them into their business processes.
Working with the right AI company can help streamline the business operations, optimize the resources, and increase returns by changing the way management and employees perform their day-to-day activities at work.
Here are the top 20 artificial intelligence companies to watch out for in 2022:-
https://www.datatobiz.com/blog/top-artificial-intelligence-companies/
How businesses can align data initiatives with future goals. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences. For the event: AI, Data, and CRM: Shaping Business through Unique Experiences
Where the Warehouse Ends: A New Age of Information AccessInside Analysis
The Briefing Room with Barry Devlin and Composite Software
Live Webcast May 21, 2013
All good things must come to an end, and even though the data warehouse will remain a prominent force in the information age, the handwriting is all over the enterprise: the center of gravity is moving. Whether due to Big Data or real-time demands, Cloud computing or globalization, today's leading organizations have analytical needs that the warehouse simply cannot accommodate. That's why data virtualization continues to attract attention.
Register for this episode of The Briefing Room to hear veteran Analyst Barry Devlin explain why the traditional model for data warehousing is being outmoded by a range of more flexible methods for accessing and analyzing information assets. He'll be briefed by David Besemer of Composite Software who will discuss how his company's data virtualization platform can be used to provide access to all manner of information sources, including data warehouses, Big Data silos, as well as partner and public data sources on demand.
Visit: http://www.insideanalysis.com
Delivering Analytics at The Speed of Transactions with Data FabricDenodo
Watch full webinar here: https://bit.ly/3aAMTDD
It is no more an argument that data is the most critical asset for any business to succeed. While 85% of organizations want to improve their use of data insights in their decision making, according to a Forrester Survey, 91% of the respondents report that improving the use of data insights in decision making is challenging. To make data driven decision, organizations often turn to the data lakes, data lakehouses, cloud data warehouse etc. as their single source data repository. But the hard reality is that data is and will be spread across various repositories across cloud and regional boundaries.
Learn from renowned Forrester analyst and VP at Forrester, Noel Yuhanna:
- Why Data Fabric Is the best way to unify distributed data
- How Data Fabric be leveraged for data discovery, predictive analytics, data science and more
- Why data virtualization technology is key in building an Enterprise Data Fabric
Challenges in AI LLMs adoption in the EnterpriseGeorge Bara
The presentation "ITDays_2023_GeorgeBara" discusses challenges in adopting AI large language models (LLMs) in enterprise settings.
The presentation covers:
1. **Challenges in AI LLMs adoption**: It highlights the noise in the current AI landscape and questions the practical use of AI in real businesses.
2. **The DNA of an Enterprise**: Defines enterprise sizes and discusses the new solutions adoption process, emphasizing effective integration and minimizing disruption.
3. **Enterprise-Grade**: Lists qualities like robustness, reliability, scalability, performance, security, and support that are essential for enterprise-grade solutions.
4. **What are LLMs?**: Describes the pre-ChatGPT era with BERT, a model used for language understanding, and details its enterprise applications.
5. **LLM use-cases before ChatGPT**: Focuses on data triage, process automation, knowledge management, and the augmentation of business operations.
6. **EU Digital Decade Report**: Points out that AI adoption in Europe is slow and might not meet the 2030 targets.
7. **Adoption Challenges**: Addresses top challenges such as data security, predictability, performance, control, regulatory compliance, ethics, sustainability, and ROI.
8. **Conclusion**: Reflects on the slow adoption of AI in enterprises, suggesting that a surge might occur once the technology matures and is ready for enterprise use.
The presenter concludes by stating that despite the hype around technologies like ChatGPT, enterprises are cautious and will adopt new technologies at their own pace. He anticipates a gradual then sudden adoption pattern once LLMs are proven to be enterprise-ready.
Agile Mumbai 2022
Real-Time Insights and AI for better Products, Customer experience and Resilient Platform
Balvinder Kaur
Principal Consultant, Thoughtworks
Sushant Joshi
Product Manager, Thoughtworks
The 4 Biggest Trends In Big Data and Analytics Right For 2021Bernard Marr
Big Data is a term that’s come to be used to describe the technology and practice of working with data that’s not only large in volume but also fast and comes in many different forms. For every Elon Musk with a self-driving car to sell, or Jeff Bezos with a cashier-less convenience store, there is a sophisticated Big Data operation and an army of clever data scientists who’ve turned a vision into reality.
Designing a Successful Governed Citizen Data Science StrategyDATAVERSITY
To compete in today’s digital economy, enterprises require new ways to expand AI across their entire organization. Nearly all firms want to do more with data science, but they don't know where to begin or how to properly empower citizen data scientists to avoid common AI gone wrong accidents.
In this session, we will discuss how to approach your journey into citizen data science with existing analytics talent. Proven best practices and lessons learned from successful early adopters of augmented data science will be shared. We will walk through example initiative roadmaps, recommended staffing, upskilling, mentoring and ongoing governance.
Katpro Technologies is an advanced technology firm offering specialized services in Microsoft Azure, Microsoft Sharepoint Consulting, Microsoft .NET based IT development and mobile app developments.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
