Only on rare occasions do we consider the amount of data that our every action produces. It’s pretty overwhelming just to think about every interaction on every app on every device in our bag or pocket, in every environment and every location.
But then there’s more. We also use access cards, transportation passes and gym memberships. We have hobbies, we travel, buy groceries, books and maybe warm beverages on rainy days. We are part of multiple communities. Looking around billions of people are doing the same. Our every action produces data about us. This is big.
We believe taking an interest in this wealth of data will be the key to success for next generation Event Companies.
We are living in a fast changing world, where it’s ever more important to foresee trends and seize opportunities. A global perspective is not a strategic advantage anymore it is a necessity.
Event companies are facilitators , they create common grounds for brands and audiences, by thoughtfully connecting goals and means. Having a deep understanding of customer behaviour, group psychology, digital habits, brand interaction, communication, and awareness through unlocking the power of big data will ensure next generation event companies thrive on strategy.
Notes from the Observation Deck // A Data Revolution gngeorge
Notes from the Observation Deck will provide you with an examined look at the interesting phenomena and trends taking place around us today. We present them to you with the hope of sparking broader conversations, debates and ideas. Please use this as a resource for knowledge, inspiration and enjoyment.
In business, we need to make sure we are ahead of the
curve as this data revolution takes shape. With connected
technology putting our customers firmly in the driving
seat, there are clear challenges which will need to be
overcome.
Notes from the Observation Deck // A Data Revolution gngeorge
Notes from the Observation Deck will provide you with an examined look at the interesting phenomena and trends taking place around us today. We present them to you with the hope of sparking broader conversations, debates and ideas. Please use this as a resource for knowledge, inspiration and enjoyment.
In business, we need to make sure we are ahead of the
curve as this data revolution takes shape. With connected
technology putting our customers firmly in the driving
seat, there are clear challenges which will need to be
overcome.
O'Reilly eBook: Creating a Data-Driven Enterprise in Media | eubolrVasu S
An O'Reilly eBook about Creating a Data-Driven Enterprise in Media DataOps Insights from Comcast, Sling TV, and Turner Broadcasting.
https://www.qubole.com/resources/ebooks/ebook-creating-a-data-driven-enterprise-in-media
Most data integration software was built to run data through ETL servers. It worked well at the time for several reasons: there wasn’t that much data—1TB was considered a large amount of data at the time; most data was structured, and the turnaround time for that data was monthly. Even back then, daily loads became a problem for most companies. Because of the limitations of the early tools, much of the work was hand-coded, without documentation, and no central management.
Analysis on big data concepts and applicationsIJARIIT
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
In the analogue era information was scarce and came from questionnaires and sampling. Since the dawn of the digital age in 2012 far more data than ever before is stored and it is mainly collected passively, i.e. while people go about doing what they normally do, such as run their businesses, use their cell phones and conduct internet searches.
Analysts, policy makers and business people value business tendency surveys (BTS) and consumer opinion surveys (COS) specifically because the survey results are available before the corresponding (official) quantitative data. However, Big Data has begun to make inroads on areas traditionally covered by BTS and COS. It has a competitive edge over BTS and COS, as it is available in real-time, is based on all observations and does not rely on the active participation of respondents. Furthermore, Big Data has little direct production costs, because it is merely a by-product of business processes. In contrast, putting together and maintaining a sample of active respondents and collecting information through questionnaires as in the case of BTS and COS, require the upkeep of a costly infrastructure and the employment of people with scarce, specialised skills.
However, BTS and COS also have a competitive edge over Big Data in certain aspects. These aspects could broadly be put into two groups, namely 1) BTS and COS offer information that Big Data cannot supply and 2) BTS and COS do not suffer from some of the shortcomings of Big Data. The biggest competitive advantage of BTS and COS is that they measure phenomenon that Big Data does not cover. Big Data records only actual outcomes, while BTS and COS also cover unquantifiable expectations and assessments. Although Big Data often claims that it covers the whole population universe (and not only a selection) this does not necessarily prevent bias. For example, twitter feeds could be biased, because certain demographic or less activist groups are under-represented. In contrast, the research design and random sampling of BTS and COS limit their selection bias.
To remain relevant and survive, producers of BTS and COS will have to adapt and publicise their unique competitive advantage vis-à-vis Big Data in the future. The biggest shift will probably require that producers of BTS and COS make users more aware of the value of the unique forward looking information of BTS and COS (i.e. their recording of expectations about the future).
Big Data in the Fund Industry: From Descriptive to Prescriptive Data AnalyticsBroadridge
NICSA’s Technology Committee, including Dan Cwenar, President, Access Data, Broadridge, offer perspectives on the “state of play” of Big Data in the fund industry:
The history of “ Big Data”
The definition of Big Data in the context of industry applications.
The movement from descriptive towards prescriptive analytics in driving decisions
Common misconceptions about the use of predictive analytics.
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Big Data has recently gained relevance because companies are realizing what it can do for them and that it is a gold mine for finding competitive advantages. Proximity’s Juan Manuel Ramírez, Director of Strategy and...
Carousel30: Big data for digital marketersCarousel30
Carousel30's white paper that explains the most relevant aspects of big data for digital marketers.
It’s hard to read a blog, pick up a magazine, or have a conversation about business these days without the term “Big Data” coming up in some form or another. What it is exactly and how it relates to you as a digital marketer can be harder to determine. The purpose of this white paper is to talk about Big Data in terms that relate to marketing and advertising, and more specifically that relate to the digital marketer. There is much more information (or data, if you will) on this subject than this white paper allows, but the objective is to encourage further research and discovery on the areas of the subject that are most relevant to you and your current challenges within your organization or company.
Many of the references cited within this white paper provide deeper insights into specific aspects and we recommend reading them in their entirety, especially when they refer to areas of interest to you. We hope that this provides a good introduction to Big Data and is the beginning of a new step in the sophistication of your digital marketing and advertising efforts.
O'Reilly eBook: Creating a Data-Driven Enterprise in Media | eubolrVasu S
An O'Reilly eBook about Creating a Data-Driven Enterprise in Media DataOps Insights from Comcast, Sling TV, and Turner Broadcasting.
https://www.qubole.com/resources/ebooks/ebook-creating-a-data-driven-enterprise-in-media
Most data integration software was built to run data through ETL servers. It worked well at the time for several reasons: there wasn’t that much data—1TB was considered a large amount of data at the time; most data was structured, and the turnaround time for that data was monthly. Even back then, daily loads became a problem for most companies. Because of the limitations of the early tools, much of the work was hand-coded, without documentation, and no central management.
Analysis on big data concepts and applicationsIJARIIT
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
In the analogue era information was scarce and came from questionnaires and sampling. Since the dawn of the digital age in 2012 far more data than ever before is stored and it is mainly collected passively, i.e. while people go about doing what they normally do, such as run their businesses, use their cell phones and conduct internet searches.
Analysts, policy makers and business people value business tendency surveys (BTS) and consumer opinion surveys (COS) specifically because the survey results are available before the corresponding (official) quantitative data. However, Big Data has begun to make inroads on areas traditionally covered by BTS and COS. It has a competitive edge over BTS and COS, as it is available in real-time, is based on all observations and does not rely on the active participation of respondents. Furthermore, Big Data has little direct production costs, because it is merely a by-product of business processes. In contrast, putting together and maintaining a sample of active respondents and collecting information through questionnaires as in the case of BTS and COS, require the upkeep of a costly infrastructure and the employment of people with scarce, specialised skills.
However, BTS and COS also have a competitive edge over Big Data in certain aspects. These aspects could broadly be put into two groups, namely 1) BTS and COS offer information that Big Data cannot supply and 2) BTS and COS do not suffer from some of the shortcomings of Big Data. The biggest competitive advantage of BTS and COS is that they measure phenomenon that Big Data does not cover. Big Data records only actual outcomes, while BTS and COS also cover unquantifiable expectations and assessments. Although Big Data often claims that it covers the whole population universe (and not only a selection) this does not necessarily prevent bias. For example, twitter feeds could be biased, because certain demographic or less activist groups are under-represented. In contrast, the research design and random sampling of BTS and COS limit their selection bias.
To remain relevant and survive, producers of BTS and COS will have to adapt and publicise their unique competitive advantage vis-à-vis Big Data in the future. The biggest shift will probably require that producers of BTS and COS make users more aware of the value of the unique forward looking information of BTS and COS (i.e. their recording of expectations about the future).
Big Data in the Fund Industry: From Descriptive to Prescriptive Data AnalyticsBroadridge
NICSA’s Technology Committee, including Dan Cwenar, President, Access Data, Broadridge, offer perspectives on the “state of play” of Big Data in the fund industry:
The history of “ Big Data”
The definition of Big Data in the context of industry applications.
The movement from descriptive towards prescriptive analytics in driving decisions
Common misconceptions about the use of predictive analytics.
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
Big Data has recently gained relevance because companies are realizing what it can do for them and that it is a gold mine for finding competitive advantages. Proximity’s Juan Manuel Ramírez, Director of Strategy and...
Carousel30: Big data for digital marketersCarousel30
Carousel30's white paper that explains the most relevant aspects of big data for digital marketers.
It’s hard to read a blog, pick up a magazine, or have a conversation about business these days without the term “Big Data” coming up in some form or another. What it is exactly and how it relates to you as a digital marketer can be harder to determine. The purpose of this white paper is to talk about Big Data in terms that relate to marketing and advertising, and more specifically that relate to the digital marketer. There is much more information (or data, if you will) on this subject than this white paper allows, but the objective is to encourage further research and discovery on the areas of the subject that are most relevant to you and your current challenges within your organization or company.
Many of the references cited within this white paper provide deeper insights into specific aspects and we recommend reading them in their entirety, especially when they refer to areas of interest to you. We hope that this provides a good introduction to Big Data and is the beginning of a new step in the sophistication of your digital marketing and advertising efforts.
The success of an organization increasingly depends on their ability to draw conclusions regarding the various types of data available. Staying ahead of competitors requires many times to identify a trend, problem or opportunity microseconds before anyone else. That's why organizations must be able to analyze this information if they want to find insights that will help them to identify new opportunities underlying this phenomenon.
People are spontaneously uploading large amounts of information on the internet and this represents a great opportunity for companies to segment according to their behavior and not only socio-demographic factors. Companies store transactional information from their customers by making them fill in forms but the challenge for brands is to enrich these databases with information describing their customer’s behavior and daily habits. This info can be obtained through the online conversation and can be processed, crossed and enriched with many other types of information through different models based on Big Data. Following this procedure, we can complement the information we already have from our customers without having to ask them directly and therefor providing more value-added proposals to clients from a brand perspective.
Using the same technology with the right platform and the correct tactic, companies can achieve more ambitious goals that provide valuable information for the brand, which in turn could also enrich the customer’s experience, improving the customer journey for all types of clients.
less
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
Since 2005, when the term “Big Data” was launched, Big Data has become an increasingly topical theme. In terms of technological development and business adoption, the domain of Big Data has made powerful advances; and that is putting it mildly.
In this initial report on Big Data, the first of four, we give answers to questions concerning what exactly Big Data is, where it differs from existing data classification, how the transformative potential of Big Data can be estimated, and what the current situation (2012) is with regard to adoption and planning.
VINT attempts to create clarity in these developments by presenting experiences and visions in perspective: objectively and laced with examples. But not all answers, not by a long way, are readily available. Indeed, more questions will arise – about the roadmap, for example, that you wish to use for Big Data. Or about governance. Or about the way you may have to revamp your organization. About the privacy issues that Big Data raises, such as those involving social analytics. And about the structures that new algorithms and systems will probably bring us.
http://www.ict-books.com/books/inspiration-trends
The REAL Impact of Big Data on PrivacyClaudiu Popa
The awesome promise of Big Data is tempered by the need to protect personal information. Data scientists must expertly navigate the legislative waters and acquire the skills to protect privacy and security. This talk provides enterprise leaders with answers and suggests questions to ask when the time comes to consider the vast opportunities offered by big data.
What is big data ? | Big Data ApplicationsShilpaKrishna6
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
June 2015 (142) MIS Quarterly Executive 67The Big Dat.docxcroysierkathey
June 2015 (14:2) | MIS Quarterly Executive 67
The Big Data Industry1 2
Big Data receives a lot of press and attention—and rightly so. Big Data, the combination of
greater size and complexity of data with advanced analytics,3 has been effective in improving
national security, making marketing more effective, reducing credit risk, improving medical
research and facilitating urban planning. In leveraging easily observable characteristics and
events, Big Data combines information from diverse sources in new ways to create knowledge,
make better predictions or tailor services. Governments serve their citizens better, hospitals
are safer, firms extend credit to those previously excluded from the market, law enforcers catch
more criminals and nations are safer.
Yet Big Data (also known in academic circles as “data analytics”) has also been criticized as a
breach of privacy, as potentially discriminatory, as distorting the power relationship and as just
“creepy.”4 In generating large, complex data sets and using new predictions and generalizations,
firms making use of Big Data have targeted individuals for products they did not know they
needed, ignored citizens when repairing streets, informed friends and family that someone
is pregnant or engaged, and charged consumers more based on their computer type. Table 1
summarizes examples of the beneficial and questionable uses of Big Data and illustrates the
1 Dorothy Leidner is the accepting senior editor for this article.
2 This work has been funded by National Science Foundation Grant #1311823 supporting a three-year study of privacy online. I
wish to thank the participants at the American Statistical Association annual meeting (2014), American Association of Public Opin-
ion Researchers (2014) and the Philosophy of Management conference (2014), as well as Mary Culnan, Chris Hoofnagle and Katie
Shilton for their thoughtful comments on an earlier version of this article.
3 Both the size of the data set, due to the volume, variety and velocity of the data, as well as the advanced analytics, combine to
create Big Data. Key to definitions of Big Data are that the amount of data and the software used to analyze it have changed and
combine to support new insights and new uses. See also Ohm, P. “Fourth Amendment in a World without Privacy,” Mississippi.
Law Journal (81), 2011, pp. 1309-1356; Boyd, D. and Crawford, K. “Critical Questions for Big Data: Provocations for a Cultural,
Technological, and Scholarly Phenomenon,” Information, Communication & Society (15:5), 2012, pp. 662-679; Rubinstein, I. S.
“Big Data: The End of Privacy or a New Beginning?,” International Data Privacy Law (3:2), 2012, pp. 74-87; and Hartzog, W. and
Selinger, E. “Big Data in Small Hands,” Stanford Law Review Online (66), 2013, pp. 81-87.
4 Ur, B. et al. “Smart, Useful, Scary, Creepy: Perceptions of Online Behavioral Advertising,” presented at the Symposium On
Usable Privacy and Security, July 11-13, 2 ...
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.