The presentation will describe an algorithm through which one can recognize Devanagari Characters. Devanagari is the script in which Hindi is represented. This algorithm
could automatically segment character from the image of Devenagari text and then recognize them.
For extracting the individual characters from the image of Devanagari text, algorithm segmented the image several
times using the vertical and horizontal projection.
The algorithm starts with first segmenting the lines separately from the document by taking horizontal projection and then the line
into words by taking vertical projection of the line. Another step which is particular to the separation of
Devanagari characters was required and was done by first removing the header line by finding horizontal projection
of each word. The characters can then be extracted by vertical projection of the word without the header line.
Algorithm uses a Kohonen Neural Netowrk for the recognition task. After the separation of the characters from the
image, the image matrix was then downsampled to bring it down to a fixed size so as to make the recognition
size independent. The matrix can then be fed as input neurons to the Kohonen Neural Network and the winning neuron is
found which identifies the recognized the character. This information in Kohonen Neural Network was stored
earlier during the training phase of the neural network. For this, we first assigned random weights from input neurons
to output neurons and then for each training set, the winning neuron was calculated by finding the maximum
output produced by the neurons. The wights for this winning neuron were then adjusted so that it responds to this
pattern more strongly the next time.
Named Entity Recognition For Hindi-English code-mixed Twitter Text Amogh Kawle
Speakers often switch back and forth between languages when speaking or writing, mostly in informal settings. This language interchanging involves complex grammar and the terms “code switching” or “code mixing” are used to describe It .
BERT - Part 1 Learning Notes of Senthil KumarSenthil Kumar M
In this part 1 presentation, I have attempted to provide a '30,000 feet view' of BERT (Bidirectional Encoder Representations from Transformer) - a state of the art Language Model in NLP with high level technical explanations. I have attempted to collate useful information about BERT from various useful sources.
This Part 2 presentation is a more in-depth view of BERT - Bidirectional Encoder Representations from Transformer. The source links offer more depth to the brief overview in the slides
The presentation will describe an algorithm through which one can recognize Devanagari Characters. Devanagari is the script in which Hindi is represented. This algorithm
could automatically segment character from the image of Devenagari text and then recognize them.
For extracting the individual characters from the image of Devanagari text, algorithm segmented the image several
times using the vertical and horizontal projection.
The algorithm starts with first segmenting the lines separately from the document by taking horizontal projection and then the line
into words by taking vertical projection of the line. Another step which is particular to the separation of
Devanagari characters was required and was done by first removing the header line by finding horizontal projection
of each word. The characters can then be extracted by vertical projection of the word without the header line.
Algorithm uses a Kohonen Neural Netowrk for the recognition task. After the separation of the characters from the
image, the image matrix was then downsampled to bring it down to a fixed size so as to make the recognition
size independent. The matrix can then be fed as input neurons to the Kohonen Neural Network and the winning neuron is
found which identifies the recognized the character. This information in Kohonen Neural Network was stored
earlier during the training phase of the neural network. For this, we first assigned random weights from input neurons
to output neurons and then for each training set, the winning neuron was calculated by finding the maximum
output produced by the neurons. The wights for this winning neuron were then adjusted so that it responds to this
pattern more strongly the next time.
Named Entity Recognition For Hindi-English code-mixed Twitter Text Amogh Kawle
Speakers often switch back and forth between languages when speaking or writing, mostly in informal settings. This language interchanging involves complex grammar and the terms “code switching” or “code mixing” are used to describe It .
BERT - Part 1 Learning Notes of Senthil KumarSenthil Kumar M
In this part 1 presentation, I have attempted to provide a '30,000 feet view' of BERT (Bidirectional Encoder Representations from Transformer) - a state of the art Language Model in NLP with high level technical explanations. I have attempted to collate useful information about BERT from various useful sources.
This Part 2 presentation is a more in-depth view of BERT - Bidirectional Encoder Representations from Transformer. The source links offer more depth to the brief overview in the slides
An introduction to the Transformers architecture and BERTSuman Debnath
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
Near Duplicate Document Detection: Mathematical Modeling and AlgorithmsLiwei Ren任力偉
Near-duplicate document detection is a well-known problem in the area of information retrieval. It is an important problem to be solved for many applications in IT industry. It has been studied with profound research literatures. This article provides a novel solution to this classic problem. We present the problem with abstract models along with additional concepts such as text models, document fingerprints and document similarity. With these concepts, the problem can be transformed into keyword like search problem with results ranked by document similarity. There are two major techniques. The first technique is to extract robust and unique fingerprints from a document. The second one is to calculate document similarity effectively. Algorithms for both fingerprint extraction and document similarity calculation are introduced as a complete solution.
As a data science Intern at Leapcheck Services private limited, I have developed a naive chatbot using sequence to sequence model by LSTM of RNN. Sharing the tutorial which I made explicitly for the deep learning enthusiasts to
provide them a basic insight on how chatbot can be developed with the help of recurrent neural network.
Parameters Optimization for Improving ASR Performance in Adverse Real World N...Waqas Tariq
From the existing research it has been observed that many techniques and methodologies are available for performing every step of Automatic Speech Recognition (ASR) system, but the performance (Minimization of Word Error Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology is not dependent on the only technique applied in that method. The research work indicates that, performance mainly depends on the category of the noise, the level of the noise and the variable size of the window, frame, frame overlap etc is considered in the existing methods. The main aim of the work presented in this paper is to use variable size of parameters like window size, frame size and frame overlap percentage to observe the performance of algorithms for various categories of noise with different levels and also train the system for all size of parameters and category of real world noisy environment to improve the performance of the speech recognition system. This paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by applying variable size of parameters. It is observed that, it is really very hard to evaluate test results and decide parameter size for ASR performance improvement for its resultant optimization. Hence, this study further suggests the feasible and optimum parameter size using Fuzzy Inference System (FIS) for enhancing resultant accuracy in adverse real world noisy environmental conditions. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI). Keywords: ASR Performance, ASR Parameters Optimization, Multi-Environmental Training, Fuzzy Inference System for ASR, ubiquitous ASR system, Human Computer Interaction (HCI)
THE EFFECTS OF THE LDA TOPIC MODEL ON SENTIMENT CLASSIFICATIONijscai
Online reviews are a feedback to the product and play a key role in improving the product to cater to consumers. Online reviews that rely heavily on manual categorization are time consuming and labor intensive.The recurrent neural network in deep learning can process time series data, while the long and short term memory network can process long time sequence data well. This has good experimental verification support in natural language processing, machine translation, speech recognition and language model.The merits of the extracted data features affect the classification results produced by the classification model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way long-term and short-term memory network model, which provides a strong technical support for the experiment.Using the LDA topic model to extract the keywords needed to train the neural network and increase the internal relationship between words can improve the learning efficiency of the model. The experimental results in the same experimental environment are better than the traditional word frequency features.
NLP techniques used for Spell checking to recommend find error in the written word and also suggest a relevant word.
Algorithm: Jaccard Coefficient, The Levenshtein Distance
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
InftyReader is an Optical Character Recognition (OCR) application that automatically converts "inaccessible" math content such as: 1) printed textbooks containing mathematics; 2) images containing mathematics; and, 3) PDF files that containing mathematics into formats that are accessible by students with "print disabilities." These formats include LaTeX, MathML, and Word XML. A "print disability" is a condition related to blindness, visual impairment, specific learning disability or other physical condition in which the student needs an alternative or specialized format (i.e., Braille, Large Print, Audio, Digital text) in order to access and acquire knowledge from conventional print/digital materials.
ChattyInfty 3 is a talking math editor. It can be used to edit files processed by InftyReader. Once editing is complete, ChattyInfty 3 can export files into a wide range of accessible formats including:
1. Spoken Text
2. DAISY 2.02 multimedia
3. DAISY 2.02 audio
4. DAISY 3 multimedia
5. DAISY 3 text (with audio for math)
6. DAISY 3 text-only
7. EPUB3 media overlays
8. EPUB3 no audio
9. EPUB3 iBooks media overlays
BERT: Bidirectional Encoder Representation from Transformer.
BERT is a Pretrained Model by Google for State of the art NLP tasks.
BERT has the ability to take into account Syntaxtic and Semantic meaning of Text.
Python Application: Visual Approach of Hopfield Discrete Method for Hiragana ...journalBEEI
Python is a dynamic object-oriented programming language. Python provides strong support for integration with other programming languages and other tools. Python programming is rarely used in the field of artificial intelligence, especially artificial neural networks. This research focuses on running Python programming to recognize hiragana letters. In learning the character of Hiragana, one can experience difficulties because of the many combinations of vowels that form new letters by different means of reading and meaning. Discrete Hopfield network is a fully connected, that every unit is attached to every other unit. This network has asymmetrical weights. At Hopfield Network, each unit has no relationship with itself. Therefore it is expected that a computer system that can help recognize the Hiragana Images. With this pattern recognition Application of Hiragana Images, it is expected the system can be developed further to recognize the Hiragana Images quickly and precisely.
Agile 2015 Talk with Mike Lowery
“They are resisting the changes I am trying to implement!” It’s a common refrain when people don’t embrace a change with the speed or enthusiasm desired. Do you keep pushing, give up or call in the big guns? How you respond to resistance can doom the change to failure, or boost the chance of success.
As coaches, we introduce new ideas in many different contexts. Relying on positional authority (our role as coach), or calling on outside authority (the managers who hired us) isn't likely to get those ideas a fair hearing.
In this talk, Mike and Esther will help you see resistance from a new perspective. By understanding how much influence you have, what forces are interacting around you and seeing different ways to re-frame your issues you can still get your message across without “inflicting help” on others.
We hear so much about being an introvert but just knowing that isn't enough. You need to translate your personality into a competitive advantage and have strategies for where you need to adapt.
An introduction to the Transformers architecture and BERTSuman Debnath
The transformer is one of the most popular state-of-the-art deep (SOTA) learning architectures that is mostly used for natural language processing (NLP) tasks. Ever since the advent of the transformer, it has replaced RNN and LSTM for various tasks. The transformer also created a major breakthrough in the field of NLP and also paved the way for new revolutionary architectures such as BERT.
Near Duplicate Document Detection: Mathematical Modeling and AlgorithmsLiwei Ren任力偉
Near-duplicate document detection is a well-known problem in the area of information retrieval. It is an important problem to be solved for many applications in IT industry. It has been studied with profound research literatures. This article provides a novel solution to this classic problem. We present the problem with abstract models along with additional concepts such as text models, document fingerprints and document similarity. With these concepts, the problem can be transformed into keyword like search problem with results ranked by document similarity. There are two major techniques. The first technique is to extract robust and unique fingerprints from a document. The second one is to calculate document similarity effectively. Algorithms for both fingerprint extraction and document similarity calculation are introduced as a complete solution.
As a data science Intern at Leapcheck Services private limited, I have developed a naive chatbot using sequence to sequence model by LSTM of RNN. Sharing the tutorial which I made explicitly for the deep learning enthusiasts to
provide them a basic insight on how chatbot can be developed with the help of recurrent neural network.
Parameters Optimization for Improving ASR Performance in Adverse Real World N...Waqas Tariq
From the existing research it has been observed that many techniques and methodologies are available for performing every step of Automatic Speech Recognition (ASR) system, but the performance (Minimization of Word Error Recognition-WER and Maximization of Word Accuracy Rate- WAR) of the methodology is not dependent on the only technique applied in that method. The research work indicates that, performance mainly depends on the category of the noise, the level of the noise and the variable size of the window, frame, frame overlap etc is considered in the existing methods. The main aim of the work presented in this paper is to use variable size of parameters like window size, frame size and frame overlap percentage to observe the performance of algorithms for various categories of noise with different levels and also train the system for all size of parameters and category of real world noisy environment to improve the performance of the speech recognition system. This paper presents the results of Signal-to-Noise Ratio (SNR) and Accuracy test by applying variable size of parameters. It is observed that, it is really very hard to evaluate test results and decide parameter size for ASR performance improvement for its resultant optimization. Hence, this study further suggests the feasible and optimum parameter size using Fuzzy Inference System (FIS) for enhancing resultant accuracy in adverse real world noisy environmental conditions. This work will be helpful to give discriminative training of ubiquitous ASR system for better Human Computer Interaction (HCI). Keywords: ASR Performance, ASR Parameters Optimization, Multi-Environmental Training, Fuzzy Inference System for ASR, ubiquitous ASR system, Human Computer Interaction (HCI)
THE EFFECTS OF THE LDA TOPIC MODEL ON SENTIMENT CLASSIFICATIONijscai
Online reviews are a feedback to the product and play a key role in improving the product to cater to consumers. Online reviews that rely heavily on manual categorization are time consuming and labor intensive.The recurrent neural network in deep learning can process time series data, while the long and short term memory network can process long time sequence data well. This has good experimental verification support in natural language processing, machine translation, speech recognition and language model.The merits of the extracted data features affect the classification results produced by the classification model. The LDA topic model adds a priori a posteriori knowledge to classify the data so that the characteristics of the data can be extracted efficiently.Applied to the classifier can improve accuracy and efficiency. Two-way long-term and short-term memory networks are variants and extensions of cyclic neural networks.The deep learning framework Keras uses Tensorflow as the backend to build a convenient two-way long-term and short-term memory network model, which provides a strong technical support for the experiment.Using the LDA topic model to extract the keywords needed to train the neural network and increase the internal relationship between words can improve the learning efficiency of the model. The experimental results in the same experimental environment are better than the traditional word frequency features.
NLP techniques used for Spell checking to recommend find error in the written word and also suggest a relevant word.
Algorithm: Jaccard Coefficient, The Levenshtein Distance
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
InftyReader is an Optical Character Recognition (OCR) application that automatically converts "inaccessible" math content such as: 1) printed textbooks containing mathematics; 2) images containing mathematics; and, 3) PDF files that containing mathematics into formats that are accessible by students with "print disabilities." These formats include LaTeX, MathML, and Word XML. A "print disability" is a condition related to blindness, visual impairment, specific learning disability or other physical condition in which the student needs an alternative or specialized format (i.e., Braille, Large Print, Audio, Digital text) in order to access and acquire knowledge from conventional print/digital materials.
ChattyInfty 3 is a talking math editor. It can be used to edit files processed by InftyReader. Once editing is complete, ChattyInfty 3 can export files into a wide range of accessible formats including:
1. Spoken Text
2. DAISY 2.02 multimedia
3. DAISY 2.02 audio
4. DAISY 3 multimedia
5. DAISY 3 text (with audio for math)
6. DAISY 3 text-only
7. EPUB3 media overlays
8. EPUB3 no audio
9. EPUB3 iBooks media overlays
BERT: Bidirectional Encoder Representation from Transformer.
BERT is a Pretrained Model by Google for State of the art NLP tasks.
BERT has the ability to take into account Syntaxtic and Semantic meaning of Text.
Python Application: Visual Approach of Hopfield Discrete Method for Hiragana ...journalBEEI
Python is a dynamic object-oriented programming language. Python provides strong support for integration with other programming languages and other tools. Python programming is rarely used in the field of artificial intelligence, especially artificial neural networks. This research focuses on running Python programming to recognize hiragana letters. In learning the character of Hiragana, one can experience difficulties because of the many combinations of vowels that form new letters by different means of reading and meaning. Discrete Hopfield network is a fully connected, that every unit is attached to every other unit. This network has asymmetrical weights. At Hopfield Network, each unit has no relationship with itself. Therefore it is expected that a computer system that can help recognize the Hiragana Images. With this pattern recognition Application of Hiragana Images, it is expected the system can be developed further to recognize the Hiragana Images quickly and precisely.
Agile 2015 Talk with Mike Lowery
“They are resisting the changes I am trying to implement!” It’s a common refrain when people don’t embrace a change with the speed or enthusiasm desired. Do you keep pushing, give up or call in the big guns? How you respond to resistance can doom the change to failure, or boost the chance of success.
As coaches, we introduce new ideas in many different contexts. Relying on positional authority (our role as coach), or calling on outside authority (the managers who hired us) isn't likely to get those ideas a fair hearing.
In this talk, Mike and Esther will help you see resistance from a new perspective. By understanding how much influence you have, what forces are interacting around you and seeing different ways to re-frame your issues you can still get your message across without “inflicting help” on others.
We hear so much about being an introvert but just knowing that isn't enough. You need to translate your personality into a competitive advantage and have strategies for where you need to adapt.
How teams work do's and don'ts for dealing with resistance to your team projectMike Cardus
Working on teams you will deal with resistance. What To Do When Stakeholders Resist Your Project … And What Not To Do. The checklist provides guidance on how to effectively deal with resistant behavior … and what not to do.
Evolving as a professional software developerAnton Kirillov
This is second edition of my keynote "On Being a Professional Software Developer" with slide comments (in Russian) which contain main ideas of the keynote.
I hope the slides could be used as a standalone reading material.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The Future of Computing is Distributed
Professor Ion Stoica, UC Berkeley RISELab
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
On how to change the utility curve of deep learning to make deep learning projects deliver an ROI no matter how accurate the machine learning system is - presented at the Nasscom Analytics Summit 2018.
"The workshop is designed for beginners to programming
The primary target audience are students of all ages and backgrounds. Attendees in the workshop will learn the basics of the Python programming language and get help for hands-on, project-based practice. Attendees will set up a Python development environment on their own computer and complete a short project in Python."
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.