The document discusses machine translation evaluation and deployment. It begins by explaining how machine translation quality varies across languages and domains, and how customization depends on data quality and volume. It then discusses Intento's multi-engine approach to machine translation evaluation and their Enterprise MT Hub for deploying the best machine translation engines. The Hub is designed to optimize machine translation for various scenarios like support chats, learning content, and more. It also discusses challenges of machine translation evaluation, integration, and management along with Intento's solutions.
Insights on broad capabilities in Machine Translation optimisation, Localization, Global Enterprise Enabling with Intento.
Procure and deploy best-fit MT
State of the Machine Translation by Intento (stock engines, Jan 2019)Konstantin Savenkov
Evaluation of 23 major Cloud Machine Translation Services with Stock (pre-trained) models (Alibaba, Amazon, Baidu, DeepL, Google Translate, GTCom Yeecloud, IBM Watson v3, Microsoft Text Translator v3, ModernMT, Naver Papago, Niutrans, PROMT, SAP Translation Hub, SDL Language Cloud and BeGlobal, Systran SMT and PNMT, Sogou, Tencent, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyze how the MT landscape changed over the last year.
EVALUATION IN USE: NAVIGATING THE MT ENGINE LANDSCAPE WITH THE INTENTO EVALUA...Konstantin Savenkov
The document discusses evaluating machine translation engines, including choosing candidate engines, translating projects with machine translation, reference-based scoring, and manual evaluation. It provides tips on factors to consider like language support, available quality and prices of different machine translation solutions, and how to select the optimal engines for a given project. Evaluation methods can help navigation the complex landscape of machine translation options.
This presentation covers our approach to building multi-purpose MT deployments. We talk about different enterprise use-cases for MT and the requirements of those use-cases. Since those requirements often have nothing to do with the objective linguistic quality, sometimes you don't want to select a specific MT engine just to meet them. Therefore, we provide some examples of how it's possible to fulfill those requirements by building NLP on top of your favorite Machine Translation black box.
State of the Machine Translation by Intento (November 2017)Konstantin Savenkov
Evaluation of 11 major Machine Translation (Google, Microsoft, IBM, SAP, Yandex, SDL, Systran, Baidu, GTCom, PROMT, DeepL) providers for 35 most popular language pairs: performance, quality, language coverage, API update frequency.
Evaluation of 14 major Cloud Machine Translation Engines (Google, Microsoft, IBM, IBM NMT, SAP, Amazon, Yandex, SDL, Systran, Systran PNMT, Baidu, GTCom, PROMT, DeepL) for 48 language pairs: performance, quality, language coverage, API update frequency.
Evaluation of 19 major Cloud Machine Translation Engines (Alibaba, Amazon, Baidu, DeepL, Google, GRCom, IBM SMT and NMT, Microsoft SMT and NMT, ModernMT, PROMT, SAP, SDL Language Cloud, Systran SMT and PNMT, Tencent, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyse how the MT landscape changed over the last year.
This document discusses machine translation (MT) procurement, deployment, and maintenance from Intento's perspective. It outlines that MT should be treated like software, with phases for procurement/evaluation, deployment/integration, and maintenance through training and updates. When procuring MT, key factors to consider include linguistic quality, customizability, tag support, and how these vary by language pair and direction. Deployment involves getting MT integrated into workflows and optimizing both the MT and workflows. Maintenance requires regular model retraining and technology monitoring to keep the MT performing well over time.
Insights on broad capabilities in Machine Translation optimisation, Localization, Global Enterprise Enabling with Intento.
Procure and deploy best-fit MT
State of the Machine Translation by Intento (stock engines, Jan 2019)Konstantin Savenkov
Evaluation of 23 major Cloud Machine Translation Services with Stock (pre-trained) models (Alibaba, Amazon, Baidu, DeepL, Google Translate, GTCom Yeecloud, IBM Watson v3, Microsoft Text Translator v3, ModernMT, Naver Papago, Niutrans, PROMT, SAP Translation Hub, SDL Language Cloud and BeGlobal, Systran SMT and PNMT, Sogou, Tencent, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyze how the MT landscape changed over the last year.
EVALUATION IN USE: NAVIGATING THE MT ENGINE LANDSCAPE WITH THE INTENTO EVALUA...Konstantin Savenkov
The document discusses evaluating machine translation engines, including choosing candidate engines, translating projects with machine translation, reference-based scoring, and manual evaluation. It provides tips on factors to consider like language support, available quality and prices of different machine translation solutions, and how to select the optimal engines for a given project. Evaluation methods can help navigation the complex landscape of machine translation options.
This presentation covers our approach to building multi-purpose MT deployments. We talk about different enterprise use-cases for MT and the requirements of those use-cases. Since those requirements often have nothing to do with the objective linguistic quality, sometimes you don't want to select a specific MT engine just to meet them. Therefore, we provide some examples of how it's possible to fulfill those requirements by building NLP on top of your favorite Machine Translation black box.
State of the Machine Translation by Intento (November 2017)Konstantin Savenkov
Evaluation of 11 major Machine Translation (Google, Microsoft, IBM, SAP, Yandex, SDL, Systran, Baidu, GTCom, PROMT, DeepL) providers for 35 most popular language pairs: performance, quality, language coverage, API update frequency.
Evaluation of 14 major Cloud Machine Translation Engines (Google, Microsoft, IBM, IBM NMT, SAP, Amazon, Yandex, SDL, Systran, Systran PNMT, Baidu, GTCom, PROMT, DeepL) for 48 language pairs: performance, quality, language coverage, API update frequency.
Evaluation of 19 major Cloud Machine Translation Engines (Alibaba, Amazon, Baidu, DeepL, Google, GRCom, IBM SMT and NMT, Microsoft SMT and NMT, ModernMT, PROMT, SAP, SDL Language Cloud, Systran SMT and PNMT, Tencent, Yandex, Youdao) for 48 language pairs: pricing, performance, quality, and language coverage. We also analyse how the MT landscape changed over the last year.
This document discusses machine translation (MT) procurement, deployment, and maintenance from Intento's perspective. It outlines that MT should be treated like software, with phases for procurement/evaluation, deployment/integration, and maintenance through training and updates. When procuring MT, key factors to consider include linguistic quality, customizability, tag support, and how these vary by language pair and direction. Deployment involves getting MT integrated into workflows and optimizing both the MT and workflows. Maintenance requires regular model retraining and technology monitoring to keep the MT performing well over time.
Talk by Konstantin Savenkov (Intento, Inc.) at Developer Week 2019 (Seattle: Cloud Edition).
There are already hundreds of AI functions available via different APIs. Pick Machine Translation, Sentiment Analysis, Image Tagging or anything else - there's already a choice of 20-30 AI vendors to pick from. I will make a brief overview of what types of models are already available in the cloud, which of those enable customization and important things to look after when selecting the model (or a set of those) for a specific project. I will also touch AI API developer experience, to give an idea what a developer should be prepared for when choosing the API to work with.
https://devweeksea2019.sched.com/event/OGSp/pro-talk-cloud-ai-landscape
State of the Machine Translation by Intento (stock engines, Jun 2019)Konstantin Savenkov
The document summarizes the state of machine translation models from various commercial providers. It finds that overall machine translation quality has improved for several language pairs since the previous report. The best performing machine translation provider has changed for 19 out of 48 language pairs evaluated. To achieve the best quality across all language pairs, eight different machine translation engines are required. Many providers have also increased their language coverage in recent months.
Improving the Demand Side of the AI Economy (API World 2018)Konstantin Savenkov
Training AI in-house is often infeasible as it requires a critical mass of talent and data, and has high R&D risks. For Cognitive AI, like machine translation and speech recognition, hundreds of pre-trained and adaptive models are already available on the market via APIs from many vendors. Their performance varies case by case and change often. Their prices are 100x-200x times different, hence a wrong choice may be a complete miss.
In this talk, we argue that the only way to go is to evaluate and continuously optimize AI vendor portfolio and introduce our vendor-agnostic demand-side API platform for AI.
In this survey, we compare features, language support, and pricing for 15 vendors of Sentiment Analysis.
We consider only hosted services with public API: several algorithms on Algorithmia marketplace, Microsoft Text Analytics, Repustate, Google Cloud Natural Language, IBM Watson NLU,
Meaning Cloud, TheSay PreCeive, AWS Comprehend, Aylien,
Bozon NLP, Salesforce Einstein Language, Twinword.
Talk at Stanford HAI Workshop on "Measurement in AI Policy: Opportunities and Challenges", October 30, 2019, Stanford, USA
When we procure Machine Translation vendors for the multi-vendor MT solutions we build for enterprises, we run a lot of MT evaluation projects. We evaluate commercial MT systems on public and private data to find the best system for a specific language pair and domain. These evaluations are quite different from what you see in WMT benchmarks, as we evaluate commercial systems, which are optimized for economic efficiency and real-time performance.
We have evaluated intent prediction performance, false positives, learning rate, language coverage, response time and pricing for 7 NLU providers: Amazon Lex, Facebook’s wit.ai, IBM Watson Conversation, Google’s API.ai, Microsoft LUIS, Recast.ai, Snips.ai
New Breakthroughs in Machine Transation Technologykantanmt
Tony O’Dowd takes us through some of the most innovative technologies offered on the KantanMT.com platform which are helping a growing community of KantanMT users to develop and self-manage custom Machine Translation engines in the cloud.
Maxim Khalilov then illustrates bmmt’s journey with Machine Translation on KantanMT. He discusses what they have achieved so far in terms of MT engine development and showcases the value that his team is bringing to their growing international client base through the use of Machine Translation.
The document discusses Intento's Enterprise MT Hub, which provides a single platform to evaluate, access, and manage multiple machine translation engines. It allows routing translations to the best-fit engine for the language pair and domain, and provides tools to integrate MT into internal systems, monitor performance, and manage models over their lifecycle.
State of the Domain-Adaptive Machine Translation by Intento (November 2018)Konstantin Savenkov
In this report, we have evaluated 6 modern domain-adaptive NMT engines on Biomedical dataset (English to German). ModernMT, Globalese, Google AutoML, IBM Custom NMT, Microsoft Custom Translate, and Tilde. We explored how they compare by performance (using reference-based scores, linguistic quality analysis and automatic quality estimation), total cost of ownership, dataset size requirements, training time, data protection policy and how to start using this advanced technology.
Dodging AI biases in future-proof Machine Translation solutionsKonstantin Savenkov
We all want to act locally while going global, and maintain an inclusive multilingual work environment for the international workforce. Every AI model has its linguistic, cultural, and geopolitical biases. Besides providing better linguistic quality for specific languages and domains, a particular Machine Translation system may not be fully compliant with local dialect, tone of voice, gender, and data locality rules. In this talk, we consider practical cases when those biases create obstacles in building a global presence and an inclusive multilingual work environment for an international company. We discuss how to dodge those biases by using multi-vendor international AI, and in some cases go further, by leveraging those biases to create more diverse and inclusive translations.
5 challenges of scaling l10n workflows KantanMT/bmmt webinarkantanmt
In this joint presentation, Tony O’Dowd, Founder and Chief Architect of KantanMT and Maxim Khalilov, Technical Lead of bmmt deliver an overview of the MT technology currently available in the language technology market, the challenges of operating MT systems at scale and speed, and their opinions on the future trajectory of MT.
Each presentation will be grounded with client examples, and how they’ve successfully integrated MT into their localization workflows.
Finally, both presenters will finish off with a 5 point checklist for successful MT deployment based on both the MT provider and LSP point of view.
If you have any questions about this presentation or want to get in touch with either company please contact:
Louise Irwin, Marketing Specialist at KantanMT (louisei@kantanmt.com)
Peggy Linder, Operations Manager at bmmt (peggy.lindner@bmmt.eu)
KantanMT provides a cloud-based platform that allows customers to build, measure, and deploy customized machine translation engines without needing hardware or software. Their platform includes features like TotalRecall to generate translation memories from uploaded data, BuildAnalytics to analyze training data quality, and an API to integrate machine translation into various systems. The platform aims to make machine translation easy for language service providers to adopt and use through consulting, free starter engines, and transparent analytics.
Maximising Machine Translation Return on Investment (KantanMT/Medialocate)kantanmt
The document discusses maximizing return on investment for machine translation projects. It introduces KantanMT, a cloud-based statistical machine translation system, and associated tools like KantanAnalytics and Kantan BuildAnalytics that help project managers and SMT developers optimize machine translation quality and costs. Case studies are presented showing how KantanMT and post-editing delivered significant cost savings, increased translator productivity, and enabled fast localization turnarounds for clients in software documentation, automotive parts data, and e-commerce product descriptions.
Working with MOSES and building high quality MT systems is not for the faint hearted. It requires a wide range of technical and linguistic based knowledge that is often difficult to find and develop within organisations. Consequently, only the biggest organisations have the financial muscle to invest and reap the awards of MT. This puts the small-to-medium sized organisations at a distinct disadvantage. KantanMT changes everything! KantanMT is a cloud-based implementation of MOSES which enables SMEs to embrace the advantages of MT - quickly and economically. This presentation will demonstrate the KantanMT approach to rapid engine training and tuning, data analytics used to predict MT quality and create tiered pricing structures and instantaneous engine deployment - all of which are driving the new MT Revolution!
Tony (Chief Architect, KantanMT.com) opens the proceedings with a temporal look at how MT technology has progressed. While embracing Rule Based MT in the 1970s, the industry switched over to Statistical MT around 2002 and is now faced with a new paradigm of Neural MT in 2016. For each technology progression, improved translation quality and fluency were achieved.
Summary: https://www.youtube.com/watch?v=19yyDa6mAsc
Full video: https://www.youtube.com/watch?v=EtbML0DTNHk
ILUG 2008 - The future of Notes & Domino Reporting - Let your Notes data rock...Synaptris Inc.
Here is the first part of the presentation made by Synaptris at the ILUG 2008 Conference titled “The future of Notes & Domino reporting. Make your Notes data rock!” on June 4, 2008. This presentation takes you through the case study of Orange Romania, IntelliPRINT customer, and explains how they revolutionized the way they look at Lotus Notes & Domino data and achieved 80% savings in IT time, 15% reduction in overall IT overhead and RoI within 12 weeks of deploying IntelliPRINT Reporting.
The other 3 parts of the Synaptris session at ILUG 08 will be soon uploaded here.
Manoj Yadav has over 7.5 years of experience delivering solutions using IBM's Tivoli portfolio including Netcool, Cognos, and Maximo. He has experience designing and developing automated cell outage reporting processes, developing customized reports, and implementing monitoring solutions. He is proficient in technologies like Cognos, Netcool, Oracle, SQL Server, and Linux/Windows and holds certifications in Cognos Report Author and Netcool Administrator.
- The document provides a summary of S Hariharan's professional experience including over 20 years of experience in Business Intelligence, Data Warehousing, ERP implementations, and managing teams of 3-5 members.
- Some of the key tools and technologies he has experience with include IBM Cognos TM1, Cognos BI, SQL Server, Infor Sun Systems, and SSIS.
- Currently he works as a Senior Consultant at Tekacademy Labs providing consulting services for Hindustan Unilever and Unilever Sri Lanka on their IBM Cognos TM1 and Cognos BI implementations.
The document discusses IBM's Z strategy and digital transformation model. It highlights how IBM Z continues to drive the global economy by processing billions of daily transactions. It also outlines IBM's digital transformation model for clients, which includes exposing APIs to enable apps and data, evolving to automate delivery pipelines, optimizing with analytics, and predicting and responding to service interruptions. The model is meant to help clients address digital transformation needs, leverage existing IBM Z assets to accelerate transformation, and achieve business and technical goals.
The document summarizes the MMT project, which aims to deliver a large-scale commercial online machine translation service based on a new open-source distributed architecture. The key points are:
1. MMT received 3M Euros in funding from Horizon 2020 from 2015-2017 with the goal of developing a next-generation machine translation system.
2. The MMT system is intended to be easier for users to set up and use for translation compared to existing systems, allowing users to directly connect translation memories and start translating.
3. MMT utilizes data pooling from various sources, including MyMemory, the TAUS Data Cloud, and CommonCrawl, with over 785 million words and 423 million segments
Talk by Konstantin Savenkov (Intento, Inc.) at Developer Week 2019 (Seattle: Cloud Edition).
There are already hundreds of AI functions available via different APIs. Pick Machine Translation, Sentiment Analysis, Image Tagging or anything else - there's already a choice of 20-30 AI vendors to pick from. I will make a brief overview of what types of models are already available in the cloud, which of those enable customization and important things to look after when selecting the model (or a set of those) for a specific project. I will also touch AI API developer experience, to give an idea what a developer should be prepared for when choosing the API to work with.
https://devweeksea2019.sched.com/event/OGSp/pro-talk-cloud-ai-landscape
State of the Machine Translation by Intento (stock engines, Jun 2019)Konstantin Savenkov
The document summarizes the state of machine translation models from various commercial providers. It finds that overall machine translation quality has improved for several language pairs since the previous report. The best performing machine translation provider has changed for 19 out of 48 language pairs evaluated. To achieve the best quality across all language pairs, eight different machine translation engines are required. Many providers have also increased their language coverage in recent months.
Improving the Demand Side of the AI Economy (API World 2018)Konstantin Savenkov
Training AI in-house is often infeasible as it requires a critical mass of talent and data, and has high R&D risks. For Cognitive AI, like machine translation and speech recognition, hundreds of pre-trained and adaptive models are already available on the market via APIs from many vendors. Their performance varies case by case and change often. Their prices are 100x-200x times different, hence a wrong choice may be a complete miss.
In this talk, we argue that the only way to go is to evaluate and continuously optimize AI vendor portfolio and introduce our vendor-agnostic demand-side API platform for AI.
In this survey, we compare features, language support, and pricing for 15 vendors of Sentiment Analysis.
We consider only hosted services with public API: several algorithms on Algorithmia marketplace, Microsoft Text Analytics, Repustate, Google Cloud Natural Language, IBM Watson NLU,
Meaning Cloud, TheSay PreCeive, AWS Comprehend, Aylien,
Bozon NLP, Salesforce Einstein Language, Twinword.
Talk at Stanford HAI Workshop on "Measurement in AI Policy: Opportunities and Challenges", October 30, 2019, Stanford, USA
When we procure Machine Translation vendors for the multi-vendor MT solutions we build for enterprises, we run a lot of MT evaluation projects. We evaluate commercial MT systems on public and private data to find the best system for a specific language pair and domain. These evaluations are quite different from what you see in WMT benchmarks, as we evaluate commercial systems, which are optimized for economic efficiency and real-time performance.
We have evaluated intent prediction performance, false positives, learning rate, language coverage, response time and pricing for 7 NLU providers: Amazon Lex, Facebook’s wit.ai, IBM Watson Conversation, Google’s API.ai, Microsoft LUIS, Recast.ai, Snips.ai
New Breakthroughs in Machine Transation Technologykantanmt
Tony O’Dowd takes us through some of the most innovative technologies offered on the KantanMT.com platform which are helping a growing community of KantanMT users to develop and self-manage custom Machine Translation engines in the cloud.
Maxim Khalilov then illustrates bmmt’s journey with Machine Translation on KantanMT. He discusses what they have achieved so far in terms of MT engine development and showcases the value that his team is bringing to their growing international client base through the use of Machine Translation.
The document discusses Intento's Enterprise MT Hub, which provides a single platform to evaluate, access, and manage multiple machine translation engines. It allows routing translations to the best-fit engine for the language pair and domain, and provides tools to integrate MT into internal systems, monitor performance, and manage models over their lifecycle.
State of the Domain-Adaptive Machine Translation by Intento (November 2018)Konstantin Savenkov
In this report, we have evaluated 6 modern domain-adaptive NMT engines on Biomedical dataset (English to German). ModernMT, Globalese, Google AutoML, IBM Custom NMT, Microsoft Custom Translate, and Tilde. We explored how they compare by performance (using reference-based scores, linguistic quality analysis and automatic quality estimation), total cost of ownership, dataset size requirements, training time, data protection policy and how to start using this advanced technology.
Dodging AI biases in future-proof Machine Translation solutionsKonstantin Savenkov
We all want to act locally while going global, and maintain an inclusive multilingual work environment for the international workforce. Every AI model has its linguistic, cultural, and geopolitical biases. Besides providing better linguistic quality for specific languages and domains, a particular Machine Translation system may not be fully compliant with local dialect, tone of voice, gender, and data locality rules. In this talk, we consider practical cases when those biases create obstacles in building a global presence and an inclusive multilingual work environment for an international company. We discuss how to dodge those biases by using multi-vendor international AI, and in some cases go further, by leveraging those biases to create more diverse and inclusive translations.
5 challenges of scaling l10n workflows KantanMT/bmmt webinarkantanmt
In this joint presentation, Tony O’Dowd, Founder and Chief Architect of KantanMT and Maxim Khalilov, Technical Lead of bmmt deliver an overview of the MT technology currently available in the language technology market, the challenges of operating MT systems at scale and speed, and their opinions on the future trajectory of MT.
Each presentation will be grounded with client examples, and how they’ve successfully integrated MT into their localization workflows.
Finally, both presenters will finish off with a 5 point checklist for successful MT deployment based on both the MT provider and LSP point of view.
If you have any questions about this presentation or want to get in touch with either company please contact:
Louise Irwin, Marketing Specialist at KantanMT (louisei@kantanmt.com)
Peggy Linder, Operations Manager at bmmt (peggy.lindner@bmmt.eu)
KantanMT provides a cloud-based platform that allows customers to build, measure, and deploy customized machine translation engines without needing hardware or software. Their platform includes features like TotalRecall to generate translation memories from uploaded data, BuildAnalytics to analyze training data quality, and an API to integrate machine translation into various systems. The platform aims to make machine translation easy for language service providers to adopt and use through consulting, free starter engines, and transparent analytics.
Maximising Machine Translation Return on Investment (KantanMT/Medialocate)kantanmt
The document discusses maximizing return on investment for machine translation projects. It introduces KantanMT, a cloud-based statistical machine translation system, and associated tools like KantanAnalytics and Kantan BuildAnalytics that help project managers and SMT developers optimize machine translation quality and costs. Case studies are presented showing how KantanMT and post-editing delivered significant cost savings, increased translator productivity, and enabled fast localization turnarounds for clients in software documentation, automotive parts data, and e-commerce product descriptions.
Working with MOSES and building high quality MT systems is not for the faint hearted. It requires a wide range of technical and linguistic based knowledge that is often difficult to find and develop within organisations. Consequently, only the biggest organisations have the financial muscle to invest and reap the awards of MT. This puts the small-to-medium sized organisations at a distinct disadvantage. KantanMT changes everything! KantanMT is a cloud-based implementation of MOSES which enables SMEs to embrace the advantages of MT - quickly and economically. This presentation will demonstrate the KantanMT approach to rapid engine training and tuning, data analytics used to predict MT quality and create tiered pricing structures and instantaneous engine deployment - all of which are driving the new MT Revolution!
Tony (Chief Architect, KantanMT.com) opens the proceedings with a temporal look at how MT technology has progressed. While embracing Rule Based MT in the 1970s, the industry switched over to Statistical MT around 2002 and is now faced with a new paradigm of Neural MT in 2016. For each technology progression, improved translation quality and fluency were achieved.
Summary: https://www.youtube.com/watch?v=19yyDa6mAsc
Full video: https://www.youtube.com/watch?v=EtbML0DTNHk
ILUG 2008 - The future of Notes & Domino Reporting - Let your Notes data rock...Synaptris Inc.
Here is the first part of the presentation made by Synaptris at the ILUG 2008 Conference titled “The future of Notes & Domino reporting. Make your Notes data rock!” on June 4, 2008. This presentation takes you through the case study of Orange Romania, IntelliPRINT customer, and explains how they revolutionized the way they look at Lotus Notes & Domino data and achieved 80% savings in IT time, 15% reduction in overall IT overhead and RoI within 12 weeks of deploying IntelliPRINT Reporting.
The other 3 parts of the Synaptris session at ILUG 08 will be soon uploaded here.
Manoj Yadav has over 7.5 years of experience delivering solutions using IBM's Tivoli portfolio including Netcool, Cognos, and Maximo. He has experience designing and developing automated cell outage reporting processes, developing customized reports, and implementing monitoring solutions. He is proficient in technologies like Cognos, Netcool, Oracle, SQL Server, and Linux/Windows and holds certifications in Cognos Report Author and Netcool Administrator.
- The document provides a summary of S Hariharan's professional experience including over 20 years of experience in Business Intelligence, Data Warehousing, ERP implementations, and managing teams of 3-5 members.
- Some of the key tools and technologies he has experience with include IBM Cognos TM1, Cognos BI, SQL Server, Infor Sun Systems, and SSIS.
- Currently he works as a Senior Consultant at Tekacademy Labs providing consulting services for Hindustan Unilever and Unilever Sri Lanka on their IBM Cognos TM1 and Cognos BI implementations.
The document discusses IBM's Z strategy and digital transformation model. It highlights how IBM Z continues to drive the global economy by processing billions of daily transactions. It also outlines IBM's digital transformation model for clients, which includes exposing APIs to enable apps and data, evolving to automate delivery pipelines, optimizing with analytics, and predicting and responding to service interruptions. The model is meant to help clients address digital transformation needs, leverage existing IBM Z assets to accelerate transformation, and achieve business and technical goals.
The document summarizes the MMT project, which aims to deliver a large-scale commercial online machine translation service based on a new open-source distributed architecture. The key points are:
1. MMT received 3M Euros in funding from Horizon 2020 from 2015-2017 with the goal of developing a next-generation machine translation system.
2. The MMT system is intended to be easier for users to set up and use for translation compared to existing systems, allowing users to directly connect translation memories and start translating.
3. MMT utilizes data pooling from various sources, including MyMemory, the TAUS Data Cloud, and CommonCrawl, with over 785 million words and 423 million segments
The document summarizes the Metadata Machine project at Yle, the Finnish Broadcasting Company. The project tested various automated metadata services to understand how they could enhance metadata creation workflows and generate metadata for archive collections currently lacking it. Services tested included OCR, speech-to-text, facial detection, audio classification, and logo detection. The project identified basic analysis bundles for audio and video, and concluded that focusing on specific use cases is important to define how technology will be used and what success looks like. Defining use cases helps determine what to measure beyond just technical performance.
This document summarizes an MT evaluation report for translating from English to an unspecified language for a company. It analyzes the performance of 5 customized and 12 stock NMT engines. Key findings include:
1) All engines improved with customization, especially Custom NMT 2 which showed impressive gains but also some omissions.
2) The top-performing engines were ModernMT Custom, SDL Stock, Globalese Custom, Google Custom, and DeepL Stock.
3) Different types of challenging segments were identified for further analysis, including hard segments, controversial segments, and weak spots among engines.
4) Human evaluation of sample segments found ModernMT Custom and Google Custom produced translations requiring the least post
MINDs Lab provides an AI platform called MAUM AI that offers various AI services through modules like Brain, M2U, MLT, BOT, and API. The platform allows businesses to integrate AI services into their operations in a plug-and-play manner without having to manage complex systems. It also ensures technologies are always up-to-date through continuous upgrades. Some applications of MAUM AI include MINDs Chatbot, MINDs VOC/RS/QC, and MINDs English.
Gestión proyectos traducción en la Universitat Autònoma de BarcelonaManuel Herranz
Descripción del funcionamiento de una empresa de traducción, departamentos y procesos, tomando a www.pangeanic.es como ejemplo. Descripción de funciones, normas y flujo de trabajo con un énfasis en los procesos de traducción automática.
Gestión proyectos traducción - Universitat Autònoma de BarcelonaManuel Herranz
Presentación sobre el modelo de gestión de proyectos en una empresa de traducción, sirviendo www.pangeanic.es como ejemplo. Descripción de departamentos y procesos.
Integrating Service Mesh with Kubernetes-based connected vehicle platformJun Kai Yong
This document summarizes a presentation by two engineers from DENSO Corporation about their development of a Kubernetes-based connected vehicle platform prototype called Misaki. They introduce Misaki's orchestrator for deploying and managing applications across edge and cloud, as well as its service mesh for handling network issues. While Misaki addresses many challenges of developing vehicle applications, the engineers note there is still work needed to support additional protocols, improve flexibility, and minimize resource usage on edge devices. They invite the audience to follow their continued work on Misaki.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
This presentation provides valuable insights into effective cost-saving techniques on AWS. Learn how to optimize your AWS resources by rightsizing, increasing elasticity, picking the right storage class, and choosing the best pricing model. Additionally, discover essential governance mechanisms to ensure continuous cost efficiency. Whether you are new to AWS or an experienced user, this presentation provides clear and practical tips to help you reduce your cloud costs and get the most out of your budget.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Letter and Document Automation for Bonterra Impact Management (fka Social Sol...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on automated letter generation for Bonterra Impact Management using Google Workspace or Microsoft 365.
Interested in deploying letter generation automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Dive into the realm of operating systems (OS) with Pravash Chandra Das, a seasoned Digital Forensic Analyst, as your guide. 🚀 This comprehensive presentation illuminates the core concepts, types, and evolution of OS, essential for understanding modern computing landscapes.
Beginning with the foundational definition, Das clarifies the pivotal role of OS as system software orchestrating hardware resources, software applications, and user interactions. Through succinct descriptions, he delineates the diverse types of OS, from single-user, single-task environments like early MS-DOS iterations, to multi-user, multi-tasking systems exemplified by modern Linux distributions.
Crucial components like the kernel and shell are dissected, highlighting their indispensable functions in resource management and user interface interaction. Das elucidates how the kernel acts as the central nervous system, orchestrating process scheduling, memory allocation, and device management. Meanwhile, the shell serves as the gateway for user commands, bridging the gap between human input and machine execution. 💻
The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.