Simple machine learning for the masses - Konstantin DavydovPAPIs.io
Using Google's Cloud Machine Learning Services, users can set up an entire Machine Learning pipeline quickly and with limited or no Machine Learning expertise. It is also possible to build applications on top of the Prediction API that allow for non-technical users to leverage the power of Machine Learning to help solve real world problems.
By using black-box Machine Learning via Google’s Machine Learning Services, it is possible to build an end-to-end Machine Learning pipeline with little to no ML expertise. The service automatically handles complex tasks such as data preprocessing, feature selection, classifier selection, parameter tuning, model evaluation, model hosting, and model updating.
As an example of the type of apps that can be built on top of the Prediction API, SmartAutofill spreadsheets add-on allows for easy, one-click application of Machine Learning directly from a Google spreadsheet.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification) using a publicly available aircraft engine run-to-failure data set, and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.
Digital simulation tools enable designers to virtually prototype industrial controls engineering, by Don Talend, brand storytelling, content management, and content strategy expert. Industrial controls industry
LabVIEW tutorial for control systems design. Control design involves developing mathematical models that describe
a physical system, analyzing the models to learn about their dynamic
characteristics, and creating a controller to achieve certain dynamic
characteristics.
Control systems in engineering contain an arrangement of physical
components related in a way that commands, directs, or regulates the
physical system. The controlled physical system also is known as the plant.
In this manual, the control system refers to the sensors, the controller, and
the actuators. In a
closed-loop system, the control system monitors the outputs of the plant
and adjusts the inputs to the plant to make the actual response closer to
the desired response. Closed-loop systems also are known as feedback
systems.
To better understand a closed-loop system, consider a control system
that regulates the temperature of a room. The thermometer reads the
temperature of the room. Based on the desired temperature, the thermostat
turns on the heater or the air conditioner. In this example, the room is the
plant, the thermometer is the sensor, the thermostat is the controller, and the
heater or air conditioner is the actuator.
Database@Home : The Future is Data DrivenTammy Bednar
These slides were presented during the Database@Home : Data-Driven Apps event. This session will discuss the importance of data to an organisation and the need to build applications where the value within that data can easily be exploited. To achieve that aim we need to start building applications that benefit from the flexibility of new development paradigms but don't create artificial barriers of complexity that stop us from easily responding to change within our organisations.
Simple machine learning for the masses - Konstantin DavydovPAPIs.io
Using Google's Cloud Machine Learning Services, users can set up an entire Machine Learning pipeline quickly and with limited or no Machine Learning expertise. It is also possible to build applications on top of the Prediction API that allow for non-technical users to leverage the power of Machine Learning to help solve real world problems.
By using black-box Machine Learning via Google’s Machine Learning Services, it is possible to build an end-to-end Machine Learning pipeline with little to no ML expertise. The service automatically handles complex tasks such as data preprocessing, feature selection, classifier selection, parameter tuning, model evaluation, model hosting, and model updating.
As an example of the type of apps that can be built on top of the Prediction API, SmartAutofill spreadsheets add-on allows for easy, one-click application of Machine Learning directly from a Google spreadsheet.
[Tutorial] building machine learning models for predictive maintenance applic...PAPIs.io
This talk introduces the landscape and challenges of predictive maintenance applications in the industry, illustrates how to formulate (data labeling and feature engineering) the problem with three machine learning models (regression, binary classification, multi-class classification) using a publicly available aircraft engine run-to-failure data set, and showcases how the models can be conveniently trained and compared with different algorithms in Azure ML.
Digital simulation tools enable designers to virtually prototype industrial controls engineering, by Don Talend, brand storytelling, content management, and content strategy expert. Industrial controls industry
LabVIEW tutorial for control systems design. Control design involves developing mathematical models that describe
a physical system, analyzing the models to learn about their dynamic
characteristics, and creating a controller to achieve certain dynamic
characteristics.
Control systems in engineering contain an arrangement of physical
components related in a way that commands, directs, or regulates the
physical system. The controlled physical system also is known as the plant.
In this manual, the control system refers to the sensors, the controller, and
the actuators. In a
closed-loop system, the control system monitors the outputs of the plant
and adjusts the inputs to the plant to make the actual response closer to
the desired response. Closed-loop systems also are known as feedback
systems.
To better understand a closed-loop system, consider a control system
that regulates the temperature of a room. The thermometer reads the
temperature of the room. Based on the desired temperature, the thermostat
turns on the heater or the air conditioner. In this example, the room is the
plant, the thermometer is the sensor, the thermostat is the controller, and the
heater or air conditioner is the actuator.
Database@Home : The Future is Data DrivenTammy Bednar
These slides were presented during the Database@Home : Data-Driven Apps event. This session will discuss the importance of data to an organisation and the need to build applications where the value within that data can easily be exploited. To achieve that aim we need to start building applications that benefit from the flexibility of new development paradigms but don't create artificial barriers of complexity that stop us from easily responding to change within our organisations.
Den Datenschatz heben und Zeit- und Energieeffizienz steigern: Mathematik und...Joachim Schlosser
In einer Gesellschaft, in der das Sammeln von personenbezogenen Daten mittlerweile alltäglich geworden ist, ist es nicht weiter verwunderlich, dass auch der innovative Maschinenbauer Daten sammelt, wo er nur kann. Produktdaten, Maschinendaten, Statistikdaten – in einer durchschnittlichen Produktionsanlage fallen bereits heute jeden Tag Gigabytes an Daten an. „Big Data“ wurde eines der Schlagworte der Industrie 4.0.
Doch was verspricht man sich davon? Welche Information steckt in den aufgezeichneten Maschinen- und Produktdaten? Und wie erfolgt die Auswertung?
Im Rahmen des Vortrags wird aufgezeigt, wie Unternehmen auf Basis einer etablierten Plattform wie MATLAB® ihre Auswertealgorithmen entwickeln, testen und ausrollen können. Die kontinuierliche Auswertung selbst erfolgt dann wahlweise auf einem Anlagenserver oder aber auch in Echtzeit direkt an der Maschine. Veranschaulicht wird dies anhand von Beispielen aus der Praxis.
Doch neben der gesammelten Daten kommt auch den Steuerungseinheiten in der Produktion in der Industrie 4.0 eine größere Bedeutung zu.
Wenn Werkstücke demnächst selbst wissen, wo sie im Produktionsablauf hin möchten und welcher Verarbeitungsschritt ihnen angedeihen soll, dann bedeutet das auch für die einzelnen Komponenten und Module in Produktion und Logistik ein mehr an Funktionalität, da sie auf diese Eingaben ebenfalls reagieren sollen.
Wie stellen Sie sicher, dass diese zusätzliche Funktionalität nicht zu Lasten der Energiebilanz gehen? Wie fahren Sie die Motoren und anderen aktiven Komponenten Ihrer Fertigung so, dass sie flexibel auf veränderte Routen der Werkstücke reagieren und dennoch im optimalen Bereich fahren?
Mehr denn je brauchen Sie gesteuerte und geregelte Komponenten und Module. Das sollte schon seit Industrie 3.0 vorhanden sein, jedoch ist auch hier noch viel ganz konkretes Potential zur Steigerung von Produktivität und Einsparung von Energie und Produktionszeit vorhanden.
Sie sehen im Vortrag, wie Sie ihre Komponenten besser beschalten, dass die vernetzten dynamischen Anforderungen von Industrie 4.0 lokal effizient umgesetzt werden können.
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/1...MLconf
Recommendations for Building Machine Learning Software: Building a real system that uses machine learning can be a difficult both in terms of the algorithmic and engineering challenges involved. In this talk, I will focus on the engineering side and discuss some of the practical lessons we’ve learned from years of developing the machine learning systems that power Netflix. I will go over what it takes to get machine learning working in a real-life feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. This involves lessons around challenges such as where to place algorithmic components, how to handle distribution and parallelism, what kinds of modularity are useful, how to support both production experimentation, and how to test machine learning systems.
Inteligencia de Negocios en SQL Server 2008 y Minería de Datos.
Ing. Eduardo Castro Martinez, PhD
Microsoft SQL Server MVP
http://ecastrom.blogspot.com
http://comunidadwindows.org
How Accurate is Future Facilities 6Sigma DCXRobert Schmidt
We know that our customers need accurate results that they can rely on to make critical decisions. We understand these challenges, and we’ve worked hard to ensure that 6SigmaDCX achieves this. Since the first release of the software, we’ve used the expertise of our internal development and engineering teams to develop room-scale models and their individual components. Furthermore, independent audits have been carried out by R&D establishments and educational bodies to validate the 6SigmaDCX software. The results of these audits, along with positive messages from end users, show that 6SigmaDCX provides the accuracy needed to make critical decisions about your facility’s performance.
Training report on Embedded Systems and MATLABAswin Sreeraj
An embedded system is a computer system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints. It is embedded as part of a complete device often including hardware and mechanical parts. Embedded systems control many devices in common use today.
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language.
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
ARC's Greg Gorbach Rapid Product Innovation Presentation @ ARC Industry Forum...ARC Advisory Group
Rapid Product Innovation: Improving Processes for Production System Design Implementation and Design, Implementation, Operations
ARC's Greg Gorbach Rapid Product Innovation Presentation @ ARC Industry Forum 2010 in Orlando, FL.
For certain manufacturing segments (especially discrete and portions of hybrid), introducing new products or improving the manufacturing process usually requires creating or modifying production systems. Many production system problems are not discovered until late in the design/implementation process, which introduces delays and cost. Once in operation, virtual reference models, if available, could aid in performance monitoring, process optimization, problem diagnosis, operator training, and continuous improvement
Presentation given on the 21st of September 2021 at the London Beam Meet-up
Event website: https://www.meetup.com/London-Apache-Beam-Meetup/events/280442419/
"using sagemaker to build and deploy ml models in production" - MJ Berends AW...AWS Chicago
"using sagemaker to build and deploy ml models in production" - MJ Berends, Data Engineer at ActiveCampaign
"using sagemaker to build and deploy ml models in production" - MJ Berends AWS Chicago user group June 6 2019
What is the Use of the Simulink Function in Matlab Assignment.pptxAssignment World
Simulink in MATLAB is a powerful tool for modeling, simulating, and analyzing dynamic systems. It is particularly useful for engineers and researchers working on control systems, signal processing, and other multidomain applications. Simulink enables users to visually design models and simulate them in a graphical environment, making complex systems easier to understand and work with.
Den Datenschatz heben und Zeit- und Energieeffizienz steigern: Mathematik und...Joachim Schlosser
In einer Gesellschaft, in der das Sammeln von personenbezogenen Daten mittlerweile alltäglich geworden ist, ist es nicht weiter verwunderlich, dass auch der innovative Maschinenbauer Daten sammelt, wo er nur kann. Produktdaten, Maschinendaten, Statistikdaten – in einer durchschnittlichen Produktionsanlage fallen bereits heute jeden Tag Gigabytes an Daten an. „Big Data“ wurde eines der Schlagworte der Industrie 4.0.
Doch was verspricht man sich davon? Welche Information steckt in den aufgezeichneten Maschinen- und Produktdaten? Und wie erfolgt die Auswertung?
Im Rahmen des Vortrags wird aufgezeigt, wie Unternehmen auf Basis einer etablierten Plattform wie MATLAB® ihre Auswertealgorithmen entwickeln, testen und ausrollen können. Die kontinuierliche Auswertung selbst erfolgt dann wahlweise auf einem Anlagenserver oder aber auch in Echtzeit direkt an der Maschine. Veranschaulicht wird dies anhand von Beispielen aus der Praxis.
Doch neben der gesammelten Daten kommt auch den Steuerungseinheiten in der Produktion in der Industrie 4.0 eine größere Bedeutung zu.
Wenn Werkstücke demnächst selbst wissen, wo sie im Produktionsablauf hin möchten und welcher Verarbeitungsschritt ihnen angedeihen soll, dann bedeutet das auch für die einzelnen Komponenten und Module in Produktion und Logistik ein mehr an Funktionalität, da sie auf diese Eingaben ebenfalls reagieren sollen.
Wie stellen Sie sicher, dass diese zusätzliche Funktionalität nicht zu Lasten der Energiebilanz gehen? Wie fahren Sie die Motoren und anderen aktiven Komponenten Ihrer Fertigung so, dass sie flexibel auf veränderte Routen der Werkstücke reagieren und dennoch im optimalen Bereich fahren?
Mehr denn je brauchen Sie gesteuerte und geregelte Komponenten und Module. Das sollte schon seit Industrie 3.0 vorhanden sein, jedoch ist auch hier noch viel ganz konkretes Potential zur Steigerung von Produktivität und Einsparung von Energie und Produktionszeit vorhanden.
Sie sehen im Vortrag, wie Sie ihre Komponenten besser beschalten, dass die vernetzten dynamischen Anforderungen von Industrie 4.0 lokal effizient umgesetzt werden können.
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/1...MLconf
Recommendations for Building Machine Learning Software: Building a real system that uses machine learning can be a difficult both in terms of the algorithmic and engineering challenges involved. In this talk, I will focus on the engineering side and discuss some of the practical lessons we’ve learned from years of developing the machine learning systems that power Netflix. I will go over what it takes to get machine learning working in a real-life feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. This involves lessons around challenges such as where to place algorithmic components, how to handle distribution and parallelism, what kinds of modularity are useful, how to support both production experimentation, and how to test machine learning systems.
Inteligencia de Negocios en SQL Server 2008 y Minería de Datos.
Ing. Eduardo Castro Martinez, PhD
Microsoft SQL Server MVP
http://ecastrom.blogspot.com
http://comunidadwindows.org
How Accurate is Future Facilities 6Sigma DCXRobert Schmidt
We know that our customers need accurate results that they can rely on to make critical decisions. We understand these challenges, and we’ve worked hard to ensure that 6SigmaDCX achieves this. Since the first release of the software, we’ve used the expertise of our internal development and engineering teams to develop room-scale models and their individual components. Furthermore, independent audits have been carried out by R&D establishments and educational bodies to validate the 6SigmaDCX software. The results of these audits, along with positive messages from end users, show that 6SigmaDCX provides the accuracy needed to make critical decisions about your facility’s performance.
Training report on Embedded Systems and MATLABAswin Sreeraj
An embedded system is a computer system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints. It is embedded as part of a complete device often including hardware and mechanical parts. Embedded systems control many devices in common use today.
MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language.
AI Solutions for Industries | Quality Inspection | Data Insights | AI-accelerated CFD | Self-Checkout | byteLAKE.com
byteLAKE: Empowering Industries with AI Solutions. Embrace cutting-edge technology for advanced quality inspection, data insights, and more. Harness the potential of our CFD Suite, accelerating Computational Fluid Dynamics for heightened productivity. Unlock new possibilities with Cognitive Services: image analytics for precise visual inspection for Manufacturing, sound analytics enabling proactive maintenance for Automotive, and wet line analytics for the Paper Industry. Seamlessly convert data into actionable insights using Data Insights' AI module, enabling advanced predictive maintenance and risk detection. Simplify Restaurant and Retail operations with our efficient self-checkout solution, recognizing meals and groceries and elevating customer satisfaction. Custom AI Development services available for tailored solutions. Discover more at www.byteLAKE.com.
► byteLAKE's CFD Suite: Accelerate your Computational Fluid Dynamics (CFD) simulations by leveraging the speed and efficiency of artificial intelligence. Slash simulation times, minimize trial-and-error costs, and supercharge decision-making for heightened productivity. Learn more at www.byteLAKE.com/en/CFDSuite.
ARC's Greg Gorbach Rapid Product Innovation Presentation @ ARC Industry Forum...ARC Advisory Group
Rapid Product Innovation: Improving Processes for Production System Design Implementation and Design, Implementation, Operations
ARC's Greg Gorbach Rapid Product Innovation Presentation @ ARC Industry Forum 2010 in Orlando, FL.
For certain manufacturing segments (especially discrete and portions of hybrid), introducing new products or improving the manufacturing process usually requires creating or modifying production systems. Many production system problems are not discovered until late in the design/implementation process, which introduces delays and cost. Once in operation, virtual reference models, if available, could aid in performance monitoring, process optimization, problem diagnosis, operator training, and continuous improvement
Presentation given on the 21st of September 2021 at the London Beam Meet-up
Event website: https://www.meetup.com/London-Apache-Beam-Meetup/events/280442419/
"using sagemaker to build and deploy ml models in production" - MJ Berends AW...AWS Chicago
"using sagemaker to build and deploy ml models in production" - MJ Berends, Data Engineer at ActiveCampaign
"using sagemaker to build and deploy ml models in production" - MJ Berends AWS Chicago user group June 6 2019
What is the Use of the Simulink Function in Matlab Assignment.pptxAssignment World
Simulink in MATLAB is a powerful tool for modeling, simulating, and analyzing dynamic systems. It is particularly useful for engineers and researchers working on control systems, signal processing, and other multidomain applications. Simulink enables users to visually design models and simulate them in a graphical environment, making complex systems easier to understand and work with.
