Slides used on the event [Getting Started with Machine Learning.Net & Windows Machine Learning] hosted on November 22, in the Mississauga .NET User Group
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...Bruno Capuano
Slides used during the session [Getting Started with Machine Learning .Net and Windows Machine Learning [ML.Net & WinML]] on Kitchener Ontario, on 26 Sept 2018 for the Canada's Technology Triangle .Net User Group
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
This presentation provides an overview of the technology with demos run in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and F# and run in Visual Studio Community 2019. This technology is ready for production implementation and runs on .NET Core.
This presentation is the first of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
201906 02 Introduction to AutoML with ML.NET 1.0Mark Tabladillo
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
“Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019.
This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
NimbusML enables data scientists to use ML.NET to train models in Azure Machine Learning or anywhere else they use Python. NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices. The components are authored by the team members, as well as numerous contributors from MSR, CISL, Bing and other teams at Microsoft. NimbusML is interoperable with scikit-learn estimators and transforms, while adding a suite of highly optimized algorithms written in C++ and C# for speed and performance.
The trained machine learning model can be used in a .NET application with ML.NET. This presentation will outline the features of NimbusML and provide a notebook-based demonstration using Azure Notebooks.
This presentation is the third of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
2018 11 14 Artificial Intelligence and Machine Learning in AzureBruno Capuano
Slides used during my session "Artificial Intelligence and Machine Learning in Azure" for The Azure Group (Canada's Azure User Community) on November 14 2018.
Public group
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
2018 09 26 CTT .NET User Group - Introduction to Machine Learning.Net and Win...Bruno Capuano
Slides used during the session [Getting Started with Machine Learning .Net and Windows Machine Learning [ML.Net & WinML]] on Kitchener Ontario, on 26 Sept 2018 for the Canada's Technology Triangle .Net User Group
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
This presentation provides an overview of the technology with demos run in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and F# and run in Visual Studio Community 2019. This technology is ready for production implementation and runs on .NET Core.
This presentation is the first of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
201906 02 Introduction to AutoML with ML.NET 1.0Mark Tabladillo
ML.NET 1.0 release is the first major milestone of a great journey that started in May 2018 when we released ML.NET 0.1 as open source. ML.NET is an open-source and cross-platform machine learning framework for .NET developers. Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more.
“Automated ML” is a collection of new technologies from Microsoft to enhance the data science development process. Still in preview, Auto ML for ML.NET 1.0 will be demonstrated in a Deep Learning Virtual Machine running Windows Server 2016. Code examples are in C# and run in Visual Studio Community 2019.
This presentation is the second of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
NimbusML enables data scientists to use ML.NET to train models in Azure Machine Learning or anywhere else they use Python. NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices. The components are authored by the team members, as well as numerous contributors from MSR, CISL, Bing and other teams at Microsoft. NimbusML is interoperable with scikit-learn estimators and transforms, while adding a suite of highly optimized algorithms written in C++ and C# for speed and performance.
The trained machine learning model can be used in a .NET application with ML.NET. This presentation will outline the features of NimbusML and provide a notebook-based demonstration using Azure Notebooks.
This presentation is the third of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
2018 11 14 Artificial Intelligence and Machine Learning in AzureBruno Capuano
Slides used during my session "Artificial Intelligence and Machine Learning in Azure" for The Azure Group (Canada's Azure User Community) on November 14 2018.
Public group
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI.
This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
This presentation is the fourth of four related to ML.NET and Automated ML. The presentation will be recorded with video posted to this YouTube Channel: http://bit.ly/2ZybKwI
Microsoft has released Automated ML technologies for developers through ML.NET, Azure ML Service, and Azure Databricks. This presenter is a data scientist and Microsoft architect, and will give a comprehensive overview of the utility and use case of this automated technology for production solutions. The presentation includes code you can try now.
DataWeekender 4_2 Cosmos DB and Azure Functions- A serverless database proces...Luis Beltran
Slides of my presentation about Serverless database processing using Azure Functions and Cosmos DB to build an API for CRUD operations at Data Weekender 4.2 event
Writing Machine Learning code is now possible with .NET native library ML.NET that has recently reached 1.0 milestole. Let's look what we can do with this lib, which scenarios can be handled.
Leverage the power of machine learning on windowsMia Chang
Note:
The Content was modified from the Microsoft Content team.
Deck Owner: Nitah Onsongo
Tech/Msg Review: Cesar De La Torre, Simon Tao, Clarke Rahrig
---
Event: Insider Dev Tour Berlin
Event Description: Microsoft is going on a world tour with the announcements of Build 2019. The Insider Dev Tour focuses on innovations related to Microsoft 365 from a developer's perspective.
Date: June 7th, 2019
Event link: https://www.microsoft.com/de-de/techwiese/news/best-of-build-insider-dev-tour-am-7-juni-in-berlin.aspx
Linkedin: http://linkedin.com/in/mia-chang/
How to use Azure Machine Learning service to manage the lifecycle of your models. Azure Machine Learning uses a Machine Learning Operations (MLOps) approach, which improves the quality and consistency of your machine learning solutions.
Introduction to Machine learning and Deep LearningNishan Aryal
Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.
Slide deck used to introduce machine learning with Azure Machine Learning Service. Focus on deployment of models with the machine learning SDK and consumption of the models with Python and Go.
Introduction to Azure Machine Learning describes the purpose of Azure Machine Learning, and introduces the main features of Azure Machine Learning Studio.
John Robert: Making your machine learning model usable by othersLviv Startup Club
John Robert: Making your machine learning model usable by others
Data Science Online Camp 2021
Website - https://dscamp.org/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/Data-Science-Camp-103012708431833
Lo scorso 10 ottobre si è tenuto presso il Politecnico di Torino l'SQL Saturday #454.
Per noi di SolidQ c'era Davide Mauri che, in quanto Microsoft SQL Server MVP, ha tenuto una sessione su Azure Machine Learning.
Ecco la presentazione in 23 slides.
Apple makes it really easy to get started with Machine Learning as a developer. See how you can easily use Create ML and Turi Create to train Machine Learning models and use them in your iOS apps.
Webinar GLUGNet - Machine Learning.Net and Windows Machine LearningBruno Capuano
Slides used during the webinar session on Machine Learning.Net and Windows Machine Learning on 2019 02 21 for the GLUGnet User Group for .NET, Web, Mobile, Database
Vamos explorar como podemos utilizar aprendizagem de máquina, de forma fácil, nas aplicações que desenvolvemos no dia a dia utilizando nossas habilidades em .NET através do ML.NET, um framework open source e cross-platform!
Microsoft has released Automated ML technologies for developers through ML.NET, Azure ML Service, and Azure Databricks. This presenter is a data scientist and Microsoft architect, and will give a comprehensive overview of the utility and use case of this automated technology for production solutions. The presentation includes code you can try now.
DataWeekender 4_2 Cosmos DB and Azure Functions- A serverless database proces...Luis Beltran
Slides of my presentation about Serverless database processing using Azure Functions and Cosmos DB to build an API for CRUD operations at Data Weekender 4.2 event
Writing Machine Learning code is now possible with .NET native library ML.NET that has recently reached 1.0 milestole. Let's look what we can do with this lib, which scenarios can be handled.
Leverage the power of machine learning on windowsMia Chang
Note:
The Content was modified from the Microsoft Content team.
Deck Owner: Nitah Onsongo
Tech/Msg Review: Cesar De La Torre, Simon Tao, Clarke Rahrig
---
Event: Insider Dev Tour Berlin
Event Description: Microsoft is going on a world tour with the announcements of Build 2019. The Insider Dev Tour focuses on innovations related to Microsoft 365 from a developer's perspective.
Date: June 7th, 2019
Event link: https://www.microsoft.com/de-de/techwiese/news/best-of-build-insider-dev-tour-am-7-juni-in-berlin.aspx
Linkedin: http://linkedin.com/in/mia-chang/
How to use Azure Machine Learning service to manage the lifecycle of your models. Azure Machine Learning uses a Machine Learning Operations (MLOps) approach, which improves the quality and consistency of your machine learning solutions.
Introduction to Machine learning and Deep LearningNishan Aryal
Overview of Machine Learning and Deep Learning. Brief introduction to different types of BI Reporting tools like Power BI, SSMS, Cortana, Azure ML, TenserFlow and other tools.
Slide deck used to introduce machine learning with Azure Machine Learning Service. Focus on deployment of models with the machine learning SDK and consumption of the models with Python and Go.
Introduction to Azure Machine Learning describes the purpose of Azure Machine Learning, and introduces the main features of Azure Machine Learning Studio.
John Robert: Making your machine learning model usable by othersLviv Startup Club
John Robert: Making your machine learning model usable by others
Data Science Online Camp 2021
Website - https://dscamp.org/
Youtube - https://www.youtube.com/startuplviv
FB - https://www.facebook.com/Data-Science-Camp-103012708431833
Lo scorso 10 ottobre si è tenuto presso il Politecnico di Torino l'SQL Saturday #454.
Per noi di SolidQ c'era Davide Mauri che, in quanto Microsoft SQL Server MVP, ha tenuto una sessione su Azure Machine Learning.
Ecco la presentazione in 23 slides.
Apple makes it really easy to get started with Machine Learning as a developer. See how you can easily use Create ML and Turi Create to train Machine Learning models and use them in your iOS apps.
Webinar GLUGNet - Machine Learning.Net and Windows Machine LearningBruno Capuano
Slides used during the webinar session on Machine Learning.Net and Windows Machine Learning on 2019 02 21 for the GLUGnet User Group for .NET, Web, Mobile, Database
Vamos explorar como podemos utilizar aprendizagem de máquina, de forma fácil, nas aplicações que desenvolvemos no dia a dia utilizando nossas habilidades em .NET através do ML.NET, um framework open source e cross-platform!
As Machine learning reaches the mainstream, new tools available to developers makes it possible to implement machine-learning features—voice, face, and image recognition; personalized recommendations; and more—in a mobile context.
TensorFlow Lite applies many techniques for achieving low latency; optimizing the kernels for mobile apps, pre-fused activations, and quantized kernels that allow smaller and faster (fixed-point math) models.
Kostiantyn Bokhan, N-iX. CD4ML based on Azure and KubeflowIT Arena
Kostiantyn Bokhan, a technical lead at N-IX, focuses on data science projects. He leads data science projects in several areas: Computer vision, NLP, and signal processing as well as consults clients regarding digital transformations with AI. When free, he conducts research in the deep machine learning area. Kostiantyn has been an associate professor and faculty member of several universities since 2002. His research focuses on machine learning, deep learning, signal, and image processing. He received a PhD degree in network and telecommunications systems with research in digital signal processing in 2013. He has served on the scientific committees and review boards of several conferences.
Speech Overview:
Applying machine learning to make business applications and services intelligent is more than just training models and serving them. It requires implementing end-to-end and continuously repeatable cycles of training, testing, deploying, monitoring, and operating the models. Continuous delivery for machine learning (CD4ML) is a technique that enables reliable end-to-end cycles of development, deploying, and monitoring machine learning models. There are a lot of tools and frameworks that can be used to implement CD4ML. One of them is Kubeflow. Our experience of using Kubeflow for implementing CD4ML for the manufacturing area based on Azure Kubernetes service will be described in this speech.
Presentation to the Silverlight User Group in London on October 12th to provide a round-up of the recent BUILD conference in LA and an introduction to Windows 8 and the Windows Runtime.
DataPalooza at the San Francisco Loft: In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
by Mahendra Bairagi, AI Specialist Solutions Architect, AWS
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun. You are looking to use Machine Learning and IoT technologies to solve this unique problem.
Do you accept the Challenge?
The objective of this task is to help the festival-goers quickly identify the DJ stage where crowd is the happiest. You've seen a lot of buzz around computer vision, machine learning, and IoT and want to use this technology to detect crowd emotions. From your initial research there are existing ML models that you can leverage to do face and emotion detection, but there are two ways that the predictions (inference) can be done; on the cloud and on the camera itself, but which one will work the best for your needs at the festival? You are going to test both approaches and find out!
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Microsoft is working hard to make Artificial Intelligence available to everyone. We not only infuse AI in our products but also give you the platform to build your very own solution, that you are a developer, a citizen data scientist or a hard core data scientist.
As the CTO of a new startup, you have taken up a challenge of improving the EDM music festival experience. At venues with multiple stages, festival-goers are always looking to identify DJ stage areas with the liveliest atmosphere. This causes them to constantly move around between different stages and miss out on having fun.
In this workshop you will use AWS and Intel technologies to learn how to build, deploy, and run ML inference on the cloud as well as on the IoT Edge. You will learn to use Amazon SageMaker with Intel C5 Instances, AWS DeepLens, AWS Greengrass, Amazon Rekognition, and AWS Lambda to build an end-to-end IoT solution that performs machine learning.
Here's a high level overview of what motivates many AI teams at Google, what gives us confidence that humans will solve intelligence, the recent impact of advances in this work, and some examples of how people can get started today... for free! I first gave this talk to recipients of the 2019 AI for Good Awards, then again to recipients of the 2019 NASA FDL Challenge Fellowships. The slides are mainly a backdrop, but people still seemed to want a copy.
Here are the backdrop slides to my recent SIFMA talk, "Advanced AI for People in a Hurry." We talk about the advent of deep learning and the rapid rise of software that can see, read, hear, speak and create... often better than humans. I end with a few examples of how people can get started today with offerings from Google.
Global AI on Virtual Tour Oslo - Anomaly Detection using ML.Net on a drone te...Bruno Capuano
Slides used during the session "Anomaly Detection using ML.Net on a drone telemetry from Azure IoT" for the Global AI on Virtual Tour - Oslo on June 2021
2020 08 06 Global XR Talks - Lessons Learned creating a multiplatform AI proj...Bruno Capuano
Slides used during the session "Lessons Learned creating a multiplatform AI project for Azure Kinect and Hololens 2" for the Global XR Talks on the 2020 08 06
2020 06 13 Best of Build 2020 - Canada Community Edition - Artificial Intelli...Bruno Capuano
Slides used during the "Best of Build 2020 - Canada Community Edition" for the Artificial Intelligence session. Shared session with Frank Boucheros. More information on my blog.
Global Azure AI Tour Buenos Aires Argentina, Drones and AIBruno Capuano
Slides used during my session "How to fly a drone with 20 lines of code and use some AI" for the Global AI Tour event. Virtual Mode for Buenos Aires, Argentina.
2020 04 18 Global AI On Tour Monterrey - Program a Drone using AIBruno Capuano
Slides used in my online session "¡Vamos a programar a un dron para que siga rostros!" for the Global AI On Tour Monterrey.
El próximo 18 de Abril estará hablando de drones, Inteligencia Artificial, Docker, y otras sorpresas para el evento gratuito de Global AI On Tour Monterrey !
2020 04 09 Global AI Community Virtual Tour - Drones and AIBruno Capuano
Slides used during my session "Let’s code a drone to follow faces! Using AI, Python, containers and more. As a bonus we will some Enterprise scenarios." as part of the Global AI Community Virtual Tour.
Slides used during the virtual conference, NetCoreConf on April 04, 2020. The session was a introduction to Machine Learning for .Net developers, using ML.Net as the main framework.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Getting Started with Machine Learning.Net & Windows Machine Learning
1. Getting Started with
Machine Learning .Net and
Windows Machine Learning
[ ML.Net & WinML ]
Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com
2. why should I care about AI and ML?
As a developer,
3. Some problems are difficult to solve using traditional algorithms and
procedural programming.
4. IBM slaps patent on coffee-delivering drones that can read
your MIND (link)
5. IBM slaps patent on coffee-delivering drones that can read
your MIND (link)
6. “It has exquisite buttons …
with long sleeves …works for
casual as well as business
settings”{f(x) {f(x)
Machine Learning: “Programming the Unprogrammable”
8. Is this A or B? How much? How many? How is this organized?
Regression ClusteringClassification
Machine Learning Tasks
9. Get started with Machine Learning
Prepare Data Build & Train Evaluate
Azure Databricks Azure Machine Learning
Quickly launch and scale Spark on demand
Rich interactive workspace and notebooks
Seamless integration with all Azure data
services
Broad frameworks and tools support:
TensorFlow, Cognitive Toolkit, Caffe2, Keras,
MxNET, PyTorch
In the cloud – on the edge
Docker containers
Windows Machine Learning
12. Azure Machine Learning Services
gives you an end-to-end
solution to prepare data and
train your model in the Cloud.
WinMLTools converts existing
models from CoreML, scikit-
learn, LIBSVM, and XGBoost
Azure Custom Vision makes it
easy to create your own image
models - https://customvision.ai/
Azure AI Gallery curates models
for use with Windows ML -
https://gallery.azure.ai/models
How do I get ONNX models to use in my
application?
13. 1. Developers can focus on their data and
their scenarios, using Windows ML for
model evaluation
2. Enables using ML models trained with a
diverse set of toolkits
3. Hardware acceleration gets fast evaluation
results across the diversity of the entire
Windows device ecosystem.
Windows ML solves three problems for you
Direct3D
GPU
CPU
DirectML
Model Inference Engine
WinML Win32 API
WinML UWP API
Win32 App
WinML Runtime
UWP App
16. Easy / Less Control Full Control / Harder
Vision Speech Language
Knowledge SearchLabs
TextAnalyticsAPI client = new TextAnalyticsAPI();
client.AzureRegion = AzureRegions.Westus;
client.SubscriptionKey = "1bf33391DeadFish";
client.Sentiment(
new MultiLanguageBatchInput(
new List<MultiLanguageInput>()
{
new MultiLanguageInput("en","0",
"This vacuum cleaner sucks so much dirt")
}));
e.g. Sentiment Analysis using Azure Cognitive Services
9% positive
Pre-built ML Models (Azure Cognitive Services)
17. ML.NET is for building custom models
Custom models
Easier / Less Control Harder / Full Control
Pre-built models
TensorFlow
ML.NETVisionSpeech LanguageKnowledge Search
18. Prepare Your Data Build & Train Run
Build your own custom machine learning models
20. Is this A or B? Kid or Baby
Based on the age:
Kid or Baby
Age classes explained
21. And more! Samples @ https://github.com/dotnet/machinelearning-samples
Customer segmentation
Recommendations
Predictive maintenance
Forecasting
Issue Classification
Image classification
Object detection
Sentiment Analysis
A few things you can do with ML.NET …
22. Proven & Extensible Open Source
https://github.com/dotnet/machinelearning
Build your own
Supported on Windows, Linux, and macOS
Developer Focused
ML.NET 0.7.0 (Preview)
Machine Learning framework made for .NET developers
23. Windows 10 (Windows Defender)
Power Point (Design Ideas)
Excel (Chart Recommendations)
Bing Ads (Ad Predictions)
+ moreAzure Stream Analytics (Anomaly Detection)
ML.NET is Proven at scale, enterprise ready
24. ML.NET is a framework for building custom ML Models
27. ML.Net, working with
TensorFlow frozen models
MakeMagicHappen();
https://www.microsoft.com/net/learn/apps/machi
ne-learning-and-ai
28. • API improvements
• Additional ML Tasks and Scenarios
• Improved Deep Learning with TensorFlow
• Scale-out on Azure
• Better GUI to simplify ML tasks
• Improved tooling in Visual Studio
• Improvements for F#
• Language Innovation for .NET
Road Ahead for ML.NET