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.
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.
Introduction to Azure Machine Learning describes the purpose of Azure Machine Learning, and introduces the main features of Azure Machine Learning Studio.
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.
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.
Getting Started with Machine Learning.Net & Windows Machine LearningBruno Capuano
Slides used on the event [Getting Started with Machine Learning.Net & Windows Machine Learning] hosted on November 22, in the Mississauga .NET User Group
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!
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.
Introduction to Azure Machine Learning describes the purpose of Azure Machine Learning, and introduces the main features of Azure Machine Learning Studio.
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.
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.
Getting Started with Machine Learning.Net & Windows Machine LearningBruno Capuano
Slides used on the event [Getting Started with Machine Learning.Net & Windows Machine Learning] hosted on November 22, in the Mississauga .NET User Group
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!
Complete No code solution to Machine Learning using Azure ML Studio. The aim of this presentation is to discuss the capability of Azure ML Studio in enabling any novice to perform ML experiments.
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.
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.
When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed.
As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations.
In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment
Azure Machine Learning 101 slides which I used on Advanced Technology Days conference, held in Zagreb (Croatia) on November 12th and 13th.
Slides are divided into 2 parts. First part is introducing machine learning in a simple way with some basic definitions and basic examples. Second part is introducing Azure Machine Learning service including main features and workflow.
Slides are used only 30% of the presentation time so there is no much detailed information on them regarding machine learning. Rest of the time I did live demos on Azure Machine Learning portal which is probably more interesting to the audience.
Presentation can be useful as a concept for similar topics or to combine it some other resource. If you need access to the demos just send me a message so I will grant you access to Azure ML workspace where are all experiments used in this session.
asp.net using c# notes sem 5 ( we-it tutorials ).
Review of .NET frameworks, Introduction to C#, Variables and expressions, flow controls, functions, debugging and error handling, OOPs with C#, Defining classes and class members.
Assembly, Components of Assembly, Private and Shared Assembly, Garbage Collector, JIT compiler. Namespaces Collections, Delegates and Events. Introduction to ASP.NET 4: Microsoft.NET framework, ASP.NET lifecycle. CSS: Need of CSS, Introduction to CSS, Working with CSS with visual developer.
ASP.NET server controls: Introduction, How to work with button controls, Textboxes, Labels, checkboxes and radio buttons, list controls and other web server controls, web.config and global.asax files. Programming ASP.NET web pages: Introduction, data types and variables, statements, organizing code, object oriented basics.
Validation Control: Introduction, basic validation controls, validation techniques, using advanced validation controls. State Management: Using view state, using session state, using application state, using cookies and URL encoding. Master Pages: Creating master pages, content pages, nesting master pages, accessing master page controls from a content page. Navigation: Introduction to use the site navigation, using site navigation controls.
Databases: Introduction, using SQL data sources, GridView Control, DetailsView and FormView Controls, ListView and DataPager controls, Using object datasources. ASP.NET Security: Authentication, Authorization, Impersonation, ASP.NET provider model
LINQ: Operators, implementations, LINQ to objects,XML,ADO.NET, Query Syntax. ASP.NET Ajax: Introducing AJAX, Working of AJAX, Using ASP.NET AJAX
server controls. JQuery: Introduction to JQuery, JQuery UI Library, Working of JQuery
Simplifying AI and Machine Learning with Watson StudioDataWorks Summit
Are you seeing benefits from big data, AI and machine learning? Some companies are challenged by the complexity of the tools, access to quality data and the ability to operationalize these technologies. IBM’s Watson Studio addresses the needs of developers, data scientists and business analysts – who need to create, train and deploy machine and deep learning models, analyze and visualize data – all in an easy-to-use platform. Watson Studio supports Apple’s Core ML with Watson Visual Recognition service. It provides a suite of tools for data scientists, application developers and subject matter experts to collaboratively and easily work with data and use that data to build, train and deploy models at scale. When coupled with IBM Watson Knowledge Catalog, it enables companies to create a secure catalog of AI assets including datasets, documents and models. In this session, you will learn how to use these new offerings to solve real world business problems and infuse AI into your business to drive innovation.
Speaker
Sumit Goyal, IBM, Software Engineer
Deep AutoViML For Tensorflow Models and MLOps WorkflowsBill Liu
deep_autoviml is a powerful new deep learning library with a very simple design goal: Make it as easy as possible for novices and experts alike to experiment with and build tensorflow.keras preprocessing pipelines and models in as few lines of code as possible.
deep_autoviml will enable data scientists, ML engineers and data engineers to fast prototype tensorflow models and data pipelines for MLOps workflows using the latest TF 2.4+ and keras preprocessing layers. You can now upload your saved model to any Cloud provider and make predictions out of the box since all the data preprocessing layers are attached to the model itself!
In this webinar, we will discuss the problems that deep_AutoViML can solve, its architecture design and demo how to build powerful TF.Keras models on structured data, NLP and Image data domains.
https://www.aicamp.ai/event/eventdetails/W2021080918
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
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.
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Cheryl Hung, ochery.com
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https://alandix.com/academic/papers/synergy2024-epistemic/
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Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...
2018 12 18 Tech Valley UserGroup Machine Learning.Net
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 0.8.0 (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
29. Load Data Extract Features Train Model Evaluate Model
Model
consumption
labels + plain text labels + feature vectors model
End to End ML Workflow
30. Load Data Extract Features Train Model Evaluate Model
Model
consumption
labels + plain text labels + feature vectors
Enter...
in ML.NETLearningPipelines!
model
End to End ML Workflow
31. Load Data Extract Features Train Model Evaluate Model
Model
consumption
Machine Learning is Iterative
34. ML.Net, working with
TensorFlow frozen models
MakeMagicHappen();
https://www.microsoft.com/net/learn/apps/machi
ne-learning-and-ai
35. • 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