2019 12 14 Global AI Bootcamp - Auto ML with Machine Learning.NetBruno Capuano
Slides used during my session "Auto ML with Machine Learning.Net" at the Global AI Bootcamp on 2019 Dec 14 at the Microsoft Offices in the Greater Toronto Area
2019 12 14 Global AI Bootcamp - Auto ML with Machine Learning.NetBruno Capuano
Slides used during my session "Auto ML with Machine Learning.Net" at the Global AI Bootcamp on 2019 Dec 14 at the Microsoft Offices in the Greater Toronto Area
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.
2019 12 19 Mississauga .Net User Group - Machine Learning.Net and Auto MLBruno Capuano
Slides used during the "Machine Learning Galore" session, on 2019 December 19 at the Microsoft offices. Event hosted by the Mississauga .Net User Group and my session was around Machine Learning.Net and Auto ML
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.
Denver Dev Day - Smart Apps with Azure MLChris McHenry
I recently presented at the Denver Dev Day on Smart Apps with Azure ML: In the words of Marc Andreessen, "Software is eating the world". Industries are being disrupted at an alarming rate due to intelligent software. Azure Machine Learning enables developers to easily add intelligence to their Apps. In this session we'll look at the recently GA'd Azure ML service and see how it's easy to make your Apps smart!
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/
Slidedeck for my session on Insider Dev Tour 2019 (Lisbon Jul 29th).
Mostly based on tools and platform support for AI workloads and the options for edge computing and cloud computing.
ML.NET, WinML, DirectML, Model Builder, Azure Cognitive Services, ...
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
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
How to Make Cars Smarter: A Step Towards Self-Driving CarsVMware Tanzu
We are moving towards the reality of self-driving cars, but we are still years away from fully autonomous vehicles. In the meantime, however, there are a number of things we can do to make cars smarter in order to improve the lives of drivers. We can use data and analytics, for example, to prevent breakdowns and predict problems before they occur. Technology can also help cars achieve better performance in extreme situations like hydroplaning.
The reality is that data collected by car sensors is underused today.
In this webinar, we will examine:
How to detect patterns in massive amounts of connected car data
Use cases for connected car applications, such as predicting failure of parts and subsystems before they occur
How to apply analytics in real time to help drivers avoid dangerous situations
How to leverage independent data sources to increase predictive value
Deep Dive: In the second half of the webinar we will give an actual example of how we apply big data technology to this problem.
Microsoft Introduction to Automated Machine LearningSetu Chokshi
A gentle introduction to Microsoft's AutoML SDK package. This presentation introduces the concept of why automated machine learning has an important place in any data scientists tool box. Auto ML SDK allows you to to build and run machine learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, including Jupyter Notebooks or your favourite Python IDE.
The demos included in the presentation are making use of the Azure Notebooks.
Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
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.
2019 12 19 Mississauga .Net User Group - Machine Learning.Net and Auto MLBruno Capuano
Slides used during the "Machine Learning Galore" session, on 2019 December 19 at the Microsoft offices. Event hosted by the Mississauga .Net User Group and my session was around Machine Learning.Net and Auto ML
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.
Denver Dev Day - Smart Apps with Azure MLChris McHenry
I recently presented at the Denver Dev Day on Smart Apps with Azure ML: In the words of Marc Andreessen, "Software is eating the world". Industries are being disrupted at an alarming rate due to intelligent software. Azure Machine Learning enables developers to easily add intelligence to their Apps. In this session we'll look at the recently GA'd Azure ML service and see how it's easy to make your Apps smart!
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/
Slidedeck for my session on Insider Dev Tour 2019 (Lisbon Jul 29th).
Mostly based on tools and platform support for AI workloads and the options for edge computing and cloud computing.
ML.NET, WinML, DirectML, Model Builder, Azure Cognitive Services, ...
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
The session is about creating, training, evaluating and deploying machine learning with no-code approach using Azure AutoML.
* NO MACHINE LEARNING EXPERIENCE REQUIRED *
Agenda:
1. Introduction to Machine Learning
2. What is AutoML (Automated Machine Learning) ?
3. AutoML versus Conventional ML practices
4. Intro to Azure Automated Machine Learning
5. Hands-on demo
6 Contest
6. Learning resources
7. Conclusion
How to Make Cars Smarter: A Step Towards Self-Driving CarsVMware Tanzu
We are moving towards the reality of self-driving cars, but we are still years away from fully autonomous vehicles. In the meantime, however, there are a number of things we can do to make cars smarter in order to improve the lives of drivers. We can use data and analytics, for example, to prevent breakdowns and predict problems before they occur. Technology can also help cars achieve better performance in extreme situations like hydroplaning.
The reality is that data collected by car sensors is underused today.
In this webinar, we will examine:
How to detect patterns in massive amounts of connected car data
Use cases for connected car applications, such as predicting failure of parts and subsystems before they occur
How to apply analytics in real time to help drivers avoid dangerous situations
How to leverage independent data sources to increase predictive value
Deep Dive: In the second half of the webinar we will give an actual example of how we apply big data technology to this problem.
Microsoft Introduction to Automated Machine LearningSetu Chokshi
A gentle introduction to Microsoft's AutoML SDK package. This presentation introduces the concept of why automated machine learning has an important place in any data scientists tool box. Auto ML SDK allows you to to build and run machine learning workflows with the Azure Machine Learning service. You can interact with the service in any Python environment, including Jupyter Notebooks or your favourite Python IDE.
The demos included in the presentation are making use of the Azure Notebooks.
Join us to see how Public-sector organizations and AWS Partners are combining Smart Devices and Artificial Intelligence to create flexible, secure and cost-effective solutions. Applying machine learning models to live video/audio, cameras can be transformed into flexible IoT devices that perform critical functions around public safety, security, property management, smart parking & environmental management. Learn how these solutions are architected using AWS services such as AWS IoT Core, AWS GreenGrass, AWS DeepLens, Amazon SageMaker and Amazon Alexa.
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
Similar to 2020 11 19 MVP Days Israel 2020 - Introduction to Machine Learning.Net and AutoML (20)
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.
2019 11 26 BotTO November 2019 Meetup at TDBruno Capuano
This session is based on the latest news presented around Microsoft Bot Framework and LUIS at Microsoft Ignite 2019.
The slides were used on the event #BotTO November 2019 Meetup @ TD
2019 02 27 How to earn an MVP Awards and what are the benefitsBruno Capuano
Slides of my session with Ehsan Eskandari about How to earn an MVP Awards and what are the benefits of the Microsoft MVP program. This session was a lighting talk for the Toronto Metro .Net User Group of Feb 27 2019.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
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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.
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
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Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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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/
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In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
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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.
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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!
3. Proven & Extensible
Open Source & Cross platform
dot.net/ml
Build your own
Developer Focused
ML.NET is a machine learning framework
made for .NET developers
4. And many more examples
@ https://github.com/dotnet/machinelearning-samples
Customer segmentation
Recommendations
Predictive maintenance
Forecasting
Issue Classification
Ranking news/topics
Image classification
Sentiment Analysis
Machine Learning scenarios with ML.NET
7. Comment Toxic? (Sentiment)
==RUDE== Dude, you are rude … 1
== OK! == IM GOING TO VANDALIZE … 1
I also found use of the word "humanists” confusing … 0
Oooooh thank you Mr. DietLime … 0
Wikipedia detox data at https://figshare.com/articles/Wikipedia_Talk_Labels_Personal_Attacks/4054689
Features (input) Label (output)
Sentiment Analysis
8. Prepare Your Data
Example
Comment Toxic? (Sentiment)
==RUDE== Dude, you are rude … 1
== OK! == IM GOING TO VANDALIZE … 1
I also found use of the word "humanists” confusing … 0
Oooooh thank you Mr. DietLime … 0
Important concepts: Data
9. Prepare Your Data
Text Featurizer
Featurized Text
[0.76, 0.65, 0.44, …]
[0.98, 0.43, 0.54, …]
[0.35, 0.73, 0.46, …]
[0.39, 0, 0.75, …]
Example
Text
==RUDE== Dude, you are rude …
== OK! == IM GOING TO VANDALIZE …
I also found use of the word "humanists” …
Oooooh thank you Mr. DietLime …
Important concepts: Transformer
10. Build & Train
Example
Estimator
Comment Toxic? (Sentiment)
==RUDE== Dude, you … 1
== OK! == IM GOING … 1
I also found use of the … 0
Oooooh thank you Mr. … 0
Important concepts: Estimator
11. Comment
==RUDE== Dude, you …
Prediction Function
Predicted Label – Toxic? (Sentiment)
1
Run
Example
Important concepts: Prediction Function
14. Anomaly Detection
Anomaly detection detects data
points in data that does not fit well
with the rest of the data.
It has a wide range of applications
such as fraud detection, surveillance,
diagnosis, data cleanup, and
predictive maintenance.
20. How much is the taxi fare for 1 passenger going from Burlington to Toronto?
ML.NET CLI global tool accelerates productivity
AutoML with ML.NET
21. Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ
Parameter 1
Parameter 2
Parameter 3
Parameter 4
…
Distance
Trip time
Car type
Passengers
Time of day
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Distance Gradient Boosted
Model
Car type
Passengers
Getting started w/machine learning can be hard
ML.NET takes the guess work out of data prep,
feature selection & hyperparameter tuning
Which algorithm? Which parameters?Which features?
Getting started w/machine learning can be
hard
22. N Neighbors
Weights
Metric
P
ZYX
Criterion
Loss
Min Samples Split
Min Samples Leaf
XYZ
Which algorithm? Which parameters?Which features?
Distance
Trip time
Car type
Passengers
Time of day
…
Gradient Boosted
Nearest Neighbors
SGD
Bayesian Regression
LGBM
…
Nearest Neighbors
Model
Iterate
Gradient BoostedDistance
Car brand
Year of make
Car type
Passengers
Trip time
Getting started w/machine learning can be hard
ML.NET takes the guess work out of data prep,
feature selection & hyperparameter tuning
Getting started w/machine learning can be
hard
23. Which algorithm? Which parameters?Which features?
Iterate
Getting started w/machine learning can be hard
ML.NET takes the guess work out of data prep,
feature selection & hyperparameter tuning
Getting started w/machine learning can be
hard
25. 70%95% Feature importance
Distance
Trip time
Car type
Passengers
Time of day
0 1
Model B (70%)
Distance
0 1
Trip time
Car type
Passengers
Time of day
Feature importance Model A (95%)
ML.NET accelerates model development
with model explainability
ML.NET accelerates model development
28. Getting started with ML.Net
Bruno Capuano
Innovation Lead @Avanade
@elbruno | http://elbruno.com
Editor's Notes
.NET is a great tech stack for building a wide variety of applications. There is ASP.NET for web development, Xamarin for mobile development and with ML.NET we are trying to make .NET great for Machine Learning.
Even though we just recently released ML.NET at Build this year, ML.NET has been used at Microsoft heavily for over a decade by iconic MS products.
Bing Ads uses ML.NET for add-click predictions
Excel uses ML.NET for chart recommendations
PowerPoint uses ML.NET for Design Ideas
Windows10 uses ML.NET for Windows Defender
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The Anomaly Detection API can detect the following types of anomalies on time series data:
Spikes and Dips: For example, when monitoring the number of login failures to a service or number of checkouts in an e-commerce site, unusual spikes or dips could indicate security attacks or service disruptions.
Positive and negative trends: When monitoring memory usage in computing, for instance, shrinking free memory size is indicative of a potential memory leak; when monitoring service queue length, a persistent upward trend may indicate an underlying software issue.
Level changes and changes in dynamic range of values: For example, level changes in latencies of a service after a service upgrade or lower levels of exceptions after upgrade can be interesting to monitor.
The machine learning based API enables:
Flexible and robust detection: The anomaly detection models allow users to configure sensitivity settings and detect anomalies among seasonal and non-seasonal data sets. Users can adjust the anomaly detection model to make the detection API less or more sensitive according to their needs. This would mean detecting the less or more visible anomalies in data with and without seasonal patterns.
Scalable and timely detection: The traditional way of monitoring with preset thresholds set by experts' domain knowledge are costly and not scalable to millions of dynamically changing data sets. The anomaly detection models in this API are learned and models are tuned automatically from both historical and real-time data.
Proactive and actionable detection: Slow trend and level change detection can be applied for early anomaly detection. The early abnormal signals detected can be used to direct humans to investigate and act on the problem areas. In addition, root cause analysis models and alerting tools can be developed on top of this anomaly detection API service.
The anomaly detection API is an effective and efficient solution for a wide range of scenarios like service health & KPI monitoring, IoT, performance monitoring, and network traffic monitoring. Here are some popular scenarios where this API can be useful:
IT departments need tools to track events, error code, usage log, and performance (CPU, Memory and so on) in a timely manner.
Online commerce sites wants to track customer activities, page views, clicks, and so on.
Utility companies want to track consumption of water, gas, electricity and other resources.
Facility/Building management services want to monitor temperature, moisture, traffic and so on.
IoT/manufacturers want to use sensor data in time series to monitor work flow, quality and so on.
Service providers, such as call centers need to monitor service demand trend, incident volume, wait queue length and so on.
Business analytics groups want to monitor business KPIs' (such as sales volume, customer sentiments, pricing) abnormal movement in real time.
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ML.NET provides tooling that makes it easy to use. In particular, 2 really valuable tools are: AutoML and Model Builder
What is AutoML? It is an API that accelerates model development for you. A lot of developers do not have the experience required to build or train Machine Learning models. With AutoML, the process of finding the best algorithm, is automated!
Model Builder on the other hand provides an easy to understand visual interface to build, train, and deploy custom machine learning models. Prior machine learning expertise is not required. It also supports AutoML
Rememeber depending on your data, giving you the error of each of the models and you can then decide which model to use. Most people just use the model with the least error.
And we will see it in action soon.
To demonstrate what AutoML is, let’s consider that we want to provide a service that allows users to predict taxi fare before they book or call a taxi. How can we build this feature/service?
A data scientist’s job is to find the best algorithm that will do taxi fare prediction.
Let’s says we have a dataset that contains information such as trip distance, trip time, number of passengers, time of day of the trip etc.
A data scientist will spend a lot of time trying to decide which of these pieces of information is important when predicting taxi fare.
In ML, there are so many algorithms and are generally referred to as trainers, for example linear regression, convolutional neural network etc
The data scientist will try one algorithm at a time, picking features as he desires, and then wait to see how the model performs.
In this case, this model only scored 30% based on number of bad predictions it made.
Microsoft Envision 2016
Microsoft Envision 2016
Microsoft Envision 2016
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ML.NET is an end to end solution for your Machine Learning needs. The steps taken:
We loaded data, which we already do!
We initialized a progress handler which would help track each model tried!
We then ran AutoML which tried many different models and returned back to us the top models!
We picked the best preforming model and evaluated it on test data!
Finally, we saved the model for future use.
Very few lines of code needed.
No model building expertise is needed. There’s throurough documentation on the ML.NET site and there also many samples provided – you might find what you need there and code provided to you.