#Event – Resources used in the #devdotnextdigital session around Anomaly Detection #devdotnext2020

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Hi!

I had an amazing time with the Dev.Next team in a virtual session. The agenda was great, I joined a couple of session and it was great ! Take a look at the full set of contents here. Recording sessions soon !

Important: the event was postponed to August, and you can more information here.

Slides

Source

Updated source code soon !

Reference Links

General

Machine Learning.Net

Cognitive Services Anomaly Detector

Azure Machine Learning

Happy coding!

Greetings

El Bruno

#Event – (update) @devdotnext now in virtual mode ! #devdotnext2020

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Hi!

These are strange days, however we all try to do our best to move forward. An important part of today’s behaviors is to try to avoid big events. That’s why, in the last 2 weeks, we get news about events being cancelled all over the world.

I have a plan to visit Colorado, US, in a couple of weeks for the Dev.Next event. However, due to Covid-19, the event was postponed to August (more information here).

The amazing organizing team, decided to face this with a big smile and organized a mini virtual version of the event. This is the official announcement:

dev.next is happy to announce a mini digital event on March 24, 2020. The event is free to attend. We will post link here on March 24th. Recordings from the event will be available later. Please check back here for links to the recordings.

dev.next digital event

I’ll be part of this Digital / Virtual Days hosting a remote session around AI and Anomaly Detection, with code and without code !

Take a look at the agenda here.

Happy coding!

Greetings

El Bruno

#CognitiveServices – #AnomalyDetector Service running on Containers (some #docker time!)

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Hi!

I was chatting about the new features and the use cases supported using the brand new (and still in Preview) Cognitive Services Anomaly Detector and I realized that we can use the service with local containers (instead of the cloud service), but there is something different in this Service.

anomaly detector sample data chart

Why to use containers?

Azure Cognitive Services allow developers to easily add cognitive features—such as object detection, vision recognition, and language understanding—into their applications without having direct AI or data science skills or knowledge. Containerization is an approach to software distribution in which an application or service is packaged so that it can be deployed in a container host with little or no modification

I first will recommend reading the article [Getting started with Azure Cognitive Services in containers] and then I will remark 2 main advantages of using containers

  • You can get a better control of your internet usage. I mean, all the HTTP calls will be performed in your intranet
  • You will have a better data governance.

Related to the 2nd bullet, I’ll quote the official launch sample

For example, let’s look at a typical hospital system that works with patients. After many years of taking care of patients, they have numerous doctor’s notes, intake records, or other files that they want to process and derive insights about key trends. Using Cognitive Services containers, they can process all of these files, index millions of documents and find commonalities, and improve the patient experience while keeping the data in-house. Another example would be a large manufacturing plant that has limited connectivity where they want to track assets on the edge using remote sensors and cameras, using AI to predict maintenance needs.

As a downside point, you need to manage your own docker instances, which may require some extra work. However, it’s likely that you are already doing this, so this is “just more containers”

How about Anomaly Detector Containers?

And, after this small introduction, and starting to use Anomaly Detector Service I found that if you want to use Anomaly Detector with containers, you must first complete and submit the Anomaly Detector Container Request form to request access to the container.

Anomaly Detector Container Request form

https://forms.office.com/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbRxSkyhztUNZCtaivu8nmhd1UOTdDRzlPSDRBOEdZVFcxR0lTUUlBRzY1OS4u

The form requests information about you, your company, and the user scenario for which you’ll use the container. After you’ve submitted the form, the Azure Cognitive Services team reviews it to ensure that you meet the criteria for access to the private container registry.

Once you submitted your request and been approved, you can start to use the Anomaly Detector service locally with containers. And just a reminder about this: even if your data requests are not going to hit and Azure HTPP Endpoint, your containers need to be able to connect to Azure.

The container needs the billing argument values to run. These values allow the container to connect to the billing endpoint. The container reports usage about every 10 to 15 minutes. If the container doesn’t connect to Azure within the allowed time window, the container continues to run but doesn’t serve queries until the billing endpoint is restored. The connection is attempted 10 times at the same time interval of 10 to 15 minutes. If it can’t connect to the billing endpoint within the 10 tries, the container stops running.

Happy Coding!

Greetings @ Toronto

El Bruno

References

#CognitiveServices – Easy lines to convert CSV to JSON to be used on the #AnomalyDetector service

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Hi!

After the event “Building an Anomaly Detector System with a few or no lines of code” at MsftReactor, some people asked for the 2 lines that I used to convert a CSV file to JSON, to be used with Cognitive Services Anomaly Detector, so here they are.

Important: you need Newtonsoft.Json to build the json content.

The input CSV is part of the Machine Learning.Net sample data, and has this sample content:

Month,ProductSales
1-Jan,271
2-Jan,150.9
3-Jan,188.1
...

And as a bonus, the full console project can be downloaded from here.

https://github.com/elbruno/Blog/tree/master/20191125%20CSV%20to%20JSON%20for%20Anomaly%20Detector

This project also have a second function which creates the JSON content using simple string, without the need of Newtonsoft.Json.

Happy coding!

Greetings @ Burlingon

El Bruno

References

#Event – Resources used during my session “Building an Anomaly Detector System with a few or no lines of code” @MsftReactor

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Hi!

My 1st session at Microsoft Reactor and it was amazing. We had engaging conversations around Machine Learning and Anomaly Detection, and as I promised here are the resources used.

Building an Anomaly Detector System with a few or no lines of code

More Information https://www.microsoftevents.com/profile/form/index.cfm?PKformID=0x8330114abcd

Slides

Source Code

https://github.com/elbruno/events/tree/master/2019%2011%2021%20Anomaly%20Detections%20Reactor

Reference Links

General

Machine Learning.Net

Cognitive Services Anomaly Detector

Azure Machine Learning

Happy coding!

Greetings @ Toronto

El Bruno

#Event – 2×1 on #AI: Anomaly Detection at Microsoft Reactor and ML.Net @MississaugaNetU

Photo by Sachin C Nair on Pexels.com
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Hi!

Quick post to remember about a couple of events where I’ll participate.

1st, this Thursday at Microsoft Reactor in Toronto.

Building an Anomaly Detector System with a few or no lines of code

November 21, 2019 | 5:00PM – 7:00PM

Reactor Toronto
MaRS Centre, Heritage Building 101 College Street, Suite 120
Toronto Ontario M5G-1L7
Canada

More Information https://www.microsoftevents.com/profile/form/index.cfm?PKformID=0x8330114abcd

And, I’m happy to go back with my friends from the Mississauga DotNet User Group.

Machine Learning Galore!

Thursday, December 19, 2019
6:00 PM to 8:00 PM

More Information https://www.meetup.com/MississaugaNETUG/events/266518936/

Happy Coding!

Greetings @ Toronto

El Bruno

#Event – Building an Anomaly Detector System with a few or no lines of code at @MSFTReactor

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Hi !

Microsoft opened a brand new Microsoft Reactor in Toronto, and I’m lucky enough to host a AI session about Anomaly Detection. Below are the details

Detecting anomalies is a common scenario which can be applied to dozens of industries. From the analysis of power consumption, medical data, or even analysis of personal information, anomalies can be detected based on historical data.

During this workshop, Bruno will guide attendees to code a complete system that will detect anomalies: you will train a model based on historical data, and later use the same model with new data to identify anomalies. At the end of the workshop, attendees will review a new set of options to create an Anomaly Detection System without a single line of code!

Please bring a laptop or other personal device to participate in this hands-on workshop.

Event: https://www.microsoftevents.com/profile/form/index.cfm?PKformID=0x8330114abcd

Happy Coding!

Greetings @ Burlington

El Bruno

#CognitiveServices – Nuevo Servicio: Anomaly Detector

Buenas!

Durante estos días, estoy totalmente centrado en un increíble evento en Avanade Canada: Canada !nnovate Event, y casi me pierdo el lanzamiento de este nuevo servicio, en la familia Cognitive Services: Anomaly Detector.

Este fin de semana tenia pensado terminar mi sesión para la próxima Global AI Night (link), sin embargo, me parece que dedicaré un poco de tiempo para poder hablar del mismo en el próximo evento.

Después de pasar hoy en día leyendo sobre el servicio, parece que funciona con series de tiempo de datos y el uso de modelos específicos, se centran en las anomalías de los datos de la serie y .. ¡Magia! Creo que la documentación oficial lo explica mejor:

The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data with machine learning. The Anomaly Detector API adapts by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.

Detect pattern changes in service requests

More information: https://azure.microsoft.com/en-us/services/cognitive-services/anomaly-detector/

Saludos @ Burlington

El Bruno

#CognitiveServices – New service: Anomaly Detector

Hi !

So, I’m fully focused on have an amazing Canadian !nnovate Event in Avanade and I almost miss this new service, in the Cognitive Services Family: Anomaly Detector.

I was planning to run my 30K Around the Bay this weekend and also finish my session for the next Global AI Night (link), however I may play around with this service and add a quick reference on the next event.

After spending sometime today reading about the service, it seems that it works with time series of data and using specific models, it focus on anomalies of data on the series and .. magic! I think the official documentation explains this better:

The Anomaly Detector API enables you to monitor and detect abnormalities in your time series data with machine learning. The Anomaly Detector API adapts by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.

Detect pattern changes in service requests

More information: https://azure.microsoft.com/en-us/services/cognitive-services/anomaly-detector/

Greetings @ Toronto

El Bruno