If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
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With SQL Server and Cosmos Db we now have graph databases broadly available, after being studied for decades in Db theory, or being a niche approach in Open Source with Neo4J. And then there are services like Microsoft Graph and Azure Digital Twins that give us vertical implementations of graph. So let's make a walkaround of graphs in the MIcrosoft ecosystem.
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If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
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This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
A Practical Guide to Anomaly Detection for DevOpsBigPanda
Recent years have seen an explosion in the volumes of data that modern production environments generate. Making fast educated decisions about production incidents is more challenging than ever. BigPanda's team is passionate about solutions such as anomaly detection that tackle this very challenge.
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Twitter has become the de facto medium for consumption of news in real time, and billions of events are generated and analyzed on a daily basis. To analyze these events, Twitter designed its own next-generation streaming system, Heron. Arun Kejariwal and Karthik Ramasamy walk you through how Heron is used to detect anomalies in real-time data streams. Although there’s been over 75 years of prior work in anomaly detection, most of the techniques cannot be used off the shelf because they’re not suitable for high-velocity data streams. Arun and Karthik explain how to make trade-offs between accuracy and speed and discuss incremental approaches that marry sampling with robust measures such as median and MCD for anomaly detection.
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With SQL Server and Cosmos Db we now have graph databases broadly available, after being studied for decades in Db theory, or being a niche approach in Open Source with Neo4J. And then there are services like Microsoft Graph and Azure Digital Twins that give us vertical implementations of graph. So let's make a walkaround of graphs in the MIcrosoft ecosystem.
What is a Digital Twin? Why is it another point of view of the IoT stack in Azure. Which are the features? How does it relates to IoT Hub and other Azure IoT services?
Time Series Anomaly Detection with .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET o Azure? Let's see how in this session.
Five Things I Learned While Building Anomaly Detection Tools - Toufic Boubez ...tboubez
This is my presentation from LISA 2014 in Seattle on November 14, 2014.
Most IT Ops teams only keep an eye on a small fraction of the metrics they collect because analyzing this haystack of data and extracting signal from the noise is not easy and generates too many false positives.
In this talk I will show some of the types of anomalies commonly found in dynamic data center environments and discuss the top 5 things I learned while building algorithms to find them. You will see how various Gaussian based techniques work (and why they don’t!), and we will go into some non-parametric methods that you can use to great advantage.
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Recent years have seen an explosion in the volumes of data that modern production environments generate. Making fast educated decisions about production incidents is more challenging than ever. BigPanda's team is passionate about solutions such as anomaly detection that tackle this very challenge.
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Twitter has become the de facto medium for consumption of news in real time, and billions of events are generated and analyzed on a daily basis. To analyze these events, Twitter designed its own next-generation streaming system, Heron. Arun Kejariwal and Karthik Ramasamy walk you through how Heron is used to detect anomalies in real-time data streams. Although there’s been over 75 years of prior work in anomaly detection, most of the techniques cannot be used off the shelf because they’re not suitable for high-velocity data streams. Arun and Karthik explain how to make trade-offs between accuracy and speed and discuss incremental approaches that marry sampling with robust measures such as median and MCD for anomaly detection.
With tens of thousands of Java servers running in production in enterprise, Java has become a language of choice for building production systems. If our machines are to exhibit acceptable performance, they require regular tuning.This talk takes a detailed look at techniques for tuning a Java Server.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2UkZRIC.
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Let's discover what a Twin is and which features this new service offers
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https://www.bigdataspain.org/2017/talk/attacking-machine-learning-used-in-antivirus-with-reinforcement
Big Data Spain 2017
16th - 17th Kinépolis Madrid
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Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
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If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET and Azure?
Slildes from the Webinar "Five Ways to Leverage AI and Tableau". Full webinar recording: https://starschema.com/kb/five-ways-to-leverage-ai-and-tableau
Sources & Workbooks: https://github.com/starschema/tableau-ai-use-cases
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2UkZRIC.
Monal Daxini presents a blueprint for streaming data architectures and a review of desirable features of a streaming engine. He also talks about streaming application patterns and anti-patterns, and use cases and concrete examples using Apache Flink. Filmed at qconsf.com.
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Azure Digital Twins is an Azure IoT service that allow you creating comprehensive models of the physical environment the covers your devices and the envronment that surrounds them.
Let's discover what a Twin is and which features this new service offers
Attacking Machine Learning used in AntiVirus with Reinforcement by Rubén Mart...Big Data Spain
In recent years Machine Learning (ML) and especially Deep Learning (DL) have achieved great success in many areas such as visual recognition, NLP or even aiding in medical research.
https://www.bigdataspain.org/2017/talk/attacking-machine-learning-used-in-antivirus-with-reinforcement
Big Data Spain 2017
16th - 17th Kinépolis Madrid
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This is an overview of the goals and roadmap for the Yellowbrick model visualization library (www.scikit-yb.org). If you're interested in contributing to Yellowbrick or writing visualizers, this is a good place to get started.
In the presentation we discuss the expected workflow of data scientists interacting with the model selection triple and Scikit-Learn. We describe the Yellowbrick API and it's relationship to the Scikit-Learn API. We introduce our primary object: the Visualizer, an estimator that learns from data and displays it visually. Finally we describe the requirements for developing for Yellowbrick, the tools and utilities in place and how to get started.
Yellowbrick is a suite of visual diagnostic tools called "Visualizers" that extend the Scikit-Learn API to allow human steering of the model selection process. In a nutshell, Yellowbrick combines Scikit-Learn with Matplotlib in the best tradition of the Scikit-Learn documentation, but to produce visualizations for your models!
This presentation was given during the opening session of the 2017 Spring DDL Research Labs.
This talk describes the general architecture common to anomaly detections systems that are based on probabilistic models. By examining several realistic use cases, I illustrate the common themes and practical implementation methods.
ICTER 2014 Invited Talk: Large Scale Data Processing in the Real World: from ...Srinath Perera
Large scale data processing analyses and makes sense of large amounts of data. Although the field itself is not new, it is finding many usecases under the theme "Bigdata" where Google itself, IBM Watson, and Google's Driverless car are some of success stories. Spanning many fields, Large scale data processing brings together technologies like Distributed Systems, Machine Learning, Statistics, and Internet of Things together. It is a multi-billion-dollar industry including use cases like targeted advertising, fraud detection, product recommendations, and market surveys. With new technologies like Internet of Things (IoT), these use cases are expanding to scenarios like Smart Cities, Smart health, and Smart Agriculture. Some usecases like Urban Planning can be slow, which is done in batch mode, while others like stock markets need results within Milliseconds, which are done in streaming fashion. There are different technologies for each case: MapReduce for batch processing and Complex Event Processing and Stream Processing for real-time usecases. Furthermore, the type of analysis range from basic statistics like mean to complicated prediction models based on machine Learning. In this talk, we will discuss data processing landscape: concepts, usecases, technologies and open questions while drawing examples from real world scenarios.
http://icter.org/conference/invited_speeches
compute tier. Detection and filtering of anomalies in live data is of paramount importance for robust decision making. To this end, in this talk we share techniques for anomaly detection in live data.
From Pipelines to Refineries: scaling big data applications with Tim HunterDatabricks
Big data tools are challenging to combine into a larger application: ironically, big data applications themselves do not tend to scale very well. These issues of integration and data management are only magnified by increasingly large volumes of data. Apache Spark provides strong building blocks for batch processes, streams and ad-hoc interactive analysis. However, users face challenges when putting together a single coherent pipeline that could involve hundreds of transformation steps, especially when confronted by the need of rapid iterations. This talk explores these issues through the lens of functional programming. It presents an experimental framework that provides full-pipeline guarantees by introducing more laziness to Apache Spark. This framework allows transformations to be seamlessly composed and alleviates common issues, thanks to whole program checks, auto-caching, and aggressive computation parallelization and reuse.
Introduction to Analytics with Azure Notebooks and PythonJen Stirrup
Introduction to Analytics with Azure Notebooks and Python for Data Science and Business Intelligence. This is one part of a full day workshop on moving from BI to Analytics
Best Practices for Hyperparameter Tuning with MLflowDatabricks
Hyperparameter tuning and optimization is a powerful tool in the area of AutoML, for both traditional statistical learning models as well as for deep learning. There are many existing tools to help drive this process, including both blackbox and whitebox tuning. In this talk, we'll start with a brief survey of the most popular techniques for hyperparameter tuning (e.g., grid search, random search, Bayesian optimization, and parzen estimators) and then discuss the open source tools which implement each of these techniques. Finally, we will discuss how we can leverage MLflow with these tools and techniques to analyze how our search is performing and to productionize the best models.
Speaker: Joseph Bradley
Using Deep Learning on Apache Spark to Diagnose Thoracic Pathology from Chest...Databricks
Overview and extended description: AI is expected to be the engine of technological advancements in the healthcare industry, especially in the areas of radiology and image processing. The purpose of this session is to demonstrate how we can build a AI-based Radiologist system using Apache Spark and Analytics Zoo to detect pneumonia and other diseases from chest x-ray images. The dataset, released by the NIH, contains around 110,00 X-ray images of around 30,000 unique patients, annotated with up to 14 different thoracic pathology labels. Stanford University developed a state-of-the-art model using CNN and exceeds average radiologist performance on the F1 metric. This talk focuses on how we can build a multi-label image classification model in a distributed Apache Spark infrastructure, and demonstrate how to build complex image transformations and deep learning pipelines using BigDL and Analytics Zoo with scalability and ease of use. Some practical image pre-processing procedures and evaluation metrics are introduced. We will also discuss runtime configuration, near-linear scalability for training and model serving, and other general performance topics.
Time Series Anomaly Detection for .net and AzureMarco Parenzan
If you have any device or source that generates values over time (also a log from a service), you want to determine if in a time frame, the time serie is correct or you can detect some anomalies. What can you do as a developer (not a Data Scientist) with .NET and Azure?
Slildes from the Webinar "Five Ways to Leverage AI and Tableau". Full webinar recording: https://starschema.com/kb/five-ways-to-leverage-ai-and-tableau
Sources & Workbooks: https://github.com/starschema/tableau-ai-use-cases
Hard Truths About Streaming and Eventing (Dan Rosanova, Microsoft) Kafka Summ...confluent
Eventing and streaming open a world of compelling new possibilities to our software and platform designs. They can reduce time to decision and action while lowering total platform cost. But they are not a panacea. Understanding the edges and limits of these architectures can help you avoid painful missteps. This talk will focus on event driven and streaming architectures and how Apache Kafka can help you implement these. It will also discuss key tradeoffs you will face along the way from partitioning schemes to the impact of availability vs. consistency (CAP Theorem). Finally we’ll discuss some challenges of scale for patterns like Event Sourcing and how you can use other tools and even features of Kafka to work around them. This talk assumes a basic understanding of Kafka and distributed computing, but will include brief refresher sections.
Your data is in Prometheus, now what? (CurrencyFair Engineering Meetup, 2016)Brian Brazil
Prometheus is a next-generation monitoring system with a time series database at it's core. Once you have a time series database, what do you do with it though? This talk will look at getting data in, and more importantly how to use the data you collect productively.
Contact us at prometheus@robustperception.io
Performance doesn’t have the same definition between system administrators, developpers and business teams. What is Performance ? High CPU usage, not scalable web site, low business transaction rate per sec, slow response time, … This presentation is about maths, code performance, load testing, web performance, best practices, … Working on performance optimizaton is a very broad topic. It’s important to really understand main concepts and to have a clean and strong methodology because it could be a very time consumming activity. Happy reading !
This Tutorial will discuss and demonstrate how to implement different realtime streaming analytics patterns. We will start with counting usecases and progress into complex patterns like time windows, tracking objects, and detecting trends. We will start with Apache Storm and progress into Complex Event Processing based technologies.
Streamlio and IoT analytics with Apache PulsarStreamlio
To keep up with fast-moving IoT data, you need technology that can collect, process and store data with performance and scalability. This presentation from Data Day Texas looks at the technology requirements and how Apache Pulsar can help to meet them.
Discovering signal in financial time series- where and how to startNicholasSherman11
In Part 1 of our webinar series on discovering signal in financial time-series data, Stanley walked you through constructing a state-of-the-art AI-driven trading strategy, by forecasting commodity time-series data, and constructing a three-asset portfolio adjusted on predictive risk.
Now, in Part 2, Stanley Speel describes how and why generating meaningful signals and relationships in time-series data is often the key to building accurate forecasting models.
Watch Stanley walk you through:
Preparing time-series historical data for a regression model
Methods for isolating and selecting relevant and independent signals
Network-based approaches for identifying and modelling relationships between multiple time-varying signals
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Time Series Anomaly Detection with .net and Azure
1. Time Series Anomaly Detection
with .net and Azure
Marco Parenzan
Solution Sales Specialist @ Insight // Microsoft Azure MVP // 1nn0va Community Lead
Marco Parenzan
4. Agenda
• Scenario
• Anomaly Detection in Time Series
• Data Science for the .NET developer
• How Data Scientists work
• Bring ML.NET to Azure
• Anomaly Detection As A Service in Azure
• Conclusions
6. Scenario
From real industrial fridges
In an industrial fridge, you monitor temperatures to check
not the temperature «per se», but to check the healthy of
the plant
• Opening a door
• Condenser
• Evaporator
You can considering each of these events as anomalies
that alter the temperature you measure in different part of
the fridge
12. Anomaly Detection
Anomaly detection is the process of identifying unexpected items or events in data sets, which differ
from the norm.
And anomaly detection is often applied on unlabeled data which is known as unsupervised anomaly
detection.
13. Time Series
Definition
• Time series is a sequence of data points recorded in time order, often taken at successive
equally paced points in time.
Examples
• Stock prices, Sales demand, website traffic, daily temperatures, quarterly sales
Time series is different from regression analysis because of its time-dependent nature.
• Auto-correlation: Regression analysis requires that there is little or no autocorrelation in the data.
It occurs when the observations are not independent of each other. For example, in stock prices,
the current price is not independent of the previous price. [The observations have to be
dependent on time]
• Seasonality, a characteristic which we will discuss below.
14. Components of a Time Series
Trend
• is a general direction in which something is developing or changing. A trend can be upward(uptrend) or
downward(downtrend). It is not always necessary that the increase or decrease is consistently in the same direction in a
given period.
Seasonality
• Predictable pattern that recurs or repeats over regular intervals. Seasonality is often observed within a year or less.
Irregular fluctuation
• These are variations that occur due to sudden causes and are unpredictable. For example the rise in prices of food due
to war, flood, earthquakes, farmers striking etc.
15. Anomaly Detection in Time Series
In time series data, an anomaly or outlier can be termed as a data point which is not following the
common collective trend or seasonal or cyclic pattern of the entire data and is significantly distinct
from rest of the data. By significant, most data scientists mean statistical significance, which in order
words, signify that the statistical properties of the data point is not in alignment with the rest of the
series.
Anomaly detection has two basic assumptions:
• Anomalies only occur very rarely in the data.
• Their features differ from the normal instances significantly.
16. How to do Time Series Anomaly Detection?
Statistical Profiling Approach
• This can be done by calculating statistical values like mean or median moving average of the
historical data and using a standard deviation to come up with a band of statistical values which
can define the uppermost bound and the lower most bound and anything falling beyond these
ranges can be an anomaly.
• this approach is very handy and can always be the baseline approach, instead of going with any
sophisticated and complex methods which require a lot of fine tuning and may not be explainable.
• This is very effective for highly volatile time series as well, as most of the time series predictive
model algorithms fail when the data is highly volatile.
• But the main drawback of this approach is detecting the local outliers.
17. How to do Time Series Anomaly Detection?
By Predictive Confidence Level Approach
• One way of doing anomaly detection with time series data is by building a predictive model using
the historical data to estimate and get a sense of the overall common trend, seasonal or cyclic
pattern of the time series data.
• we can come up with a confidence interval, or a confidence band for the predicted values and
any actual data point which is falling beyond this confidence band is an anomaly
• The main advantage of this approach is finding local outlier but the main disadvantage is, this
approach highly depends on the efficiency of the predictive model. Any loop hole in the predictive
model can give false positives and false negatives.
18. How to do Time Series Anomaly Detection?
Clustering Based Unsupervised Approach
• Unsupervised approaches are extremely useful for anomaly detection as it does not require any
labelled data, mentioning that a particular data point is an anomaly.
20. Data Science and AI for the .NET developer
ML.NET is first and foremost a framework that you can use
to create your own custom ML models. This custom
approach contrasts with “pre-built AI,” where you use pre-
designed general AI services from the cloud (like many of
the offerings from Azure Cognitive Services). This can work
great for many scenarios, but it might not always fit your
specific business needs due to the nature of the machine
learning problem or to the deployment context (cloud vs. on-
premises).
ML.NET enables developers to use their existing .NET skills
to easily integrate machine learning into almost any .NET
application. This means that if C# (or F# or VB) is your
programming language of choice, you no longer have to
learn a new programming language, like Python or R, in
order to develop your own ML models and infuse custom
machine learning into your .NET apps.
23. Helping no-data scientits developers (all! )
Unsupervised Machine LearningNo labelling
Auto(mated) MLfind the best tuning for you with parameters and algorithms
Automated Training Set for Anomaly Detection Algorithms
• the algorithms automatically generates a simulated training set based non your input data
https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-cheat-sheet
24. Independent Identically Distributed (iid)
Data points collected in the time series are independently sampled from the same distribution
(independent identically distributed). Thus, the value at the current timestamp can be viewed as the
value at the next timestamp in expectation.
25. Singular Spectrum Analysis (SSA)
This class implements the general anomaly detection transform based on Singular Spectrum
Analysis (SSA). SSA is a powerful framework for decomposing the time-series into trend,
seasonality and noise components as well as forecasting the future values of the time-series.
In principle, SSA performs spectral analysis on the input time-series where each component in the
spectrum corresponds to a trend, seasonal or noise component in the time-series
26. Spectrum Residual Cnn (SrCnn)
to monitor the time-series continuously and alert for potential incidents on time
The algorithm first computes the Fourier Transform of the original data. Then it computes
the spectral residual of the log amplitude of the transformed signal before applying the Inverse
Fourier Transform to map the sequence back from the frequency to the time domain. This sequence
is called the saliency map. The anomaly score is then computed as the relative difference between
the saliency map values and their moving averages. If the score is above a threshold, the value at a
specific timestep is flagged as an outlier.
There are several parameters for SR algorithm. To obtain a model with good performance, we
suggest to tune windowSize and threshold at first, these are the most important parameters to SR.
Then you could search for an appropriate judgementWindowSize which is no larger than
windowSize. And for the remaining parameters, you could use the default value directly.
Time-Series Anomaly Detection Service at Microsof [https://arxiv.org/pdf/1906.03821.pdf]
27. Some tools required
.NET 5 + WPF + ML.NET
• Mandatory , the platform where we try to make experiments
Xplot.Ploty (soon you will understand I use this) https://fslab.org/XPlot/
• XPlot is a cross-platform data visualization package for the F# programming language powered by
popular JavaScript charting libraries Plotly and Google Charts. The library provides a complete
mapping for the configuration options of the underlying libraries and so you get a nice F# interface
that gives you access to the full power of Plotly and Google Charts. The XPlot library can be used
interactively from F# Interactive, but charts can equally easy be embedded in F# applications and in
HTML reports.
WebView2 https://docs.microsoft.com/en-us/microsoft-edge/webview2/gettingstarted/wpf
• The Microsoft Edge WebView2 control enables you to embed web technologies (HTML, CSS, and
JavaScript) in your native apps. The WebView2 control uses Microsoft Edge (Chromium) as the
rendering engine to display the web content in native apps. With WebView2, you may embed web
code in different parts of your native app. Build all of the native app within a single WebView instance.
30. Jupyter
Evolution and generalization of the seminal role of Mathematica
In web standards way
• Web (HTTP+Markdown)
• Python adoption (ipynb)
Written in Java
Python has an interop bridge...not native (if ever important)Python is a kernel for Jupyter
31. .NET Interactive and Jupyter
and Visual Studio Code
.NET Interactive gives C# and F# kernels to Jupyter
.NET Interactive gives all tools to create your hosting application independently from Jupyter
In Visual Studio Code, you have two different notebooks (looking similar but developed in parallel by
different teams)
.NET Interactive Notebook (by the .NET Interactive Team) that can run also Python
Jupyter Notebook (by the Azure Data Studio Team – probably) that can run also C# and F#
There is a little confusion on that
.NET Interactive has a strong C#/F# Kernel...
...a less mature infrastructure (compared to Jupiter)
34. .NET (5) hosting in Azure
Existing apps
.NET web apps (on-premises)
Cloud-Optimized
PaaS
Cloud-Native
PaaS for microservices and serverless
Monolithic / N-Tier
architectures
Monolithic / N-Tier
architectures
Microservices and serverless architectures
Cloud
Infrastructure-Ready
Monolithic / N-Tier
architectures
Relational
Database
VMs
Managed services
On-premises Azure
PaaS for containerized microservices
+ Serverless computing
+ Managed services
And Windows Containers
IaaS
(Infrastructure as a Service)
Azure Azure
35. Functions everywhere
Platform
App delivery
OS
On-premises
Code
App Service on Azure Stack
Windows
●●●
Non-Azure hosts
●●●
●●●
+
Azure Functions
host runtime
Azure Functions
Core Tools
Azure Functions
base Docker image
Azure Functions
.NET Docker image
Azure Functions
Node Docker image
●●●
36. Logic Apps
Visually design workflows in the cloud
Express logic through powerful control flow
Connect disparate functions and APIs
Utilize declarative definition to work with CI/CD
39. Azure Cognitive Services
Cognitive Services brings AI within reach of every developer—without requiring machine-learning
expertise. All it takes is an API call to embed the ability to see, hear, speak, search, understand, and
accelerate decision-making into your apps. Enable developers of all skill levels to easily add AI
capabilities to their apps.
Five areas:
• Decision
• Language
• Speech
• Vision
• Web search
Anomaly Detector
Identify potential problems early on.
Content Moderator
Detect potentially offensive or unwanted
content.
Metrics Advisor PREVIEW
Monitor metrics and diagnose issues.
Personalizer
Create rich, personalized experiences for
every user.
40. Anomaly Detector
Through an API, Anomaly Detector ingests time-series data of all types and selects the best-fitting
detection model for your data to ensure high accuracy. Customize the service to detect any level of
anomaly and deploy it where you need it most -- from the cloud to the intelligent edge with
containers. Azure is the only major cloud provider that offers anomaly detection as an AI service.
42. Anomaly Detector
Through an API, Anomaly Detector ingests time-series data of all types and selects the best-fitting
detection model for your data to ensure high accuracy. Customize the service to detect any level of
anomaly and deploy it where you need it most -- from the cloud to the intelligent edge with
containers. Azure is the only major cloud provider that offers anomaly detection as an AI service.
It seems too much simple…
43. Business metrics
monitoring
AIOps Predictive maintenance
Metrics Advisor(preview)
REST APIs (SDKs)
Data ingestion, detection/alert
configuration, anomaly query
APIs
Web-based workspace
Model customization & incident
diagnostics
44. Collect time-series data Detect anomalies Send incident alerts Analyze root cause
Metrics Monitoring Pipeline
A simple light Time-Series Insights-like service
46. Conclusions
Start simple and bulk: you already have data
If you have daily data, you need to aggregate (a month?) to have training
• take time for a correct Data Lake strategy
• there is time for realtime
The right algorithm is the one that gives you what you want to see
• Also professionals make the same (besides REAL data scientists)
• But if you know statistics, if better for you
Azure Cognitive Services will become more important
• New Metrics Advisor Service!
Anomaly detection is the process of identifying unexpected items or events in data sets, which differ from the norm. And anomaly detection is often applied on unlabeled data which is known as unsupervised anomaly detection.
https://towardsdatascience.com/effective-approaches-for-time-series-anomaly-detection-9485b40077f1
Effective Approaches for Time Series Anomaly Detection | by Aditya Bhattacharya | Towards Data Science
SSA works by decomposing a time-series into a set of principal components. These components can be interpreted as the parts of a signal that correspond to trends, noise, seasonality, and many other factors. Then, these components are reconstructed and used to forecast values some time in the future.
The Spectral Residual outlier detector is based on the paper Time-Series Anomaly Detection Service at Microsoft and is suitable for unsupervised online anomaly detection in univariate time series data. The algorithm first computes the Fourier Transform of the original data. Then it computes the spectral residual of the log amplitude of the transformed signal before applying the Inverse Fourier Transform to map the sequence back from the frequency to the time domain. This sequence is called the saliency map. The anomaly score is then computed as the relative difference between the saliency map values and their moving averages. If the score is above a threshold, the value at a specific timestep is flagged as an outlier. For more details, please check out the paper.
What’s next?
Modernize applications with .NET Core
Today we focused on Cloud-optimized .NET Framework apps. However, many applications will benefit from modern architecture built on .NET Core – a much faster, modular, cross-platform, open source .NET. Websites can be modernized with ASP.NET Core to bring in better security, compliance, and much better performance than ASP.NET on .NET Framework. .NET Core also provides code patterns for building resilient, high-performance microservices on Linux and Windows.
Build 2015
Metrics Advisor, a new platform-as-a-service, provides you an out-of-the-box intelligent metrics monitoring platform.
It simplifies the monitoring lifecycle with a built-in web-based workspace where you can setup time-series monitoring, alerting and diagnostics with a simple user interface.
A rich set of REST APIs and SDK libraries support developers to build your custom solutions easily. Because Metrics Advisor has built an end-to-end monitoring pipeline, time to value is accelerated.
The pipeline starts with abundant database connectors and pre-processing of your time-series data by cleaning, aggregating, and filling in gaps for a consistent time-series flow.
Built on Cognitive Services Anomaly Detector, the core engine for time-series anomaly detection, Metrics Advisor looks at each data set and automatically selects the best algorithm from the model pool for high accuracy.
Incidents trigger alerts through emails, webhooks, and Azure DevOps. With dimension tree and metrics graph, Metrics Advisor quickly shows you the key drivers of the problem.
Metrics Advisor is intelligent, scalable, and can be implemented in a simple manner.