Learn About CueObserve: Help You Keep Track Of Your Metrics

CueObserve helps you monitor your metrics. Know when, where, and why a metric isn't right.

CueObserve uses timeseries Anomaly detection to find where and when a metric isn't right. It then offers one-click Root Cause analysis so that you know why a metric isn't right.

CueObserve works with data in your SQL data warehouses and databases. It currently supports Snowflake, BigQuery, Redshift, Druid, Postgres, MySQL, SQL Server and ClickHouse.

CueObserve Anomaly CueObserve RCA

Getting Started

Install via Docker

wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose.yml -q -O cueobserve-docker-compose.ymldocker-compose -f cueobserve-docker-compose.yml up -d

Now visit http://localhost:3000 in your browser.

Demo Video

Watch CueObserve video

How it works

You write a SQL GROUP BY query, map its columns as dimensions and measures, and save it as a virtual Dataset.

Dataset SQL

Dataset Schema Map

You then define one or more anomaly detection jobs on the dataset.

Anomaly Definition

When an anomaly detection job runs, CueObserve does the following:

  1. Executes the SQL GROUP BY query on your data warehouse and stores the result as a Pandas dataframe.
  2. Generates one or more timeseries from the dataframe, as defined in your anomaly detection job.
  3. Generates a forecast for each timeseries using Prophet.
  4. Creates a visual card for each timeseries. Marks the card as an anomaly if the last data point is anomalous.

Features

  • Automated SQL to timeseries transformation.
  • Run anomaly detection on the aggregate metric or split it by any dimension. Limit the split to significant dimension values.
  • Use Prophet or simple mathematical rules to detect anomalies.
  • In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.
  • Slack alerts when anomalies are detected.
  • Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs.

Limitations

  • Currently supports Prophet for timeseries forecasting.
  • Not being built for real-time anomaly detection on streaming data.

Support

For general help using CueObserve, read the documentation, or go to Github Discussions.

To report a bug or request a feature, open an issue.

Contributing

We'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an issue or a discussion. Contributors are expected to adhere to our code of conduct.

Author: cuebook
Source code: https://github.com/cuebook/CueObserve
License: Apache-2.0 License

#cueobserve  #sql 

What is GEEK

Buddha Community

Learn About CueObserve: Help You Keep Track Of Your Metrics

Neural Networks Intuitions: 9. Distance Metric Learning

Welcome back to my series _Neural Networks Intuitions. _In this ninth segment, we will be looking into deep distance metric learning, the motivation behind using it, wide range of methods proposed and its applications.

Note: All techniques discussed in this article comes under Deep Metric Learning (DML) i.e distance metric learning using neural networks.


Distance Metric Learning:

Distance Metric Learning means learning a distance in a low dimensional space which is consistent with the notion of semantic similarity. (as given in [No Fuss Distance Metric Learning using Proxies])

What does the above statement mean w.r.t image domain?

It means learning a distance in a low dimensional space(non-input space) such that similar images in the input space result in similar representation(low distance) and dissimilar images result in varied representation(high distance).

Okay, this sounds exactly what a classifier does. Isn’t it? Yes.

So how is this different from supervised image classification? Why different terminology?

Metric learning addresses the problem of open-set setup in machine learning i.e generalize to new examples at test time.

This is not possible by a feature-extractor followed by fully connected layer Classification network.

Why?

This is a very important question. The answer is as follows:

  1. A classifier learns**class-specific features and not necessarily generic features.**
  2. A classifier with a standard cross entropy loss maximizes inter-class distances such that the features before FC layer are linearly separable.

#metric-learning #deep-learning #siamese-networks #triplet-loss #representation-learning #deep learning

Jerad  Bailey

Jerad Bailey

1598891580

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Beth  Cooper

Beth Cooper

1659694200

Easy Activity Tracking for Models, Similar to Github's Public Activity

PublicActivity

public_activity provides easy activity tracking for your ActiveRecord, Mongoid 3 and MongoMapper models in Rails 3 and 4.

Simply put: it can record what happens in your application and gives you the ability to present those recorded activities to users - in a similar way to how GitHub does it.

!! WARNING: README for unreleased version below. !!

You probably don't want to read the docs for this unreleased version 2.0.

For the stable 1.5.X readme see: https://github.com/chaps-io/public_activity/blob/1-5-stable/README.md

About

Here is a simple example showing what this gem is about:

Example usage

Tutorials

Screencast

Ryan Bates made a great screencast describing how to integrate Public Activity.

Tutorial

A great step-by-step guide on implementing activity feeds using public_activity by Ilya Bodrov.

Online demo

You can see an actual application using this gem here: http://public-activity-example.herokuapp.com/feed

The source code of the demo is hosted here: https://github.com/pokonski/activity_blog

Setup

Gem installation

You can install public_activity as you would any other gem:

gem install public_activity

or in your Gemfile:

gem 'public_activity'

Database setup

By default public_activity uses Active Record. If you want to use Mongoid or MongoMapper as your backend, create an initializer file in your Rails application with the corresponding code inside:

For Mongoid:

# config/initializers/public_activity.rb
PublicActivity.configure do |config|
  config.orm = :mongoid
end

For MongoMapper:

# config/initializers/public_activity.rb
PublicActivity.configure do |config|
  config.orm = :mongo_mapper
end

(ActiveRecord only) Create migration for activities and migrate the database (in your Rails project):

rails g public_activity:migration
rake db:migrate

Model configuration

Include PublicActivity::Model and add tracked to the model you want to keep track of:

For ActiveRecord:

class Article < ActiveRecord::Base
  include PublicActivity::Model
  tracked
end

For Mongoid:

class Article
  include Mongoid::Document
  include PublicActivity::Model
  tracked
end

For MongoMapper:

class Article
  include MongoMapper::Document
  include PublicActivity::Model
  tracked
end

And now, by default create/update/destroy activities are recorded in activities table. This is all you need to start recording activities for basic CRUD actions.

Optional: If you don't need #tracked but still want the comfort of #create_activity, you can include only the lightweight Common module instead of Model.

Custom activities

You can trigger custom activities by setting all your required parameters and triggering create_activity on the tracked model, like this:

@article.create_activity key: 'article.commented_on', owner: current_user

See this entry http://rubydoc.info/gems/public_activity/PublicActivity/Common:create_activity for more details.

Displaying activities

To display them you simply query the PublicActivity::Activity model:

# notifications_controller.rb
def index
  @activities = PublicActivity::Activity.all
end

And in your views:

<%= render_activities(@activities) %>

Note: render_activities is an alias for render_activity and does the same.

Layouts

You can also pass options to both activity#render and #render_activity methods, which are passed deeper to the internally used render_partial method. A useful example would be to render activities wrapped in layout, which shares common elements of an activity, like a timestamp, owner's avatar etc:

<%= render_activities(@activities, layout: :activity) %>

The activity will be wrapped with the app/views/layouts/_activity.html.erb layout, in the above example.

Important: please note that layouts for activities are also partials. Hence the _ prefix.

Locals

Sometimes, it's desirable to pass additional local variables to partials. It can be done this way:

<%= render_activity(@activity, locals: {friends: current_user.friends}) %>

Note: Before 1.4.0, one could pass variables directly to the options hash for #render_activity and access it from activity parameters. This functionality is retained in 1.4.0 and later, but the :locals method is preferred, since it prevents bugs from shadowing variables from activity parameters in the database.

Activity views

public_activity looks for views in app/views/public_activity.

For example, if you have an activity with :key set to "activity.user.changed_avatar", the gem will look for a partial in app/views/public_activity/user/_changed_avatar.html.(|erb|haml|slim|something_else).

Hint: the "activity." prefix in :key is completely optional and kept for backwards compatibility, you can skip it in new projects.

If you would like to fallback to a partial, you can utilize the fallback parameter to specify the path of a partial to use when one is missing:

<%= render_activity(@activity, fallback: 'default') %>

When used in this manner, if a partial with the specified :key cannot be located it will use the partial defined in the fallback instead. In the example above this would resolve to public_activity/_default.html.(|erb|haml|slim|something_else).

If a view file does not exist then ActionView::MisingTemplate will be raised. If you wish to fallback to the old behaviour and use an i18n based translation in this situation you can specify a :fallback parameter of text to fallback to this mechanism like such:

<%= render_activity(@activity, fallback: :text) %>

i18n

Translations are used by the #text method, to which you can pass additional options in form of a hash. #render method uses translations when view templates have not been provided. You can render pure i18n strings by passing {display: :i18n} to #render_activity or #render.

Translations should be put in your locale .yml files. To render pure strings from I18n Example structure:

activity:
  article:
    create: 'Article has been created'
    update: 'Someone has edited the article'
    destroy: 'Some user removed an article!'

This structure is valid for activities with keys "activity.article.create" or "article.create". As mentioned before, "activity." part of the key is optional.

Testing

For RSpec you can first disable public_activity and add require helper methods in the rails_helper.rb with:

#rails_helper.rb
require 'public_activity/testing'

PublicActivity.enabled = false

In your specs you can then blockwise decide whether to turn public_activity on or off.

# file_spec.rb
PublicActivity.with_tracking do
  # your test code goes here
end

PublicActivity.without_tracking do
  # your test code goes here
end

Documentation

For more documentation go here

Common examples

Set the Activity's owner to current_user by default

You can set up a default value for :owner by doing this:

  1. Include PublicActivity::StoreController in your ApplicationController like this:
class ApplicationController < ActionController::Base
  include PublicActivity::StoreController
end
  1. Use Proc in :owner attribute for tracked class method in your desired model. For example:
class Article < ActiveRecord::Base
  tracked owner: Proc.new{ |controller, model| controller.current_user }
end

Note: current_user applies to Devise, if you are using a different authentication gem or your own code, change the current_user to a method you use.

Disable tracking for a class or globally

If you need to disable tracking temporarily, for example in tests or db/seeds.rb then you can use PublicActivity.enabled= attribute like below:

# Disable p_a globally
PublicActivity.enabled = false

# Perform some operations that would normally be tracked by p_a:
Article.create(title: 'New article')

# Switch it back on
PublicActivity.enabled = true

You can also disable public_activity for a specific class:

# Disable p_a for Article class
Article.public_activity_off

# p_a will not do anything here:
@article = Article.create(title: 'New article')

# But will be enabled for other classes:
# (creation of the comment will be recorded if you are tracking the Comment class)
@article.comments.create(body: 'some comment!')

# Enable it again for Article:
Article.public_activity_on

Create custom activities

Besides standard, automatic activities created on CRUD actions on your model (deactivatable), you can post your own activities that can be triggered without modifying the tracked model. There are a few ways to do this, as PublicActivity gives three tiers of options to be set.

Instant options

Because every activity needs a key (otherwise: NoKeyProvided is raised), the shortest and minimal way to post an activity is:

@user.create_activity :mood_changed
# the key of the action will be user.mood_changed
@user.create_activity action: :mood_changed # this is exactly the same as above

Besides assigning your key (which is obvious from the code), it will take global options from User class (given in #tracked method during class definition) and overwrite them with instance options (set on @user by #activity method). You can read more about options and how PublicActivity inherits them for you here.

Note the action parameter builds the key like this: "#{model_name}.#{action}". You can read further on options for #create_activity here.

To provide more options, you can do:

@user.create_activity action: 'poke', parameters: {reason: 'bored'}, recipient: @friend, owner: current_user

In this example, we have provided all the things we could for a standard Activity.

Use custom fields on Activity

Besides the few fields that every Activity has (key, owner, recipient, trackable, parameters), you can also set custom fields. This could be very beneficial, as parameters are a serialized hash, which cannot be queried easily from the database. That being said, use custom fields when you know that you will set them very often and search by them (don't forget database indexes :) ).

Set owner and recipient based on associations

class Comment < ActiveRecord::Base
  include PublicActivity::Model
  tracked owner: :commenter, recipient: :commentee

  belongs_to :commenter, :class_name => "User"
  belongs_to :commentee, :class_name => "User"
end

Resolve parameters from a Symbol or Proc

class Post < ActiveRecord::Base
  include PublicActivity::Model
  tracked only: [:update], parameters: :tracked_values
  
  def tracked_values
   {}.tap do |hash|
     hash[:tags] = tags if tags_changed?
   end
  end
end

Setup

Skip this step if you are using ActiveRecord in Rails 4 or Mongoid

The first step is similar in every ORM available (except mongoid):

PublicActivity::Activity.class_eval do
  attr_accessible :custom_field
end

place this code under config/initializers/public_activity.rb, you have to create it first.

To be able to assign to that field, we need to move it to the mass assignment sanitizer's whitelist.

Migration

If you're using ActiveRecord, you will also need to provide a migration to add the actual field to the Activity. Taken from our tests:

class AddCustomFieldToActivities < ActiveRecord::Migration
  def change
    change_table :activities do |t|
      t.string :custom_field
    end
  end
end

Assigning custom fields

Assigning is done by the same methods that you use for normal parameters: #tracked, #create_activity. You can just pass the name of your custom variable and assign its value. Even better, you can pass it to #tracked to tell us how to harvest your data for custom fields so we can do that for you.

class Article < ActiveRecord::Base
  include PublicActivity::Model
  tracked custom_field: proc {|controller, model| controller.some_helper }
end

Help

If you need help with using public_activity please visit our discussion group and ask a question there:

https://groups.google.com/forum/?fromgroups#!forum/public-activity

Please do not ask general questions in the Github Issues.


Author: public-activity
Source code: https://github.com/public-activity/public_activity
License: MIT license

#ruby  #ruby-on-rails 

Learn About CueObserve: Help You Keep Track Of Your Metrics

CueObserve helps you monitor your metrics. Know when, where, and why a metric isn't right.

CueObserve uses timeseries Anomaly detection to find where and when a metric isn't right. It then offers one-click Root Cause analysis so that you know why a metric isn't right.

CueObserve works with data in your SQL data warehouses and databases. It currently supports Snowflake, BigQuery, Redshift, Druid, Postgres, MySQL, SQL Server and ClickHouse.

CueObserve Anomaly CueObserve RCA

Getting Started

Install via Docker

wget https://raw.githubusercontent.com/cuebook/CueObserve/latest_release/docker-compose.yml -q -O cueobserve-docker-compose.ymldocker-compose -f cueobserve-docker-compose.yml up -d

Now visit http://localhost:3000 in your browser.

Demo Video

Watch CueObserve video

How it works

You write a SQL GROUP BY query, map its columns as dimensions and measures, and save it as a virtual Dataset.

Dataset SQL

Dataset Schema Map

You then define one or more anomaly detection jobs on the dataset.

Anomaly Definition

When an anomaly detection job runs, CueObserve does the following:

  1. Executes the SQL GROUP BY query on your data warehouse and stores the result as a Pandas dataframe.
  2. Generates one or more timeseries from the dataframe, as defined in your anomaly detection job.
  3. Generates a forecast for each timeseries using Prophet.
  4. Creates a visual card for each timeseries. Marks the card as an anomaly if the last data point is anomalous.

Features

  • Automated SQL to timeseries transformation.
  • Run anomaly detection on the aggregate metric or split it by any dimension. Limit the split to significant dimension values.
  • Use Prophet or simple mathematical rules to detect anomalies.
  • In-built Scheduler. CueObserve uses Celery as the executor and celery-beat as the scheduler.
  • Slack alerts when anomalies are detected.
  • Monitoring. Slack alert when a job fails. CueObserve maintains detailed logs.

Limitations

  • Currently supports Prophet for timeseries forecasting.
  • Not being built for real-time anomaly detection on streaming data.

Support

For general help using CueObserve, read the documentation, or go to Github Discussions.

To report a bug or request a feature, open an issue.

Contributing

We'd love contributions to CueObserve. Before you contribute, please first discuss the change you wish to make via an issue or a discussion. Contributors are expected to adhere to our code of conduct.

Author: cuebook
Source code: https://github.com/cuebook/CueObserve
License: Apache-2.0 License

#cueobserve  #sql 

sophia tondon

sophia tondon

1620898103

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert