Evaluate and Monitor ML Models From Validation to Production

Evidently

An open-source framework to evaluate, test and monitor ML models in production.

📊 What is Evidently?

Evidently is an open-source Python library for data scientists and ML engineers. It helps evaluate, test, and monitor the performance of ML models from validation to production.

Evidently has a modular approach with 3 interfaces on top of the shared metrics functionality.

1. Tests: batch model checks

Tests perform structured data and ML model quality checks. They verify a condition and return an explicit pass or fail result.

You can create a custom Test Suite from 50+ individual tests or run a preset (for example, Data Drift or Regression Performance). You can get results as an interactive visual dashboard inside Jupyter notebook or Colab, or export as JSON or Python dictionary.

Tests are best for automated batch model checks. You can integrate them as a pipeline step using tools like Airlfow.

2. Reports: interactive dashboards

Note We added a new Report object starting from v0.1.57.dev0. Reports unite the functionality of Dashboards and JSON profiles with a new, cleaner API. You can still use the old Dashboards API but it will soon be depreciated.

Reports calculate various data and ML metrics and render rich visualizations. You can create a custom Report or run a preset to evaluate a specific aspect of the model or data performance. For example, a Data Quality or Classification Performance report.

You can get an HTML report (best for exploratory analysis and debugging) or export results as JSON or Python dictionary (best for logging, documention or to integrate with BI tools).

3. Real-time ML monitoring

Note This functionality is in development and subject to API change.

Evidently has monitors that collect data and model metrics from a deployed ML service. You can use it to build live monitoring dashboards. Evidently configures the monitoring on top of streaming data and emits the metrics in Prometheus format. There are pre-built Grafana dashboards to visualize them.

👩‍💻 Installing from PyPI

MAC OS and Linux

Evidently is available as a PyPI package. To install it using pip package manager, run:

$ pip install evidently

If you only want to get results as HTML or JSON files, the installation is now complete. To display the dashboards inside a Jupyter notebook, you need jupyter nbextension. After installing evidently, run the two following commands in the terminal from the evidently directory.

To install jupyter nbextension, run:

$ jupyter nbextension install --sys-prefix --symlink --overwrite --py evidently

To enable it, run:

$ jupyter nbextension enable evidently --py --sys-prefix

That's it! A single run after the installation is enough.

Note: if you use Jupyter Lab, the reports might not display in the notebook. However, you can still save them as HTML files.

Windows

Evidently is available as a PyPI package. To install it using pip package manager, run:

$ pip install evidently

Unfortunately, building reports inside a Jupyter notebook is not yet possible for Windows. The reason is Windows requires administrator privileges to create symlink. In later versions we will address this issue. You can still generate the HTML to view externally.

▶️ Getting started

Note This is a simple Hello World example. You can find a complete Getting Started Tutorial in the docs.

Jupyter Notebook

To start, prepare your data as two pandas DataFrames. The first should include your reference data, the second - current production data. The structure of both datasets should be identical. To run some of the evaluations (e.g. Data Drift), you need input features only. In other cases (e.g. Target Drift, Classification Performance), you need Target and/or Prediction.

Option 1: Test Suites

After installing the tool, import Evidently test suite and required presets. We'll use a simple toy dataset:

import pandas as pd

from sklearn import datasets

from evidently.test_suite import TestSuite
from evidently.test_preset import DataStabilityTestPreset
from evidently.test_preset import DataQualityTestPreset

iris_data = datasets.load_iris(as_frame='auto')
iris_frame = iris_data.frame

To run the Data Stability test suite and display the reports in the notebook:

data_stability= TestSuite(tests=[
    DataStabilityTestPreset(),
])
data_stability.run(current_data=iris_frame.iloc[:90], reference_data=iris_frame.iloc[90:], column_mapping=None)
data_stability 

To save the results as an HTML file:

data_stability.save_html("file.html")

You'll need to open it from the destination folder.

To get the output as JSON:

data_stability.json()

Option 2: Reports

After installing the tool, import Evidently report and required presets:

import pandas as pd

from sklearn import datasets

from evidently.report import Report
from evidently.metric_preset import DataDriftPreset

iris_data = datasets.load_iris(as_frame='auto')
iris_frame = iris_data.frame

To generate the Data Drift report, run:

data_drift_report = Report(metrics=[
    DataDriftPreset(),
])

data_drift_report.run(current_data=iris_frame.iloc[:90], reference_data=iris_frame.iloc[90:], column_mapping=None)
data_drift_report

To save the report as HTML:

data_drift_report.save_html("file.html")

You'll need to open it from the destination folder.

To get the output as JSON:

data_drift_report.json()

💻 Contributions

We welcome contributions! Read the Guide to learn more.

📚 Documentation

For more information, refer to a complete Documentation. You can start with this Tutorial for a quick introduction.

🗂️ Examples

Here you can find simple examples on toy datasets to quickly explore what Evidently can do right out of the box.

Report	Jupyter notebook	Colab notebook	Contents
Getting Started Tutorial	link	link	Data Stability and custom test suites, Data Drift and Target Drift reports
Evidently Metric Presets	link	link	Data Drift, Target Drift, Data Quality, Regression, Classification reports
Evidently Metrics	link	link	All individual metrics
Evidently Test Presets	link	link	NoTargetPerformance, Data Stability, Data Quality, Data Drift Regression, Milti-class Classification, Binary Classification, Binary Classification top-K test suites
Evidently Tests	link	link	All individual tests

Integrations

See how to integrate Evidently in your prediction pipelines and use it with other tools.

Title	link to tutorial
Real-time ML monitoring with Grafana	Evidently + Grafana
Batch ML monitoring with Airflow	Evidently + Airflow
Log Evidently metrics in MLflow UI	Evidently + MLflow

☎️ User Newsletter

To get updates on new features, integrations and code tutorials, sign up for the Evidently User Newsletter.

✅ Discord Community

If you want to chat and connect, join our Discord community!

Docs | Discord Community | User Newsletter | Blog | Twitter

Download Details:

Author: Evidentlyai
Source Code: https://github.com/evidentlyai/evidently 
License: Apache-2.0 license

#machinelearning #datascience #pandas #dataframes #jupyter #notebook 

What is GEEK

Buddha Community

How to Monitor Third Party API Integrations

Many enterprises and SaaS companies depend on a variety of external API integrations in order to build an awesome customer experience. Some integrations may outsource certain business functionality such as handling payments or search to companies like Stripe and Algolia. You may have integrated other partners which expand the functionality of your product offering, For example, if you want to add real-time alerts to an analytics tool, you might want to integrate the PagerDuty and Slack APIs into your application.

If you’re like most companies though, you’ll soon realize you’re integrating hundreds of different vendors and partners into your app. Any one of them could have performance or functional issues impacting your customer experience. Worst yet, the reliability of an integration may be less visible than your own APIs and backend. If the login functionality is broken, you’ll have many customers complaining they cannot log into your website. However, if your Slack integration is broken, only the customers who added Slack to their account will be impacted. On top of that, since the integration is asynchronous, your customers may not realize the integration is broken until after a few days when they haven’t received any alerts for some time.

How do you ensure your API integrations are reliable and high performing? After all, if you’re selling a feature real-time alerting, you’re alerts better well be real-time and have at least once guaranteed delivery. Dropping alerts because your Slack or PagerDuty integration is unacceptable from a customer experience perspective.

What to monitor

Latency

Specific API integrations that have an exceedingly high latency could be a signal that your integration is about to fail. Maybe your pagination scheme is incorrect or the vendor has not indexed your data in the best way for you to efficiently query.

Latency best practices

Average latency only tells you half the story. An API that consistently takes one second to complete is usually better than an API with high variance. For example if an API only takes 30 milliseconds on average, but 1 out of 10 API calls take up to five seconds, then you have high variance in your customer experience. This is makes it much harder to track down bugs and harder to handle in your customer experience. This is why 90th percentile and 95th percentiles are important to look at.

Reliability

Reliability is a key metric to monitor especially since your integrating APIs that you don’t have control over. What percent of API calls are failing? In order to track reliability, you should have a rigid definition on what constitutes a failure.

Reliability best practices

While any API call that has a response status code in the 4xx or 5xx family may be considered an error, you might have specific business cases where the API appears to successfully complete yet the API call should still be considered a failure. For example, a data API integration that returns no matches or no content consistently could be considered failing even though the status code is always 200 OK. Another API could be returning bogus or incomplete data. Data validation is critical for measuring where the data returned is correct and up to date.

Not every API provider and integration partner follows suggested status code mapping

Availability

While reliability is specific to errors and functional correctness, availability and uptime is a pure infrastructure metric that measures how often a service has an outage, even if temporary. Availability is usually measured as a percentage of uptime per year or number of 9’s.

AVAILABILITY %DOWNTIME PER YEARDOWNTIME PER MONTHDOWNTIME PER WEEKDOWNTIME PER DAY90% (“one nine”)36.53 days73.05 hours16.80 hours2.40 hours99% (“two nines”)3.65 days7.31 hours1.68 hours14.40 minutes99.9% (“three nines”)8.77 hours43.83 minutes10.08 minutes1.44 minutes99.99% (“four nines”)52.60 minutes4.38 minutes1.01 minutes8.64 seconds99.999% (“five nines”)5.26 minutes26.30 seconds6.05 seconds864.00 milliseconds99.9999% (“six nines”)31.56 seconds2.63 seconds604.80 milliseconds86.40 milliseconds99.99999% (“seven nines”)3.16 seconds262.98 milliseconds60.48 milliseconds8.64 milliseconds99.999999% (“eight nines”)315.58 milliseconds26.30 milliseconds6.05 milliseconds864.00 microseconds99.9999999% (“nine nines”)31.56 milliseconds2.63 milliseconds604.80 microseconds86.40 microseconds

Usage

Many API providers are priced on API usage. Even if the API is free, they most likely have some sort of rate limiting implemented on the API to ensure bad actors are not starving out good clients. This means tracking your API usage with each integration partner is critical to understand when your current usage is close to the plan limits or their rate limits.

Usage best practices

It’s recommended to tie usage back to your end-users even if the API integration is quite downstream from your customer experience. This enables measuring the direct ROI of specific integrations and finding trends. For example, let’s say your product is a CRM, and you are paying Clearbit $199 dollars a month to enrich up to 2,500 companies. That is a direct cost you have and is tied to your customer’s usage. If you have a free tier and they are using the most of your Clearbit quota, you may want to reconsider your pricing strategy. Potentially, Clearbit enrichment should be on the paid tiers only to reduce your own cost.

How to monitor API integrations

Monitoring API integrations seems like the correct remedy to stay on top of these issues. However, traditional Application Performance Monitoring (APM) tools like New Relic and AppDynamics focus more on monitoring the health of your own websites and infrastructure. This includes infrastructure metrics like memory usage and requests per minute along with application level health such as appdex scores and latency. Of course, if you’re consuming an API that’s running in someone else’s infrastructure, you can’t just ask your third-party providers to install an APM agent that you have access to. This means you need a way to monitor the third-party APIs indirectly or via some other instrumentation methodology.

#monitoring #api integration #api monitoring #monitoring and alerting #monitoring strategies #monitoring tools #api integrations #monitoring microservices

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How to validate mobile phone number in laravel with example

Data validation and sanitization is a very important thing from security point of view for a web application. We can not rely on user’s input. In this article i will let you know how to validate mobile phone number in laravel with some examples.

if we take some user’s information in our application, so usually we take phone number too. And if validation on the mobile number field is not done, a user can put anything in the mobile number field and without genuine phone number, this data would be useless.

Since we know that mobile number can not be an alpha numeric or any alphabates aand also it should be 10 digit number. So here in this examples we will add 10 digit number validation in laravel application.

We will aalso see the uses of regex in the validation of mobile number. So let’s do it with two different way in two examples.

Example 1:

In this first example we will write phone number validation in HomeController where we will processs user’s data.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|digits:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

Example 2:

In this second example, we will use regex for user’s mobile phone number validation before storing user data in our database. Here, we will write the validation in Homecontroller like below.

<?php

namespace App\Http\Controllers;

use Illuminate\Http\Request;
use App\User;
use Validator;

class HomeController extends Controller
{
    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function create()
    {
        return view('createUser');
    }

    /**
     * Show the application dashboard.
     *
     * @return \Illuminate\Http\Response
     */
    public function store(Request $request)
    {
        $request->validate([
                'name' => 'required',
                'phone' => 'required|regex:/^([0-9\s\-\+\(\)]*)$/|min:10',
                'email' => 'required|email|unique:users'
            ]);

        $input = $request->all();
        $user = User::create($input);

        return back()->with('success', 'User created successfully.');
    }
}

#laravel #laravel phone number validation #laravel phone validation #laravel validation example #mobile phone validation in laravel #phone validation with regex #validate mobile in laravel

Variables in Python

In this Python tutorial for beginners, we learn about Variables in Python. Variables are containers for storing data values. A Python variable is a symbolic name that is a reference or pointer to an object.

Code in GitHub: https://github.com/AlexTheAnalyst/PythonYouTubeSeries/blob/main/Python%20Basics%20101%20-%20Variables.ipynb 

---

Creating Variables

Python has no command for declaring a variable.

A variable is created the moment you first assign a value to it.

Example

x = 5
y = "John"
print(x)
print(y)

Variables do not need to be declared with any particular type, and can even change type after they have been set.

Example

x = 4       # x is of type int
x = "Sally" # x is now of type str
print(x)

Casting

If you want to specify the data type of a variable, this can be done with casting.

Example

x = str(3)    # x will be '3'
y = int(3)    # y will be 3
z = float(3)  # z will be 3.0

Get the Type

You can get the data type of a variable with the type() function.

Example

x = 5
y = "John"
print(type(x))
print(type(y))

Single or Double Quotes?

String variables can be declared either by using single or double quotes:

Example

x = "John"
# is the same as
x = 'John'

Case-Sensitive

Variable names are case-sensitive.

Example

This will create two variables:

a = 4
A = "Sally"
#A will not overwrite a

---

Python Variables: How to Define/Declare String Variable Types

What is a Variable in Python?

A Python variable is a reserved memory location to store values. In other words, a variable in a python program gives data to the computer for processing.

Python Variable Types

Every value in Python has a datatype. Different data types in Python are Numbers, List, Tuple, Strings, Dictionary, etc. Variables in Python can be declared by any name or even alphabets like a, aa, abc, etc.

In this tutorial, we will learn,

• How to Declare and use a Variable
• Re-declare a Variable
• Concatenate Variables
• Local & Global Variables
• Delete a variable

How to Declare and use a Variable

Let see an example. We will define variable in Python and declare it as “a” and print it.

a=100 
print (a)

Re-declare a Variable

You can re-declare Python variables even after you have declared once.

Here we have Python declare variable initialized to f=0.

Later, we re-assign the variable f to value “guru99”

Python 2 Example

# Declare a variable and initialize it
f = 0
print f
# re-declaring the variable works
f = 'guru99'
print f

Python 3 Example

# Declare a variable and initialize it
f = 0
print(f)
# re-declaring the variable works
f = 'guru99'
print(f)

Python String Concatenation and Variable

Let’s see whether you can concatenate different data types like string and number together. For example, we will concatenate “Guru” with the number “99”.

Unlike Java, which concatenates number with string without declaring number as string, while declaring variables in Python requires declaring the number as string otherwise it will show a TypeError

For the following code, you will get undefined output –

a="Guru"
b = 99
print a+b

Once the integer is declared as string, it can concatenate both “Guru” + str(“99”)= “Guru99” in the output.

a="Guru"
b = 99
print(a+str(b))

Python Variable Types: Local & Global

There are two types of variables in Python, Global variable and Local variable. When you want to use the same variable for rest of your program or module you declare it as a global variable, while if you want to use the variable in a specific function or method, you use a local variable while Python variable declaration.

Let’s understand this Python variable types with the difference between local and global variables in the below program.

1. Let us define variable in Python where the variable “f” is global in scope and is assigned value 101 which is printed in output
2. Variable f is again declared in function and assumes local scope. It is assigned value “I am learning Python.” which is printed out as an output. This Python declare variable is different from the global variable “f” defined earlier
3. Once the function call is over, the local variable f is destroyed. At line 12, when we again, print the value of “f” is it displays the value of global variable f=101

Python 2 Example

# Declare a variable and initialize it
f = 101
print f
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print f
someFunction()
print f

Python 3 Example

# Declare a variable and initialize it
f = 101
print(f)
# Global vs. local variables in functions
def someFunction():
# global f
    f = 'I am learning Python'
    print(f)
someFunction()
print(f)

While Python variable declaration using the keyword global, you can reference the global variable inside a function.

1. Variable “f” is global in scope and is assigned value 101 which is printed in output
2. Variable f is declared using the keyword global. This is NOT a local variable, but the same global variable declared earlier. Hence when we print its value, the output is 101

We changed the value of “f” inside the function. Once the function call is over, the changed value of the variable “f” persists. At line 12, when we again, print the value of “f” is it displays the value “changing global variable”

Python 2 Example

f = 101;
print f
# Global vs.local variables in functions
def someFunction():
  global f
  print f
  f = "changing global variable"
someFunction()
print f

Python 3 Example

f = 101;
print(f)
# Global vs.local variables in functions
def someFunction():
  global f
  print(f)
  f = "changing global variable"
someFunction()
print(f)

Delete a variable

You can also delete Python variables using the command del “variable name”.

In the below example of Python delete variable, we deleted variable f, and when we proceed to print it, we get error “variable name is not defined” which means you have deleted the variable.

Example of Python delete variable or Python clear variable :

f = 11;
print(f)
del f
print(f)

Summary:

• Variables are referred to “envelop” or “buckets” where information can be maintained and referenced. Like any other programming language Python also uses a variable to store the information.
• Variables can be declared by any name or even alphabets like a, aa, abc, etc.
• Variables can be re-declared even after you have declared them for once
• Python constants can be understood as types of variables that hold the value which can not be changed. Usually Python constants are referenced from other files. Python define constant is declared in a new or separate file which contains functions, modules, etc.
• Types of variables in Python or Python variable types : Local & Global
• Declare local variable when you want to use it for current function
• Declare Global variable when you want to use the same variable for rest of the program

To delete a variable, it uses keyword “del”.

---

A Beginner’s Guide To Python Variables

A variable is a fundamental concept in any programming language. It is a reserved memory location that stores and manipulates data. This tutorial on Python variables will help you learn more about what they are, the different data types of variables, the rules for naming variables in Python. You will also perform some basic operations on numbers and strings. We’ll use Jupyter Notebook to implement the Python codes.

Variables are entities of a program that holds a value. Here is an example of a variable:

x=100 

In the below diagram, the box holds a value of 100 and is named as x. Therefore, the variable is x, and the data it holds is the value.

The data type for a variable is the type of data it holds. 

In the above example, x is holding 100, which is a number, and the data type of x is a number.

In Python, there are three types of numbers: Integer, Float, and Complex.

Integers are numbers without decimal points. Floats are numbers with decimal points. Complex numbers have real parts and imaginary parts.

Another data type that is very different from a number is called a string, which is a collection of characters.

Let’s see a variable with an integer data type:

x=100

To check the data type of x, use the type() function:

type(x)

Python allows you to assign variables while performing arithmetic operations.

x=654*6734
type(x)

To display the output of the variable, use the print() function.

print(x) #It gives the product of the two numbers

Now, let’s see an example of a floating-point number:

x=3.14
print(x)

type(x) #Here the type the variable is float

Strings are declared within a single or double quote.

x=’Simplilearn’

print(x)

x=” Simplilearn.”

print(x)

type(x)

In all of the examples above, we only assigned a single value to the variables. Python has specific data types or objects that hold a collection of values, too. A Python List is one such example.

Here is an example of a list:

x=[14,67,9]

print(x)

type(x)

You can extract the values from the list using the index position method. In lists, the first element index position starts at zero, the second element at one, the third element at two, and so on.

To extract the first element from the list x:

print(x[0])

To extract the third element from the list x:

print(x[2])

Lists are mutable objects, which means you can change the values in a list once they are declared.

x[2]=70 #Reassigning the third element in the list to 70

print(x)

Earlier, the elements in the list had [14, 67, 9]. Now, they have [14, 67, 70].

Tuples are a type of Python object that holds a collection of value, which is ordered and immutable. Unlike a list that uses a square bracket, tuples use parentheses.

x=(4,8,6)

print(x)

type(x)

Similar to lists, tuples can also be extracted with the index position method.

print(x[1]) #Give the element present at index 1, i.e. 8

If you want to change any value in a tuple, it will throw an error. Once you have stored the values in a variable for a tuple, it remains the same.

When we deal with files, we need a variable that points to it, called file pointers. The advantage of having file pointers is that when you need to perform various operations on a file, instead of providing the file’s entire path location or name every time, you can assign it to a particular variable and use that instead.

Here is how you can assign a variable to a file:

x=open(‘C:/Users/Simplilearn/Downloads/JupyterNotebook.ipynb’,’r’) 

type(x)

Suppose you want to assign values to multiple variables. Instead of having multiple lines of code for each variable, you can assign it in a single line of code.

(x, y, z)=5, 10, 5

The following line code results in an error because the number of values assigned doesn’t match with the number of variables declared.

If you want to assign the same value to multiple variables, use the following syntax:

x=y=z=1

Now, let's look at the various rules for naming a variable.

1. A variable name must begin with a letter of the alphabet or an underscore(_)

Example:

abc=100 #valid syntax

    _abc=100 #valid syntax

    3a=10 #invalid syntax

    @abc=10 #invalid syntax

. The first character can be followed by letters, numbers or underscores.

Example:

a100=100 #valid

    _a984_=100 #valid

    a9967$=100 #invalid

    xyz-2=100 #invalid

Python variable names are case sensitive.

Example:

a100 is different from A100.

    a100=100

  A100=200

Reserved words cannot be used as variable names.

Example:

break, class, try, continue, while, if

break=10

class=5

try=100

Python is more effective and more comfortable to perform when you use arithmetic operations.

The following is an example of adding the values of two variables and storing them in a third variable:

x=20

y=10

result=x+y

print(result)

Similarly, we can perform subtraction as well.

result=x-y

print(result)

Additionally, to perform multiplication and division, try the following lines of code:

result=x*y

print(result)

result=x/y

print(result)

As you can see, in the case of division, the result is not an integer, but a float value. To get the result of the division in integers, use “//” — the integer division.

The division of two numbers gives you the quotient. To get the remainder, use the modulo (%) operator.

Now that we know how to perform arithmetic operations on numbers let us look at some operations that can be performed on string variables.

var = ‘Simplilearn’

You can extract each character from the variable using the index position. Similar to lists and tuples, the first element position starts at index zero, the second element index at one, and so on.

print(var[0]) #Gives the character at index 0, i.e. S

print(var[4]) #Gives the character at index 4, i.e. l

If you want to extract a range of characters from the string variable, you can use a colon (:) and provide the range between the ones you want to receive values from. The last index is always excluded. Therefore, you should always provide one plus the number of characters you want to fetch. 

print(var[0:3]) #This will extract the first three characters from zero, first, and second index.

The same operation can be performed by excluding the starting index.

print(var[:3])

The following example prints the values from the fifth location until the end of the string.

Let’s see what happens when you try to print the following:

print(var[0:20]) #Prints the entire string, although the string does not have 20 characters.

To print the length of a string, use the len() function.

len(var)

Let’s see how you can extract characters from two strings and generate a new string.

var1 = “It’s Sunday”

var2 = “Have a great day”

The new string should say, “It’s a great Sunday” and be stored in var3.

var3 = var1[:5] + var2[5:13] + var1[5:]

print(var3)

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Conclusion

I hope this blog helped you learn the concepts of Python variables. After reading this blog, you may have learned more about what a variable is, rules for declaring a variable, how to perform arithmetic operations on variables, and how to extract elements from numeric and string variables using the index position.

#python #programming