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Epsilon India

Software Product Success: 3 Key Operational Excellence Drivers

Software products, offered as Software as a Service (SaaS) subscription model, attract significant investor and market interest as they provide clients with one of the quickest paths to realize business value. While most of the focus remains on the core product functionality, features, and usability—and quite justifiably so— the ultimate business success of a product hinges on the customers’ experience with the product and the quantifiable customer value realized. The science and engineering of SaaS operational excellence result from a relentless focus on defining, executing, measuring, and improving standard operating procedures in response to incidents and client requests. This, along with constant innovation, focused on improvising customer experience. Operational excellence as a strategic differentiator is not as often spoken about, but it remains a crucial part of value unlocking. In this article, we examine a few key capabilities that the product operations function needs to develop to help companies differentiate their products through functional (feature) and operational innovations. 1. Evolve DevOps culture to the reliability of a system focused Site Reliability Engineering (SRE) mindset DevOps and SRE are best visualized as integrated practices in the continuous value chain of reliably launching and running product features that meet an ever-evolving client need. Simply put, DevOps practices focus on enabling fast, efficient, and continuous development and deployment of software product features. SRE brings in the user-centric view of how these product features are experienced, focusing on the system’s reliability impact on that experience. This requires a cultural shift to view system reliability not just as an operations problem but as a critical responsibility where all teams, including engineering, products, and operations, have a stake. SRE is both a practice and skillset that organizations must invest in to build capacity. This practice will help reorient engineering and operations functions into an integrated whole governed by metrics that impact the end user. This perspective is critically important to break down organization silos, foster collaboration, and define the true ‘North Star’ of software product's success. SRE, as a function infused and housed in operations will have a culturally transformational effect on how customer experience is perceived and impacted by all processes that contribute to achieving that outcome. 2. Develop hyper-automation capabilities and deploy at scale for constant operational improvements Hyper automation is a discipline of systematically infusing automation into IT and Business operations across all activities and processes of software products. The outcome of such automation initiatives often leads to the reimagination of operational processes resulting in new ways of working. Hyper automation initiatives that heavily leverage operational data to derive operational insights for preventive and predictive maintenance lead to a discipline of Machine Learning Operations (MLOps), a process of building, testing, and maintaining ML models as an operations discipline which can lead to impactful outcomes for both the business and clients. Hyper automation is a holy grail of current operations with quantum benefits impacting operational efficiencies, reduced toil, improved employee morale, and an impact on improved customer experience. Deploying automation at scale requires organizations to reorient their approach to operations from a cost function to a value-generating function. Operations automation is a key driver for expanding people capabilities, overcoming operational, and technical debt, and creating continuous operating leverage, which can plow back efficiencies quarter after quarter. The discipline of building, growing and executing an automation pipeline with the same rigor as the product feature backlog will deliver operational savings on an ongoing basis. 3. Align operational success metrics with key client success metrics Organization silos sometimes lack visibility and a line-of-sight between the work functional teams perform and its impact on client experience. A DevOps culture helps address the issue in some ways, but the metrics tracked here are still somewhat technical. There is a need for a more direct alignment of business with operations, with both speaking the same language. This calls for technical leaders to build more profound domain expertise and talk about the lingo of an industry that impacts client outcomes. The impact on client experience can be significant, with an organizational capability to translate key client impacting operational improvement initiatives to business benefits relatable to clients. This will build a virtuous cycle of aligning all corporate efforts to deliver a superior client experience. Developing a customer-centric approach to operational excellence can have a transformational impact on an organization’s culture and the morale of teams. It will add a renewed sense of purpose with a streamlined alignment of organizational goals with day-to-day tasks when the positive outcome of these initiatives can be linked to the acknowledgment of progress, reward, and recognition. This will lead to superior customer satisfaction and, ultimately, product success. The author is Senior Vice President of SaaS Operations and Engineering at Epsilon India. Madhavan Krishnan ,

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THE EVENING SKY AT THE VERNAL EQUINOX

THE STARS OF SPRING

Inside Earth: The Journey of Conru

THE EVENING SKY AT THE AUTUMNAL EQUINOX

Introduction We all look for a to get faster where we want to go, shortcut Who has never quickly flip through some pages of a book or even go straight to the last chapter? Although this may seem like a good idea at the beginning, I’ve already shared in several blog posts multiple reasons why a gradual improvement is much better and more productive than going straight to fun things. The truth is, if you want to code for several years and do not burn out in the first years or even months of a career as a web developer you have to study the basics, I tell you a secret,  even when, after years of experience, when you believe that there is nothing you do not know about a basic topic, pick up the first book or one of the first articles you read, you will always find a paragraph that you did not pay attention to or a concept that you did not fully understand before. This post, in fact, is for everyone. As beginners, they are just starting their journey with to expert programmers who will discover features of the code they did not know. PHP Even on a basic topic like what we’re going to see below, Here, in fact, there is general and detailed information on variables in PHP. I’ll give you some helpful insight on what are and how to declare and use variables in the best way for your web applications. This post is part of the series “PHP basics for expert programmers” click on the links below for other posts published so far. PHP basics for expert web developers Construct and Comments of PHP 7 When writing a program, whether it is a simple to-do list or an integrated payment system for a bank that has clients worldwide you are going to need to use variables. Variables are used to store data. Think of variables as if they were boxes. In every language, a “box” has a name written on the outside which is the name by which the box is called and a “thing” inside. programming As said every box (variable) has a name, when we give a name to the box and define what is inside it we say that we assign a value to a variable. There are different types of “things” that a box can have inside, the variables are in fact divided into three main categories: scalar type variables, composite variables and resources. The scalars are the most basic and easy to understand. They are divided into: Boolean Integer Float Strings The composites ones are a bit more complicated but nothing impossible, they are: Arrays Objects Callables Iterables Then we have , which can be seen simply as, for example, objects external to PHP as files, database connections. resources Finally, we have the type, which is used for variables that do not have an assigned value, NULL it is not possible to transmit to a NULL type but it is possible to assign a value of NULL to a variable. Now, PHP is a loosely-typed language, which means that a variable can change its contents during runtime. In fact, the value can change from string to integer to boolean without creating any problem with the script and even without throwing any errors. When it comes to variables, a fundamental part to understand is the so-called casting, that is, how the variable is seen by the programming language. As you’ve seen, in PHP variables can change type and understand how these changes happen behind the scenes is an essential part of the work of a web developer (not to mention the number of migraines that you will avoid). Imagine that you have a type-string variable $str containing “10”, imagine that, a couple of lines below you sum the variable with another number, let’s say $str = "10";$num = $str + 5;// $num will take the value of 15 PHP is smart enough to understand that the intention was to add the number 10 contained in the string with the number 5. The operation will parse successfully without errors and the value of $num will be 15. The reasoning that PHP does is called “casting” and I am sure you already understood that it is a powerful ace in our sleeve but at the same time can easily turn into a double-edged sword. You will see all kinds of variables and their declarations below, for further information I invite you to take a look at the official manual. Boolean variables Understanding how the Boolean variables work is the easiest thing in programming. A boolean variable indicates only one bit, thus only two values: 1 or 0, it can be true or false, yes or no, there is no other alternative, Boolean is the value that is considered during conditional statements like if, else, and cases of the switch-case. There are several rules to keep in mind regarding casting on Boolean variables. Empty arrays are counted as 0 or false; unless you consider empty stings, they are always considered true; if a string contains the number 0 the Boolean value is considered 0 or false; if a string contains any other number when cast as a boolean the value becomes true; every integer or float that does not have the value of 0 is to be considered true, this means that a negative value is true as well. The guys at techterms.com have created a brief explanation of what Boolean means and how it can be used on search conditions and search engines Check their definition Integer variables Integers are all the numbers that belong to the whole-number category. For integers, I intend all the negative and positive 0 included. Are also to be considered integer numbers in bases different from 10, in fact, in PHP you can use numbers based on binary, octal, decimal, and hexadecimal; You must use a prefix that will set the type of base you want to use 0b indicates numbers based on binary, “0b1100100” represents the value 100 in base 2, 0 followed by a number indicates that the number is to be considered octal 100 (base 10) becomes 0o144 in base (8). The prefix to use what you intend to indicate hexadecimal numbers instead is’ 0x 0x64 is the hexadecimal value to use for 100 (base 10). The range of numbers available depends on how powerful your computer is. The maximum value that variables of type integer can have is (for PC running 32 bit) and about , for 64-bit platforms. 2 billion 9E18, yes 18 zeroes The maximum value of type integer in 64-bit platform is 1000000000000000000 You can modify this limit using constants like PHP_INT_SIZE, PHP_INT_MAX and PHP_INT_MIN. But unless you work for Elon Musk and you are in charge of developing the software that will make us land on Mars I really discourage their use. (if you are and you have decided to use PHP, well, good luck with that). To be honest, it is possible to overcome this limit without using the constants but the variables will no longer be considered as integers but will be transformed into floats, here is an example available in the official manual of PHP: $number = 2147483647;// this is an integer on 32-bit machines$number = 2147483648;// this is a float on 32-bit machines You can also cast variables and make them integer using the keywords (int) or (integer) followed by the number you intend to cast. Note also that when you cast a value to integer PHP does not round above or below but simply cuts the portion of the decimal number 100.57 becomes 100 -100.57 becomes -100 when you cast a string in an integer, it will give 0 unless the string starts with a number “10 o ‘clock” becomes 10; “half past 10” becomes 0; Last note to consider is dealing with irrational numbers. look here, echo (int) ((0.1 + 0.7) * 10); Would you expect that the result would be 0.8 right? not exactly. 0.7 can be expressed as a rational number based on decimal but not on base 2. The values echoed would be 0.7. Since there is only a limited number of bits available to be saved it is inevitable that there is a loss of precision during these calculations. To be clear the internal representation of the code above will be something like 7.9999999999999991118… The degree of accuracy depends on the machine on which PHP runs, below there is a small snippet created by that allows you to check the accuracy level of your system. Andrew Beak This value is known as the machine epsilon, or unit roundoff, and is the smallest acceptable difference in calculations. $pi = 3.14159625;Indian $ = 3.2; $epsilon = 0.00001;if (abs($pi - $Indian) < $epsilon) {echo "Those values look the same";} else {echo "Those values are different";} This code checks if the values are equal to five degrees of precision. Float variables Unlike integer, float or double numbers are all numbers that are not whole-number, To make it simple, the numbers that have a comma are to be considered float. Although, as for the integer, the range of these variables depend on the power of the platform on which we work, PHP uses a standard called IEEE 754 in order to limit errors to a minimum. We have already seen however that for values such as 0.1 and 0.7 there is no exact representation which involves some issues. As regards the conversion of float variables, the procedure is rather simple and depends on the type of source variable. To integer the procedure is almost natural, just add 0.n to the number and there you go! here is a float or double number. The procedure on strings is a bit more complicated but there will be no errors if you continue to follow the rules and the string contains appropriate values. $var = 10 + "100.3";// $var is afloat and its value is 110.3$var = 7 + "11.2 kg of ham";// $var is a float and its value is 18.2 String variables After describing variables that contain numbers, now let’s talk about variables that contain characters, A character is worth one byte, which means that PHP has only 256 character characters available. PHP strings are a series of bytes. The information stored inside PHP regarding strings are their length and the content, 32-bit systems support strings as large as 2GB whereas 64-bit systems do not have any particular limit. A string can be declared using two different methods, the simple method does not evaluate control characters and variables and the complex method that takes into account variables’ value. To use the simple method you need to use the single-quote-marks while for the complex you would use the double-quote-marks $language = 'PHP';$simple = 'I code in $language';echo $simple;// The string echoed will be: I code in $language$complex = "I code in $language";echo $complex;// The string echoed will be: I code in PHP As you can see the double quotes evaluates the $language variable and show its actual value. This is a huge advantage because let you create complete strings of text without the necessity of using concatenation, which in my opinion make the code much simpler to read $city = 'London';echo 'I am a Web Developer based in ' . $city;echo "I am a Web Developer based in $city"; Did you see what I mean? A little tip that I use some time to specify to the PHP parser that I want to evaluate a variable is to use curly braces. $burger = "hamburger";echo "I love $burgers";// I love $burgersecho "I love {$burger}s";// I love hamburgers Since is not a defined variable, in the first example PHP will not evaluate it and show a plain string, in the second example the curly braces indicate that you want the variable to be evaluated so it will be shown in a plural context. $burgers You will see that there are more examples of proficient use of curly braces with strings such as using them when evaluating data within arrays and objects. Conclusion As you have also seen a basic element of programming as the variables have so much to hide and must be deepened in such a way as to be fully understood. In this blog post, we focused on scalar variables, which are the most common and elementary variables, Stay tuned, In the next episode of the series, we’re going to study composite variables like null array objects and resources. Also if you haven’t read the other parts of this series The renaissance of PHP Construct and Comments of PHP 7 Now that you understand this other basic concept of PHP it’s time to practice and start creating your own brand online. You can do it now with hosting services like Blue Host that allows you to create an account and put your work on the worldwide showcase that is the internet. Originally published at anastasionico.uk .

How to use variables (PHP 7)

Linear regression is a fundamental statistical technique used to model the relationship between a dependent variable and one or more independent variables. In simple terms, it helps us understand how changes in the independent variables are associated with changes in the dependent variable. In machine learning and data analysis, linear regression holds significant importance. It serves as a foundational tool for various tasks, including prediction, forecasting, and understanding the underlying patterns in data. It provides a simple yet powerful framework for making predictions based on historical data, making it a cornerstone in the field of predictive modeling. Moreover, linear regression also serves as a basis for more complex models and techniques in machine learning, making it a crucial concept to grasp for anyone working with data. What is Linear Regression The basic idea behind linear regression is to find the best-fitting line (or hyperplane in higher dimensions) that minimizes the difference between the predicted values and the actual values of a dependent variable based on one or more independent variables. In simple linear regression, there is one dependent variable (Y) and one independent variable (X). The relationship between them is modeled as: Here, Y represents the dependent variable (the one we want to predict). X represents the independent variable (the one used to make predictions). Beta_0 ​ is the intercept (the point where the line crosses the Y-axis). Beta_1 is the slope (the rate of change of Y with respect to X). epsilon represents the error term, which accounts for the variability in Y that is not explained by X. The goal of linear regression is to find the best values for Beta_0 and Beta_1 that minimize the sum of squared errors (the vertical distance between the actual data points and the predicted values on the line). In multiple linear regression, there are multiple independent variables. The relationship is extended as: Here, X_1, X_2, …, X_n represents the independent variables and Beta_1, Beta_2,…,Beta_n are the respective coefficients. Linear regression allows us to quantify the relationship between the dependent variable and the independent variable(s). It helps us understand how changes in the independent variables are associated with changes in the dependent variable. This understanding is crucial for making predictions, drawing insights, and making informed decisions based on data. Now, lets start implementing simple linear regression and multiple linear regression using sklearn. In this article, I’ll use the Dataset from Kaggle. California House Price Simple Linear Regression with Sklearn To demonstrate simple linear regression using the sklearn library, we'll use a California house price prediction dataset from Kaggle. Importing Libraries import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error Load the Dataset data = pd.read_csv('california_housing.csv')

# Explore the dataset
print(data.head()) Prepare the Data For this demonstration, we'll use the 'median_income' column as the independent variable ( ) and the 'median_house_value' column as the dependent variable ( ). X y X = data[['median_income']]
y = data['median_house_value'] Split the Data into Training and Testing Sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) Initialize and Train the Model # Initialize the model
model = LinearRegression()

# Train the model on the training data
model.fit(X_train, y_train) Make Predictions # Use the trained model to make predictions on the test set
y_pred = model.predict(X_test) Evaluate the Model # Calculate the Mean Squared Error (MSE) to evaluate the model's performance
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

# -------------- OUTPUT -------------- #
Mean Squared Error: 7091157771.76555 Multiple Linear Regression with Skearn Multiple linear regression extends the concept of simple linear regression to incorporate multiple independent variables. This allows us to model relationships between a dependent variable and two or more independent variables. Let's use the California housing dataset for this demonstration. Import Libraries import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error Load Dataset data = pd.read_csv('california_housing.csv')

# Explore the dataset
print(data.head()) Prepare the Data For this demonstration, we'll use 'median_income', 'total_rooms', and 'population' as the independent variables ( ) and 'median_house_value' as the dependent variable ( ). X y X = data[['median_income', 'total_rooms', 'population']]
y = data['median_house_value'] Splitting Dataset X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) Initialize and Train the Model # Initialize the model
model = LinearRegression()

# Train the model on the training data
model.fit(X_train, y_train) Make Predictions # Use the trained model to make predictions on the test set
y_pred = model.predict(X_test) Evaluate the Model # Calculate the Mean Squared Error (MSE) to evaluate the model's performance
mse = mean_squared_error(y_test, y_pred)
print(f'Mean Squared Error: {mse}')

# ---------------- OUTPUT -------------- #
Mean Squared Error: 7056856633.523101 I used Mean Squared Error evaluation metric to evaluate both the models, but there are multiple evaluation metrics that you can use for a model like this. Lets now learn about all the different evaluation metrics. Model Evaluation and Performance Metrics Evaluating the performance of a linear regression model is crucial to understand how well it fits the data and makes accurate predictions. Here are some common metrics used for this purpose: Mean Absolute Error (MAE): : The Mean Absolute Error is the average of the absolute differences between the predicted and actual values. It measures the average magnitude of errors. Definition : MAE provides a straightforward and easy-to-interpret measure of model performance. It's expressed in the same units as the target variable, making it intuitive to understand. Significance Mean Squared Error (MSE): : The Mean Squared Error is the average of the squared differences between the predicted and actual values. It gives more weight to large errors compared to MAE. Definition : MSE penalizes larger errors more heavily. It is useful when larger errors are considered more problematic (e.g., in situations where outliers have a significant impact on the model's performance). Significance Root Mean Squared Error (RMSE): : The Root Mean Squared Error is the square root of the MSE. It provides a measure of the average magnitude of the errors, but in the same units as the dependent variable. Definition : RMSE is particularly useful because it is in the same units as the target variable. This makes it easy to interpret and compare with the original data. Significance R-squared (R²): : R-squared is a statistical measure that represents the proportion of the variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, where 1 indicates a perfect fit. Definition : R-squared provides an overall indication of how well the model explains the variability in the data. A higher R-squared value indicates a better fit. However, it doesn't tell you about the magnitude or direction of individual errors. Significance Adjusted R-squared: : Adjusted R-squared is an adjusted version of R-squared that takes into account the number of independent variables in the model. It penalizes the inclusion of unnecessary variables. Definition : It provides a more accurate measure of the model's explanatory power when there are multiple independent variables. It helps prevent overfitting by accounting for the complexity of the model. Significance Interpretation: : "On average, our model's predictions are off by X units." MAE : "The average squared prediction error of our model is X." MSE/RMSE : "X% of the variance in the dependent variable is explained by the independent variables in our model." R-squared Choosing the appropriate evaluation metric depends on the specific problem and the importance of different types of errors. For instance, in some cases, minimizing the impact of large errors (MSE/RMSE) might be more critical, while in others, a clear and intuitive interpretation (MAE) might be preferred. R-squared provides a broad overview of the model's fit, but it's important to consider other metrics in conjunction with it.

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