If you’re dealing with big data, you definitely know how long it takes to finish a job. Imagine a situation where you have to run long-running jobs or perform parallel processing, and if you happen to manage servers hosted in AWS to do the processing, this is going to cost you a lot of processing hours which results in $$$$$. This is where batch processing solutions comes into place. Let’s get to know what batch processing is and how we can stand up a batch processing solution in AWS at a fraction of the lower cost.
Jobs that can run without end user involvement, or can be scheduled to run as resources permit, are called batch jobs. Batch processing is for those often used programs that can be executed with minimal human intervention. Batch processing can be called as a technique for automating multiple transactions and treating them as a single group.
The adaptability and flexibility of today’s cloud services present a lot of opportunities to cut infrastructure costs. Amazon Web Services and its plethora of services let you set up any kind of cloud environment for any type of application, without forcing you to make long-term commitments. At the very least, you don’t have to make a big initial investment to set up your cloud environments.
AWS resources are designed to make deploying cloud-native applications easy and affordable. Affordability is always important for businesses because cost-efficient applications guarantee higher returns on cloud investment. The way AWS services are set up allows for easy scaling of apps and cloud resource usage, but keeping your cloud environment efficient is not without its challenges.
#aws #amazon web services #cost #cost optimization #cost analysis #cost management #cost analytics #aws costs
Since 1994, Digital banking has been here. It is a very long time, but digital banking through mobile devices is entirely new to the banking industry. It all started when Atom became the first digital-only bank in the UK.
Nowadays, Tech-savvy customers expect corporations to support their digital movement, and because of this, almost every industry has adopted technologies to stay relevant with these modern customers. Most of the newbies who plan to develop a banking app have two questions in mind: “What is the cost of developing a banking application” and “Which hidden factors affect the cost of developing a banking app?”
You can get all the answers to these questions here, because this article will take you through the cost of developing a banking app, the features of banking apps, and much other pertinent information. After reading this, you will be able to plan better for your mobile banking app development. But before directly jumping into the cost of mobile banking app development, let’s take a look at the global digital payment market size of mobile banking.
According to GlobalNewsWire, by 2026, the Global Digital Payment Market size is estimated to reach $175.8 billion, rising at a market growth of 20% CAGR during the forecast period.
Around 23% of millennials use mobile banking apps daily.
Around 49.2% of total smartphone users use mobile banking apps.
41% of Americans said that mobile banking apps had minimized their concerns about managing finances.
Data Source: Statista
As you can see, the data clearly indicates that the percentage of smartphone users are increasing day-by-day. Therefore, by engaging in your own mobile banking app development currently, you will be able to take advantage of the growth in mobile users. But, the cost of developing a banking application depends on so many factors like the platform, features, technologies, and so on.
Cost of developing a banking app depends on various factors. To give you a rough idea of the mobile banking application development cost, the total development time for a fully-featured app sums to 3760 hours. Considering hourly rate for fintech projects of $25, the cost of developing a feature-loaded banking app stands around $94k.
Banking Application Development Cost depends on different phases such as:
It’s not easy to imagine an app that does not utilize this necessary mobile capability. Push notifications always increase your users’ engagement with your mobile banking app and encourage the desired action. Push notifications are of three types:
Transactional notifications notify users about their account updates.
The Application-based notifications indicate when the mobile banking app requires the user’s attention, whether related to the password change requests or document submissions.
Promotional notifications are to grab the attention of customers to offer discounts and attractive deals.
For most users, mobile banking has a steep learning curve, and due to that, the customer will require immediate assistance on various occasions. Hence, creating a chatbot for customer service is the best way for many institutions to improve their customer service availability. The chatbots will save you a lot of time and money, whilst providing customer support 24/7. But this feature has a separate development process, and therefore you have to pay separately for this.
Servers are where your mobile banking app will be hosted. If you are not with the largest enterprises, you will want to outsource hosting from Amazon, Azure, or Google, which will result in more costs.
A CDN is a system that is used to deliver content to the app based on the origin of the content, the content delivery server, and the geographic location of the particular user. In simple words, if you have users across the globe, and they have to keep coming back to one far off location to access the content, then the app will not perform in a good way. So, if you want your mobile banking app to perform effectively, you should use a content delivery network, because it reduces the app loading time and also increases the responsiveness of the app.
If you want to use paid deployment tools like iBuildApp, Appy Pie, and IBM MobileFirst, to develop your mobile banking apps, you will need to subscribe to them over the lifespan of your app. This will also affect your banking app development cost.
As we all know, both platforms constantly release updates, and those updates require maintenance. Depending on the extent of maintenance required, the cost in the long-term can sometimes be significant.
Every mobile app usually has multiple third-party APIs that they interact with, especially at the enterprise level. If you make changes to any of these applications, they will require periodic maintenance of your APIs. For instance, Facebook updated their API version four times in 2016; now, what if you want to integrate with Facebook? You will need to update your app to accommodate those changes.
As you know, every app has bugs, and not even a single developer can assure you that there will be no bugs in the future in your app. It’s just that sometimes they go undiscovered for months or even years. User communities are not kind to apps that are slow to address the issues that they have reported.
The cost of banking application development not only depend on the features of the banking application, but they are also heavily affected by the hidden factors I have mentioned. The primary issue with mobile banking app development cost is the amount of individual components that you need to gather. Each of them can cost thousands of dollars, and these costs will continue throughout the lifespan of your app. However, the rewards that come from a successful mobile banking app development project are huge.
Pro Tip: The cost of developing a banking application greatly depends on the hourly rates of programmers and the expertise of the team. FinTech experts are able to complete these projects much more efficiently.
#banking app development cost #banking application development cost #cost of developing a banking app #cost of developing a banking application #mobile banking app development cost #mobile banking application development cost
The final objective is to estimate the cost of a certain house in a Boston suburb. In 1970, the Boston Standard Metropolitan Statistical Area provided the information. To examine and modify the data, we will use several techniques such as data pre-processing and feature engineering. After that, we'll apply a statistical model like regression model to anticipate and monitor the real estate market.
Before using a statistical model, the EDA is a good step to go through in order to:
# Import the libraries #Dataframe/Numerical libraries import pandas as pd import numpy as np #Data visualization import plotly.express as px import matplotlib import matplotlib.pyplot as plt import seaborn as sns #Machine learning model from sklearn.linear_model import LinearRegression
#Reading the data path='./housing.csv' housing_df=pd.read_csv(path,header=None,delim_whitespace=True)
Crime: It refers to a town's per capita crime rate.
ZN: It is the percentage of residential land allocated for 25,000 square feet.
Indus: The amount of non-retail business lands per town is referred to as the indus.
CHAS: CHAS denotes whether or not the land is surrounded by a river.
NOX: The NOX stands for nitric oxide content (part per 10m)
RM: The average number of rooms per home is referred to as RM.
AGE: The percentage of owner-occupied housing built before 1940 is referred to as AGE.
DIS: Weighted distance to five Boston employment centers are referred to as dis.
RAD: Accessibility to radial highways index
TAX: The TAX columns denote the rate of full-value property taxes per $10,000 dollars.
B: B=1000(Bk — 0.63)2 is the outcome of the equation, where Bk is the proportion of blacks in each town.
PTRATIO: It refers to the student-to-teacher ratio in each community.
LSTAT: It refers to the population's lower socioeconomic status.
MEDV: It refers to the 1000-dollar median value of owner-occupied residences.
# Check if there is any missing values. housing_df.isna().sum() CRIM 0 ZN 0 INDUS 0 CHAS 0 NOX 0 RM 0 AGE 0 DIS 0 RAD 0 TAX 0 PTRATIO 0 B 0 LSTAT 0 MEDV 0 dtype: int64
No missing values are found
We examine our data's mean, standard deviation, and percentiles.
The crime, area, sector, nitric oxides, 'B' appear to have multiple outliers at first look because the minimum and maximum values are so far apart. In the Age columns, the mean and the Q2(50 percentile) do not match.
We might double-check it by examining the distribution of each column.
Because the model is overly generic, removing all outliers will underfit it. Keeping all outliers causes the model to overfit and become excessively accurate. The data's noise will be learned.
The approach is to establish a happy medium that prevents the model from becoming overly precise. When faced with a new set of data, however, they generalise well.
We'll keep numbers below 600 because there's a huge anomaly in the TAX column around 600.
The overall distribution, particularly the TAX, PTRATIO, and RAD, has improved slightly.
Perfect correlation is denoted by the clear values. The medium correlation between the columns is represented by the reds, while the negative correlation is represented by the black.
With a value of 0.89, we can see that 'MEDV', which is the medium price we wish to anticipate, is substantially connected with the number of rooms 'RM'. The proportion of black people in area 'B' with a value of 0.19 is followed by the residential land 'ZN' with a value of 0.32 and the percentage of black people in area 'ZN' with a value of 0.32.
The metrics that are most connected with price will be plotted.
Gradient descent is aided by feature scaling, which ensures that all features are on the same scale. It makes locating the local optimum much easier.
Mean standardization is one strategy to employ. It substitutes (target-mean) for the target to ensure that the feature has a mean of nearly zero.
def standard(X): '''Standard makes the feature 'X' have a zero mean''' mu=np.mean(X) #mean std=np.std(X) #standard deviation sta=(X-mu)/std # mean normalization return mu,std,sta mu,std,sta=standard(X) X=sta X
For the sake of the project, we'll apply linear regression.
Typically, we run numerous models and select the best one based on a particular criterion.
Linear regression is a sort of supervised learning model in which the response is continuous, as it relates to machine learning.
Form of Linear Regression
y= θX+θ1 or y= θ1+X1θ2 +X2θ3 + X3θ4
y is the target you will be predicting
0 is the coefficient
x is the input
We will Sklearn to develop and train the model
#Import the libraries to train the model from sklearn.model_selection import train_test_split from sklearn.linear_model import LinearRegression
Allow us to utilise the train/test method to learn a part of the data on one set and predict using another set using the train/test approach.
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) [7.22218258] 24.66379606613584
In this example, you will learn the model using below hypothesis:
Price= 24.85 + 7.18* Room
It is interpreted as:
For a decided price of a house:
A 7.18-unit increase in the price is connected with a growth in the number of rooms.
As a side note, this is an association, not a cause!
You will need a metric to determine whether our hypothesis was right. The RMSE approach will be used.
Root Means Square Error (RMSE) is defined as the square root of the mean of square error. The difference between the true and anticipated numbers called the error. It's popular because it can be expressed in y-units, which is the median price of a home in our scenario.
def rmse(predict,actual): return np.sqrt(np.mean(np.square(predict - actual))) # Split the Data into train and test set X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.4) #Create and Train the model model=LinearRegression().fit(X_train,y_train) #Generate prediction predictions_test=model.predict(X_test) #Compute loss to evaluate the model coefficient= model.coef_ intercept=model.intercept_ print(coefficient,intercept) loss=rmse(predictions_test,y_test) print('loss: ',loss) print(model.score(X_test,y_test)) #accuracy [7.43327725] 24.912055881970886 loss: 3.9673165450580714 0.7552661033654667 Loss will be 3.96
This means that y-units refer to the median value of occupied homes with 1000 dollars.
This will be less by 3960 dollars.
While learning the model you will have a high variance when you divide the data. Coefficient and intercept will vary. It's because when we utilized the train/test approach, we choose a set of data at random to place in either the train or test set. As a result, our theory will change each time the dataset is divided.
This problem can be solved using a technique called cross-validation.
With 'Forward Selection,' we'll iterate through each parameter to assist us choose the numbers characteristics to include in our model.
We'll use a random state of 1 so that each iteration yields the same outcome.
cols= los= los_train= scor= i=0 while i < len(high_corr_var): cols.append(high_corr_var[i]) # Select inputs variables X=new_df[cols] #mean normalization mu,std,sta=standard(X) X=sta # Split the data into training and testing X_train,X_test,y_train,y_test= train_test_split(X,y,random_state=1) #fit the model to the training lnreg=LinearRegression().fit(X_train,y_train) #make prediction on the training test prediction_train=lnreg.predict(X_train) #make prediction on the testing test prediction=lnreg.predict(X_test) #compute the loss on train test loss=rmse(prediction,y_test) loss_train=rmse(prediction_train,y_train) los_train.append(loss_train) los.append(loss) #compute the score score=lnreg.score(X_test,y_test) scor.append(score) i+=1
We have a big 'loss' with a smaller collection of variables, yet our system will overgeneralize in this scenario. Although we have a reduced 'loss,' we have a large number of variables. However, if the model grows too precise, it may not generalize well to new data.
In order for our model to generalize well with another set of data, we might use 6 or 7 features. The characteristic chosen is descending based on how strong the price correlation is.
high_corr_var ['RM', 'ZN', 'B', 'CHAS', 'RAD', 'DIS', 'CRIM', 'NOX', 'AGE', 'TAX', 'INDUS', 'PTRATIO', 'LSTAT']
With 'RM' having a high price correlation and LSTAT having a negative price correlation.
# Create a list of features names feature_cols=['RM','ZN','B','CHAS','RAD','CRIM','DIS','NOX'] #Select inputs variables X=new_df[feature_cols] # Split the data into training and testing sets X_train,X_test,y_train,y_test= train_test_split(X,y, random_state=1) # feature engineering mu,std,sta=standard(X) X=sta # fit the model to the trainning data lnreg=LinearRegression().fit(X_train,y_train) # make prediction on the testing test prediction=lnreg.predict(X_test) # compute the loss loss=rmse(prediction,y_test) print('loss: ',loss) lnreg.score(X_test,y_test) loss: 3.212659865936143 0.8582338376696363
The test set yielded a loss of 3.21 and an accuracy of 85%.
Other factors, such as alpha, the learning rate at which our model learns, could still be tweaked to improve our model. Alternatively, return to the preprocessing section and working to increase the parameter distribution.
For more details regarding scraping real estate data you can contact Scraping Intelligence today
Bob had just arrived in the office for his first day of work as the newly hired chief technical officer when he was called into a conference room by the president, Martha, who immediately introduced him to the head of accounting, Amanda. They exchanged pleasantries, and then Martha got right down to business:
“Bob, we have several teams here developing software applications on Amazon and our bill is very high. We think it’s unnecessarily high, and we’d like you to look into it and bring it under control.”
Martha placed a screenshot of the Amazon Web Services (AWS) billing report on the table and pointed to it.
“This is a problem for us: We don’t know what we’re spending this money on, and we need to see more detail.”
Amanda chimed in, “Bob, look, we have financial dimensions that we use for reporting purposes, and I can provide you with some guidance regarding some information we’d really like to see such that the reports that are ultimately produced mirror these dimensions — if you can do this, it would really help us internally.”
“Bob, we can’t stress how important this is right now. These projects are becoming very expensive for our business,” Martha reiterated.
“How many projects do we have?” Bob inquired.
“We have four projects in total: two in the aviation division and two in the energy division. If it matters, the aviation division has 75 developers and the energy division has 25 developers,” the CEO responded.
Bob understood the problem and responded, “I’ll see what I can do and have some ideas. I might not be able to give you retrospective insight, but going forward, we should be able to get a better idea of what’s going on and start to bring the cost down.”
The meeting ended with Bob heading to find his desk. Cost allocation tags should help us, he thought to himself as he looked for someone who might know where his office is.
#aws #aws cloud #node js #cost optimization #aws cli #well architected framework #aws cost report #cost control #aws cost #aws tags
Learn about the positive impact of cloud computing on mobile app development, and how Cloud Technology will help reduce your app development cost.
Organizations need to understand that cloud technology is pretty much necessary to maintain continuity. With truncated costing, even the bootstrapped start-ups can afford advanced level mobile app development and expand their business rapidly. Even the leading mobile app developers would prefer cloud computing for developing better solutions.
Cloud computing is the flamboyance that you need to take your business ahead as it helps you correctly structure mobile app development.
#cloud technology reducing app development cost #cloud computing service providing companies #impact of cloud computing #cloud technology #cloud computing #cloud computing