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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Introduction to Amazon API Gateway using AWS Lambda

P
Pavan Kumar Last Updated : 02 Jan, 2023
6 min read

Introduction

What is an API?

In simple terms, API is a messenger; let’s understand this with some examples. Let’s say you are hungry and you need to cook something at home. If you want to make noodles, you just take the ingredients out of the cupboard, fire up the stove, and make it yourself. This is a program doing something on its own with its own resources. But say you want pizza – you don’t have the ingredients, and your home oven isn’t really that suited for making a nice crispy crust. So you go to a pizza place instead. But unlike at home, you can’t just go into the kitchen and start using their ingredients to make a pizza. They don’t want your grubby hands all over their stuff! So what will you do?  You have to go to the counter and make an order – there will be a menu listing what pizzas you can order and what toppings or other options you can pick. This is an API.

An application Programming Interface (API) is a set of rules specifying how two software programs should interact.

An API allows one software program to access the functionality of another software program. For example, if you have a software program that needs to retrieve data from the internet, it can use an API to request that data from a server. The server will then respond with the requested data, and the software program can use it as needed.

APIs are used to allow different software programs to communicate with each other and share data and functionality. They are an important part of modern software development, as they allow different systems to work together and enable the creation of more complex and powerful software applications.

HTTP APIs

HTTP API is a type of API that uses the HTTP protocol for sending and receiving data. It allows software programs to send and receive data using HTTP requests and responses. These requests and responses can be in various formats, such as plain text, JSON, or XML. They are commonly used in a wide range of applications, including web applications, mobile apps, and microservices. These APIs are relatively cheaper than REST APIs and have less functionalities than the later.

REST APIs

REST API is a type of API that follows a set of architectural principles called REST (Representational State Transfer). REST is a style of software architecture that defines a set of constraints for creating web APIs.

Stateful APIs

A stateful API is an API that maintains information about each client request and uses this information to process subsequent requests. This means that the API stores data about each request, such as the request parameters, in a server-side session. This data is then used to process subsequent requests from the same client.

Stateless APIs

A stateless API, on the other hand, does not maintain information about client requests. It processes each request independently, without storing any data about previous requests. This means that the API does not maintain a server-side session and does not use data from previous requests to process subsequent requests.

Key Differences between Stateful and Stateless APIs

Below are some key differences between stateful and stateless APIs:

STATEFUL APIs

STATELESS APIs
1. Require a server-side session to store data about client requests 1. Do not require a server-side session to store data about client requests
2. These may sometimes be slower than Stateless APIs because they require and store data which takes time. 2. Stateless APIs are faster because these do not require and store data about requests.
3. Stateful APIs are not easy to scale  3. Easier to scale as they do not need to maintain data about previous requests.
4. Generally considered less secure than Stateless APIs 4. These are generally considered more secure.

 

What is Amazon API Gateway?

Amazon API Gateway is an AWS Service that is used to create, maintain and monitor both stateful (websocket) and stateless (HTTP and REST) APIs. We can use these APIs to access:

  1. Any AWS service
  2. The data stored in the AWS cloud (such as an S3 bucket)
  3. Any other web services.

If you are an API developer, you can easily make your APIs to third-party developers too.

There are mainly two types of users of AWS API Gateway.

  1. i) API Developers who create and deploy an API to enable the required functionality in API Gateway
  2. ii) App Developers who are customers of the API developer.

Amazon API Gateway Architecture

architecture

This architecture illustrates how serverless applications can be built with consistent and integrated developer experience. From end users to the data centers, API Gateway handles all the tasks involved, such as accepting and processing thousands of concurrent calls, traffic management, authorization, monitoring, access control, etc.

Amazon API Gateway Features

  1. It supports both stateful and stateless APIs. (Examples: Websocket, HTTP, and REST).
  2. Powerful authentication mechanisms include AWS Identity, Access Management Policies, and Lambda authorizer.
  3. Developer portal where APIs developers can publish their APIs.
  4. Execution and access logging for CloudWatch, including the option to set alarms.
  5. Integration with other AWS Services such as AWS Lambda and AWS Kinesis.
  6. Integration with AWS WAF is used for protection against web exploits, and AWS X-Ray is used to understand and emphasize performance latencies.

Getting Started with Amazon API Gateway

Now we are going to create a serverless API. In a serverless API, we can focus on our applications instead of spending time managing servers. It works like this:

  • API is invoked by the client
  • API sends request to lambda
  • Lambda executes the lambda function and sends the result back to the API
  • After receiving the result from lambda, API responds to the client

http api

Step 1 – Create a lambda function

The Lambda function is used for the backend of our API. Lambda runs the code only when it is needed. It also scales automatically from a few requests per day to thousand requests every second.

create lambada function

Creating a lambda function:

1) Go to the lambda console at https://console.aws.amazon.com/lambda

2) Click on Create function.

3) Enter “my-function” as the function name.

4) Choose Python 3.9 as runtime.

5) For this function we won’t need to change anything in the Permissions and Advanced Settings tab as the default permissions are sufficient for this demonstration.

4) Click on Create function.

STEP 2 – Creating an HTTP API

Amazon API

The HTTP API provides an HTTP endpoint for your Lambda function. It works like this:

  • Client submits query to the HTTP API
  • The API triggers lambda which then executes the lambda function
  • Lambda sends the result to the API
  • API responds to the client with the received response

Steps to create a HTTP API

1) Visit API Gateway console at https://console.aws.amazon.com/apigateway

2) Click on Build to create your first HTTP API

3) Click on Add integration for integration

4) Select Lambda.

5) Enter your lambda function my-function.

6) For API name, enter my-http-api.

7) Click on Next.

8) Review the route created for you, and then choose Next.

9) Review the stage created for you, and then choose Next.

10) Click on Create.

STEP 3 Test your API –

Next, we need to test our API to make sure that it’s working. For this we will use a web browser to invoke our API.

Amazon API

To test our API

  1. Go to the API Gateway console at https://console.aws.amazon.com/apigateway
  2. Select your API.
  3. Note down your API’s invoke URL. (see the image above)
  4. Copy your API’s invoke URL, and paste it in a web browser. Join the invoke URL and the name of your lambda function to call your Lambda function. The API Gateway console creates a route using the name of your Lambda function “my-function” by default. 

    The full URL should look like https://abcdef123.execute-api.us-east-2.amazonaws.com/my-function.When you load this URL, a GET request is sent by your browser to the API

  1. You should see the text “Hello from Lambda!” in your browser. Hence your API’s response is verified.

Amazon API

Conclusion

We have created our first HTTP API using Amazon API Gateway and AWS Lambda. Further, we can explore various use cases of the same, and similarly, we can create REST API also, which provides more functionalities.

Did you like this article? Tell us your thoughts in the comment below. Also, don’t forget to mention which article you’d like to read next.

P
Pavan Kumar

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