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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

Machine Learning Model – Serverless Deployment

asha
asha Last Updated : 14 Dec, 2020
5 min read

Introduction

Read this article on machine learning model deployment using serverless deployment. Serverless compute abstracts away provisioning, managing
severs and configuring software, simplifying model deployment.

Aimed towards becoming a Full Stack Data Scientist.

 

What is a serverless deployment?

Serverless is the next step in Cloud Computing. This means that servers are simply hidden from the picture. In serverless computing, this separation of server and application is managed by using a platform. The responsibility of the platform or serverless provider is to manage all the needs and configurations for your application. These platforms manage the configuration of your server behind the scenes. This is how in serverless computing, one can simply focus on the application or code itself being built or deployed.

Machine Learning Model Deployment is not exactly the same as software development. In ML models a constant stream of new data is needed to keep models working well. Models need to adjust in the real world because of various reasons like adding new categories, new levels, and many other reasons. Deploying models is just the beginning, as many times models need to retrain and check their performance. So, using serverless deployment can save time and effort and for retraining models every time, which is cool!

serverless deployment : ML workflow

Fig: ML Workflow

Models are performing worse in production than in development, and the solution needs to be sought in deployment. So, it’s easy to deploy ML models through serverless deployment.

 

Prerequisites to understand serverless deployment

  • Basic understanding of cloud computing
  • Basic understanding of cloud functions
  • Machine Learning

 

Serverless Deployment Models for prediction

We can deploy our ML model in 3 ways:

  • web hosting frameworks like Flask and Django, etc.
  • Serverless compute AWS lambda, Google Cloud Functions, Azure Functions
  • Cloud Platform specific frameworks like AWS Sagemaker, Google AI Platform, Azure Function

Servverless models for prediction

Fig: Types of ML model deployment

 

Serverless deployment architecture overview

cloud function

Fig: A Image is taken from google search and modified

Store models in Google Cloud Storage buckets then write Google Cloud Functions. Using Python for retrieving models from the bucket and by using HTTP JSON requests we can get predicted values for the given inputs with the help of Google Cloud Function.

 

Steps to start serverless model deployment

1. About Data, code, and models

Taking the movie reviews datasets for sentiment analysis, see the solution here in my GitHub repository and data, models also available in the same repository.

 

2. Create a storage bucket

By executing the “ServerlessDeployment.ipynb“ file you will get 3 ML models: Decision Classifier, LinearSVC, and Logistic Regression.

Click on the Browser in Storage option for creating a new bucket as shown in the image:

serverless deployment storage bucket

Fig: click Store option from GCP

3. Create a new function

Create a new bucket, then create a folder and upload the 3 models in that folder by creating 3 subfolders as shown.

Here models are my main folder name and my subfolders are:

  • decision_tree_model
  • linear_svc_model
  • logistic_regression_model

serverless deployment new model

Fig: Folders at Storage

4. Create a function

Then go to Google Cloud Functions and create a function, then select trigger type as HTTP and select language as Python (you can choose any language):

function

Fig: Select  Cloud Function  option from GCP

5. Write cloud function in the editor

Check the cloud function in my repository, here I have imported required libraries for calling models from google cloud bucket and other libraries for HTTP request GET method used to test the URL response and POST method delete default template and paste our code then pickel is used for deserialized our model google.cloud — access our cloud storage function.

If the incoming request is GET we simply return “welcome to classifier”.

If the incoming request is POST access the JSON data in the body request get JSON gives us to instantiate the storage client object and access models from the bucket, here we have 3 — classification models in the bucket.

If the user specifies “Decision Classifier” we access the model from the respective folder respectively with other models.

If the user does not specify any model, the default model is the Logistic Regression model.

The blob variable contains a reference to the model.pkl file for the correct model.

We download the .pkl file on to the local machine where this cloud function is running. Now every invocation might be running on a different VM and we only access /temp folder on the VM that’s why we save our model.pkl file.

We desterilize the model by invoking pkl.load access the prediction instances from the incoming request and call model.predict on the prediction data.

The response that will send back from the serverless function is the original text that is the review that we want to classify and our pred class.

After main.py write requirement.txt with required libraries and versions

write cloud function editor

Fig : Google Cloud Function(find detailed code in my github page)

5. Deploy the model

deploy the model

Fig : Green tick represent successful model deployment

 

6. Test the model

test the model

Fig : Give model name and review(s) for testing

Test function with other model

test function

Fig : Test the model

 

Code References:

My GitHub Repository : https://github.com/Asha-ai/ServerlessDeployment

Become a Full Stack Data Scientist by learning various ML Model deployments and reason behind this much explanation at initial days I struggle a lot for learning ML Model deployment, So I decided my blog should useful to data science freshers end to end

will meet you with my next blog : Deploy ML Model using “Web Hosting Framework –  Flask“

asha
asha

Have 4+ years of experience in data science field, working in telecom domain With a strong foundation in Python, machine learning, deep learning, natural language processing (NLP), and artificial intelligence (AI).

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