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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

Shipping your Machine Learning Models With Dockers

Nibedita Dutta
Nibedita Dutta Last Updated : 27 Aug, 2021
4 min read

This article was published as a part of the Data Science Blogathon

Introduction

Docker is a platform that deals with building, running, managing, and distributing applications by using containers. Don’t worry if you are hearing the term ‘container’ in this context for the first time. You could imagine it as a software unit that contains the application code, required libraries, and other dependencies needed for the application that you want to build.

When do we need Dockers?

Let us suppose that we have two different Python applications that we want to host on a single server (either a physical machine or a virtual machine). Let us further assume that these two Python applications need different versions of Python and different libraries and dependencies. Since it is not possible to have different versions of Python installed on the same machine, we would either need two different physical machines or a single physical machine that is powerful enough to host two virtual machines. Both the solutions would incur quite a huge cost.

In order to avoid this cost and have an efficient solution at the same time, we could use a docker. The machine on which you wish to run and install the docker is known as the ‘Host’.

Dockers implementation

https://www.aquasec.com/cloud-native-academy/docker-container/

To deploy the applications on this host, we can create Docker containers that would contain everything needed to run the applications. These containers would not have their own operating system. The kernel of the host’s operating system would rather be shared across the multiple containers that we create. This makes these containers isolated from each other.

Some Docker Terminologies

Docker Engine

Docker Engine is an open-source containerization technology for building and containerizing applications. It works as a client-based application and has a Server, REST API and Client as its core components.

Docker Daemon

The Server runs a Docker Daemon which is nothing but a process that enables managing docker images, containers, networks, and storage volumes. Users can interact with it using the Docker Client.

Docker Engine Rest API

An API used by the applications to interact with the Docker Daemon, that can be accessed through an HTTP client.

Dockers engine

https://www.aquasec.com/cloud-native-academy/docker-container/docker-architecture/

Docker CLI

Users can interact with the Docker Daemon using Docker commands through this command-line interface client.

Docker Image

A docker image is a template that consists of the application and all the other dependencies needed for running the application on Docker.

Docker Container

A running instance of the docker image is known as a docker container.

Docker Hub

Docker hub is an online repository wherein ready to use docker images are available.

Installing Dockers

Docker Desktop can be installed for your Mac or Windows environment from the following link –

https://docs.docker.com/desktop/

Dockers CLI Commands

docker create

This command creates a new container over the specified image.

$ docker create ubuntu

The above command enables creating a container using the latest Ubuntu image.

docker start

This command is used to start any stopped container. The command below enables starting the container whose container ID starts with 70257.

$ docker start 70257

docker stop

This command is used to stop any running container. The command below enables stopping the container whose container ID starts with 70257.

$ docker stop 70257

docker run

This command is used to create a container and then start it.

$ docker run ubuntu

The above command enables creating a container from the latest Ubuntu image and then start running it. If we want to interact with the created container, we could specify ‘-it’ before the docker run command:

$ docker run -it ubuntu

This provides us with the terminal to interact with the created Ubuntu container.

root@e4e633428474:/#

docker rm

This command is used to delete a container.

$ docker rm 70257

The above command enables deleting the container whose container ID starts with 70257.

docker ps

This command enables viewing all the containers running on the docker host.

$ docker ps

In order to view all the containers irrespective of their running status, we can include ‘-a’ to the above command.

$ docker ps -a

docker images

This command enables viewing all the Docker images present on the Docker Host.

$ docker images

Build a docker container with your ML model

Step 1: Create a Dockerfile

Creating a Docker container begins by creating a Dockerfile. A Dockerfile simply put is a text file that has all the commands needed to create the container.

A Docker image has a base image on top of which layers are added with each of the layers adding some dependencies.

FROM continuumio/anaconda3:4.4.0
COPY . /usr/app/
EXPOSE 5000
WORKDIR /usr/app/
RUN pip install -r requirements.txt
CMD python flask_api.py

In the code above, we started with a base image of Anaconda. The COPY command is used to copy all the files from the current directory to the specified directory. The EXPOSE command tells Docker to get all its needed information from the specified port during runtime. The WORKDIR command defines the working directory of the container at any time. The next command is for running the requirements.txt file which installs all the required python dependencies. The last command is used to run the flask_api.py file. This file can contain a trained ML model which can be used to predict new data. This file would serve all API requests and hence would contain a function that can be called by an API endpoint.

Step 2: Build an image with the Dockerfile

In order to build a docker container from the created Dockerfile, we need to run the docker build command that builds the image as per the instructions given in the Dockerfile.

$ docker build -t IMAGE_NAME:TAG .

In the above code, the image name is given by the user along with a tag. Tagging an image is equivalent to giving an image an alias which helps in distinguishing different versions of the image.

We can test the container we created on our local machine at this point by running the following command:

$ docker run IMAGE_NAME:TAG

About Author

Nibedita Dutta

Nibedita completed her master’s in Chemical Engineering from IIT Kharagpur in 2014 and is currently working as a Senior Consultant at AbsolutData Analytics. In her current capacity, she works on building AI/ML-based solutions for clients from an array of industries.

The media shown in this article are not owned by Analytics Vidhya and are used at the Author’s discretion.
Nibedita Dutta
Nibedita Dutta

Nibedita completed her master’s in Chemical Engineering from IIT Kharagpur in 2014 and is currently working as a Senior Data Scientist. In her current capacity, she works on building intelligent ML-based solutions to improve business processes.

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