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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

How To Containerize Your Data Science Workflow With Docker System

s
saikat Last Updated : 04 Jan, 2022
6 min read

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

In my case, I was first introduced to Docker when I have to containerize a Machine Learning workflow in my organization. At that time I realized that Docker makes Machine Learning Engineers’ lives much easier. Not only that we can deploy and run applications using Docker, but also it ensures that our applications work exactly in the same environment in other systems also. This is important for any Data Science and Machine Learning application that the program must ensure reproducible results, Docker provides the solution by sharing the environment, libraries and drivers. So, let’s dive into it.

List of contents 

  1. Introduction to Docker
  2. Docker architecture elements
  3. Docker Installation
  4. Docker CLI used to run image
  5. Create a docker system image for a sample ML project
  6. Run Docker System image inside a container

Introduction to Docker

Earlier we are used to using Virtual Machines to run different apps in a system. A virtual machine runs a separate guest operating system with access to host resources through a Hypervisor. Each VM runs a separate guest OS (like Linux or Windows) irrespective of the host OS. The main drawbacks of running the VMs are – each guest OS has its separate running kernel which used many resources for its libraries and dependencies, VM usually takes usually long time to start its own guest OS, and deployment can be a little lengthy for VMs.

Docker System Architecture

Image Source: https://cloudacademy.com

So, what does Docker offer us to overcome the drawbacks?

Docker is a virtualization technology that helps to easily deploy and manage applications by using containers. A container usually shares the kernel of the host machine with other containers. Containers are lightweight, isolated from each other, and bundle their own software, libraries, and dependencies together. Containers are lightweight because unlike VMs they don’t run individual Guest OS, which also makes them quick to start. To create, execute and manage the containers we used Docker as a management application.

Docker Architectural Elements

Docker Engine – Docker engine is used for containerization of applications. This is a client-server application with a long-running daemon process, API interface to communicate with daemon and CLI clients.

Docker daemon – Docker uses a client-server architecture where clients send responses to the docker daemon. Docker daemon is a continuous runtime process that manages the containers. Docker daemon receives the API requests and manages different objects such as containers and images.

Images – Docker image is a file that contains different application codes, libraries, and dependencies needed to make an application run. Image file of docker is a read-only template with instructions that can create a Docker container. To build our own customized image file, we need to create a dockerfile by defining the steps needed to create the image and execute it.

Docker Registry – After the Docker image has been created, we can share it in some open platform or some private registry so that our application can be shared with others or can be restricted based on the requirement. We can also push the image somewhere like DockerHub, AWS ECR, etc so that it can be used further.

Containers – Container is the runnable instance of an image and we can create, start or stop a container using Docker. The containers are –

  • Lightweight – Containers share the system kernel, so they don’t need any separate OS to run.
  • Secure – All containers are isolated so applications running inside them are safe.
  • Standard and Portable – Containers are portable between registry servers and container hosts.

Docker Installation

To install docker one can use the following links and proceed with the procedure described here.

For Windows User Click Here

For Mac User Click Here

Docker CLI and Use

After installation of Docker, one can be sure of installation from the command prompt. The output of the command “docker” will be something like this –

Docker system CLI and Use

If the docker is ready, we can download any of the pre-uploaded images from DockerHub, which is an open docker image repository. Let’s try with a simple image, log in to Docker hub and search for the image, hello-world (look for an official image to avoid any problem). Now, pull this image using the command in the CLI

docker pull hello-world

It will pull the latest image to your system

pulling the image using the command in the CLI

If you want to check the docker images in your system, run the below command in the CLI

docker image ls -a
checking the docker images in your system

Now, let’s try to run one of the docker images. We have already downloaded the “hello-world” image from the docker hub, to run it, we have to run the command  –

 docker run hello-world

This gives the output

output

Create a Docker image for a sample project

Now let’s have an example of containerizing a very simple machine learning code. For a starter, I have created an elementary Machine Learning Model for predicting salary using Linear Regression Model (The code is available here in my personal git repository). Before containerizing your code you need to create a Dockerfile. Dockerfile is a text file that contains all the necessary commands a user needs to give to create a docker image. A user can run the docker build command to execute a successive number of commands necessary to create an image, docker build command runs the command from Dockerfile and creates the image then after. For example, I have run the following command to execute a docker build 

docker build -t new_image .

The output of the same is down below for your reference: 

Create a Docker image for a sample project

You can check the docker image from CLI also by using the command –

docker image ls
checking the docker image from CLI also by using the command

In the above output, we can see that we have successfully created a docker image for the project. 

Run the Docker image inside a container

Now, we have the docker image, so how we can run this image in a container? The container has its own file system, networking, and isolated processes. To run a docker image inside a container we have to use the below command –

docker run -d -p 8000:8000 new_image

Similarly, to stop a container from running we can use the command 

docker stop

Since we run the containers we also need to confirm if the container is running or how many containers are started or exited and we use the below command (docker ps -a) to confirm the same.

 

checking if the container is running

We can see the two images which were running on two different containers and their container ids are also listed in the first column of the output. 

Conclusion

Docker has many benefits. One of the most common reasons to use docker is after we test our application in the containerized environment we can deploy it in any other system where docker is running. The programmer can be assured that a containerized application will not fail in any other system running the docker system. 

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

I am a Machine Learning Engineer with a keen interest in data and technology. I love to learn new things and also try to write down the same from my experience. I have 3yrs + of experience in data analysis and predictive analytics and I also have an interest in cloud technologies, automation, and security analysis. Other than these, I love to travel, take pictures, and write short blogs

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A Complete Guide for Deploying ML Models in Docker
A Complete Guide for Deploying ML Models in Docker
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A Complete Guide on Docker for Beginners

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