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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

Data Science Pipelines And Workflow

S
Sharvari Last Updated : 02 May, 2021
6 min read

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

Source: Myriam Jessier on Unsplash

Pipelines for data science and workflow include many complex, varied, and similar steps. Let’s take the workflow of developing a typical machine model as an example. We start with data preparation and then move on to model training. Finally, we use our model (or application) in the production area. Each of these steps contains a few subtasks.

If we use AWS, our raw data may already be available on Amazon Simple Storage Service (Amazon S3) and stored as CSV, Apache Parquet, or its equivalent. We can start instant training models using Amazon AI or automated automation services (AutoML) to establish basic functionality by directly pointing to our database and by clicking one “train” button.

In customized machine learning models – the main focus of this article is that we can begin the stages of importing and evaluating personal data, including data analysis, data quality checks, summaries, missing values, quantile statistics, data analysis, integration analysis, etc.

After that, we have to define the type of machine learning problem regression, classification, clustering, etc. Once we have identified the type of problem, we can select the machine learning algorithm that is best suited to solve the given problem. Depending on the algorithm we choose, we need to select our data set to train, validate, and test our model. Our raw data often needs to be converted into mathematical vectors to enable numerical optimization and model training. For example, we may decide to convert categorical columns into binary encoded vectors or convert text-based columns into word-embedding vectors. After we have converted a subset of raw data into features, we must split the features into train, validation, and test feature sets for model training, adjustment, and testing.

In the model training phase, we select an algorithm and then train our model with our training element set to ensure that our model code and algorithm are ready to solve the given problem.
In the model tuning phase, we adjust the hyper-parameter algorithm and evaluate the performance of the model in comparison with the validation feature set. We repeat these steps – add more data or change hyperparameters as needed – until the model meets the expected results in the test feature set. These results should be in line with the purpose of our business before pressing the model into production.

The final phase from continuation to production often poses a great challenge to data scientists and machine learning experts.
Once we have built all the individual steps in the flow of our machine learning activity, we can begin to change the steps into a single machine learning duplication. When new data arrives at S3, our pipeline restarts with the latest data and presses the latest production model to run our applications. There are many workflow orchestration tools and AWS services available to help us build automatic machine learning pipelines.

Amazon SageMaker Pipelines

Amazon SageMaker Pipelines and workflow data science
Source: AWS Towards Data Science

Amazon SageMaker Pipelines is the most common, and most complete way to use AI pipelines and machine learning pipelines in Amazon SageMaker. Amazon SageMaker Pipelines is the first organization designed for the purpose, ease of use, and continuous delivery (CI / CD) of machine learning (ML). With SageMaker’s pipes, you can create, use, and manage the end-to-end performance workflows.

Scheduling the workflow for each step of the machine learning process (e.g. Testing and editing data, experimenting with different algorithms and parameters, training and configuration models, and submitting models to production) can take months to encode.

 

AWS Step Functions Data Science SDK

 

AWS Step Functions Data Science SDK workflow and pipeline
Source: Wikimedia Commons

Step Functions, a service run by AWS, is a great way to build complex workflows without building and maintaining our infrastructure. The AWS Step Functions Data Science Software Development Kit (SDK) is an open-source library that allows you to easily create data processing and training and publish machine learning models using Amazon SageMaker and AWS Step Functions. You can create a flow of machine learning functionality in Python that scales infrastructure, without providing and integrating AWS services separately.

 

Kubeflow pipelines

Kubeflow pipelines
Source: VentureBeat

Kubeflow is a relatively new ecological system developed for Kubernetes which includes an orchestration system called Kubeflow Pipelines. With Kubeflow, we can restart failed pipelines, adjust pipeline performance, analyze training metrics, and track pipeline lineage.

Apache Airflow performance management in AWS
Apache Airflow is a highly mature and popular option designed specifically for data engineering pipelines and load-shifting (ETL) pipelines. We can use airflow to record workflow as a guide to acyclic graphs of activities. The Airflow Editor performs its functions for a large number of employees while following a specific dependency. We can see through pipelines in production, monitor progress, and troubleshooting problems when needed with the Airflow user interface.

MLflow

 

MLflow data science workflow and pipelines
Source: devclass.com

MLflow is an open-source project originally focused on test tracks but now supports pipelines called MLflow Workflows. We can use MLflow to track tests with Kubeflow and Apache Airflow workflows as well. MLflow requires us to build and maintain our Amazon EC2 or Amazon EKS collections. MLflow is designed to work with any ML library, algorithm, download tool, or language.

It is built on REST APIs and simple data formats (e.g., the model can be viewed as a lambda function) that can be used from a variety of tools, instead of providing only a small set of built-in functionality. This also makes it easy to add MLflow to your existing ML code for the immediate benefit and share the code using any ML library others may use in your organization.

 

TensorFlow Extended

 

TensorFlow Extended
Source: Tensorflow.org

TensorFlow Extended (TFX) is a collection of open-source Python libraries used within a pipeline orchestrator such as AWS Step Functions, Beef Flow Pipelines, Apache Airflow, or MLflow. TFX is specified in TensorFlow and relies on another open-source project, Apache Beam, to measure more than one processing process.

 

Human in the loop Workflows

 

Human in the loop Workflows data science workflow and pipelines
Source: Nordicapis

While AI and machine learning services make our lives easier, people are far from timeless. In fact, the concept of “human-in-the-loop” has emerged as a key cornerstone in most AI / ML travels. People provide the essential quality assurance of sensitive and controlled models in production.

Amazon Augmented AI (Amazon A2I) is a fully managed service to improve the flow of human interiors that include a clean user interface, component-based access control via AWS Identity and Access Management (IAM), and low-cost data storage via S3. Amazon A2I is integrated with many Amazon services including Amazon Rekognition content rating and Amazon Textract data output form. We can also use Amazon A2I with Amazon SageMaker and any of our ML models.
AWS Pipeline and Amazon SageMaker support a complete MLOps strategy, including automated pipeline re-training that can result in both GitOps decryption and statistical calculations such as data explosion, model selection, and definition variation.

The media shown in this article on Data Science Pipelines and Workflow are not owned by Analytics Vidhya and is used at the Author’s discretion. 

S
Sharvari

I am Sharvari Raut. I love to write. I am a final year student in Computer Science and Engineering from NCER Pune. I have worked as a freelance technical writer for few startups and companies. Having 2 yrs of experience in Technical Writing I have written over 100+ technical articles which are published till now. Writing for Analytics Vidhya is one of my favourite things to do.

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