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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

MLOps Part 1: Revealing the Approach behind MLOps

Illiyas
Illiyas Last Updated : 02 Feb, 2022
6 min read

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

Table of contents

  1. Overview
  2. Traditional Software development Life Cycle
  3. Waterfall Model
  4. Agile Model
  5. DevOps
  6. Challenges in ML models
  7. Understanding MLOps
    • Data Engineering
    • Machine Learning
    • DevOps
  8. Endnotes

Overview: MLOps

According to research by deeplearning.ai, only 2% of the companies using Machine Learning, Deep learning have successfully deployed a model in production. The remaining 98% of the companies either drop their implementation of ML in their firms or rely on other companies for production. Deploying an ML model in production is not the same as deploying other software applications. It needs a specific life cycle and this cycle is called MLOps. A generic MLOps workflow can be followed to build, deploy, monitor ML applications.

Let us have a detailed discussion on the fundamentals of MLOps.

Traditional Software Development Life Cycle

Software Development Life Cycle is a technique used by the software industries to plan, design, develop, and test high-quality software. The main motive of this life cycle is that the software should meet customers’ expectations that should be completed within time and within budget.

Traditional Software Development Life Cycle started with the waterfall model and after many years transformed into an agile model and DevOps. For understanding MLOps, we must be clear with the other Non-ML software development life cycles. Some of them are listed below.

Waterfall Model

This was the oldest model. It is a non-iterative method. Every stage of the model is pre-organized and executed one after another. It all starts from requirement gathering then to design, development, and testing. We can’t move into another phase without completing the ongoing phase. It is highly used for small projects where requirements are clearly defined. It is not suitable for dynamic and large projects where requirements change over time as per user demand. So we moved into the agile methodology.

MLOps

Image Source

Agile Model

This method made a breakthrough in the IT industry. Most of the organizations followed this approach. It is an iterative and procedural way of development. The solution to the problem can be segregated into different modules and they are delivered periodically. In each iteration, a working product is made and further changes are added to it in subsequent iterations. This model also involves the interaction with customers in the development and testing process to test and give feedback on the product. We can get some suggestions from the customers directly and improvements are done throughout the project development process. This agile methodology can be followed for projects where requirements are changing from time to time.

Agile Methodology | MLOps

Image Source

DevOps

The DevOps method extends agile development practices. It is the method of executing their software applications driven by continuous integration, continuous deployment, and continuous delivery. It involves collaboration, integration, and automation among software developers and IT operators to improve efficiency, speed, and quality. With the help of DevOps, it is easy to ship software to production and to keep it running reliably.

Why can’t we follow the same method for ML applications?

Challenges in ML Models

The main cause is that there is a fundamental difference between ML development and traditional software development.

Machine Learning is not just code. It is code plus data
Machine learning = Code+Data

A Machine learning model is created by applying an algorithm(via code) to fit the data to result in an ML model.

In traditional development, the version of source code produces the version of the software. In machine learning, a version of source code and the version of the data produces the version of the ML model.

Code is crafted in the development environment. Data comes from multiple sources for training and testing. The data may change over time in terms of volume, velocity, veracity, and variety. There is some additional dimension that differentiates traditional software and ML applications. Sometimes Big data, brings entirely new challenges to the development process. For this evolving data, code needs to evolve over time.

Volume: It denotes the Size of the data
Velocity: Refers to the speed at which the data enter the system
Veracity: It says how accurate the data is
Variety: Refers to different formats of data.

So data and code with the progression of time, end up going in two directions with one objective of building and maintaining a scalable ML system.

Data | Code | MLOps

Data and code progress over time( Author’s own image)

This disconnect causes several challenges that need to be solved to put an ML model in production.

To overcome these challenges, MLOps approaches bridging data and code together over the progression of time.

Data | Code | MLOps

MLOps – Binding data and code together(Image source- Author)

Let us now understanding the MLOp. 

Machine Learning Operations in MLOps

MLOps is a method to fuse ML with software development by integrating multiple domains ML, DevOps and data engineering which aims to build, deploy, and maintain ML systems in production reliably and efficiently. It brings together data engineering, ML, DevOps in a streamlined manner.

Machine learning operations

MLOps Intersection

Image Source

How does data engineering come into the picture in MLOps?

As the IT industry and internet became so wide, the data generated per day keeps on increasing. To tackle this problem of data generation and storage, companies and tech giants started using Big Data. Our data is being stored somewhere in the world in databases, data warehouses, data lakes, etc. Storing and retrieving data from these sources is not an easy job.

For building a machine learning model, we primarily require data. Extracting specific data from these large data sources can’t be done with a single line of code. So we need data engineering which involves extracting, transforming, loading(ETL), and analyzing the data at a large scale.

So, automating the data incoming pipeline for training the data is the foremost step in MLOps.

Machine Learning

After we obtain the data from various sources, we need to build the ML models. Once the problem statement and business use cases are well understood, we try to organize and preprocess the data. We try different combinations of algorithms and tune their hyperparameters. This will produce certain models that are trained and fit the data. We also test our trained model with certain evaluation metrics to monitor the performance. This data pre-processing, model building, testing altogether form the machine learning pipeline.

DevOps

DevOps is bringing the development, testing, and operational aspects of software development. Its primary aim is to automate the process, continuous integration of code, and continuous delivery of the product to production.

These 3 domains of Data engineering, Machine Learning pipeline, DevOps altogether combine to form the MLOps.

So with the help of MLOps, data scientists/Machine learning Engineers can extract the data, train the models, run experiments, validate the models, and finally deploy the scalable applications to production smoothly.

How MLOps is done in Organization?

There are so many cloud service providers started delivering microservices for ML operations. Some of the most used solutions of popular cloud platforms are listed below.

Amazon Sagemaker – Helps in the ML pipeline (i.e) build, train, deploy and monitor machine learning models.

To know more about Sagemaker, read out my blog Introduction to Exciting AutoML services of AWS

Microsoft Azure MLOps Suite:

Microsoft Machine Learning – Build, train, evaluate models

Azure Pipeline – Automate ML

Azure Monitor – Tracking and monitoring

Azure Kubernetes Service – Managing containers

Google Cloud MLOps:

Dataflow – Used for ETL

BigQuery – Cloud data warehouse, which is used as the source for ML training

TFX – to deploy models

To know about building ML pipeline in GCP, reach out to this comprehensive guide,

Google Cloud Platform with ML Pipeline: A Step-to-Step Guide

Based on the type of use case, organizations choose the best suitable tools and microservices for their ML operations. The budget is also the main factor in determining the tool.

EndNotes

In this article, we have explored the domains behind MLOps and seen how organizations perform MLOps.

If you liked this article and want to know more, go visit my other articles on Data Science and Machine Learning by clicking on the Link

I hope you enjoyed the article and increased your knowledge. Please feel free to contact me at [email protected] Linkedin

Image Sources: The sources of the images are given below the respective image. Some images are created by the author for illustration.

Read more articles on Blog.

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

agileblogathonmachine learningMLOps
Illiyas
Illiyas

I am a Machine Learning professional with a strong background in Natural Language Processing (NLP). I am passionate about predictive modeling, data analysis, and deep learning, as they provide unique opportunities to uncover valuable insights from complex datasets.

Recently, my focus has been on Language Models (LLMs), an exciting area within NLP. I have been actively involved in researching, developing, and refining LLMs to enhance their capabilities and applicability in real-world scenarios. Through my work, I strive to advance the field of NLP and contribute to the development of intelligent systems that can understand and generate human-like language.

Sharing knowledge and collaborating with others is an essential part of my professional journey. I find great joy in exchanging ideas, insights, and expertise with fellow professionals and enthusiasts. By sharing my knowledge, I aim to contribute to the growth of the Machine Learning and NLP community, fostering an environment of continuous learning and innovation.

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MLOps : Machine Learning Operations in Microsoft Azure
MLOps : Machine Learning Operations in Microsoft Azure
Streamlining Machine Learning Workflows with MLOps
Streamlining Machine Learning Workflows with MLOps

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