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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

Is MLOps Another Redundant Terminology?

Mobarak Inuwa
Mobarak Inuwa Last Updated : 08 Nov, 2022
6 min read

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

Introduction

MLOps? Many persons have barely finished digesting the meaning of DevOps, and here come a new term, MLOps. But, those who understand the meaning of the older term DevOps are on the safe side. So, if you know what’s DevOps and you do not know what MLOps is, but you know the meaning of machine learning and DevOps, then just join the two meanings together, and you are good to go. People who will have issues understanding the term MLOps will be those who do not initially grasp the concept of DevOps. Here in this article, I will walk through the idea of MLOps so that you won’t necessarily need the knowledge of DevOps.

MLOps

Sincerely sometimes, I think the terminologies in tech are becoming too much. Maybe it is because the sphere of tech is artificial, so things easily multiply and get complex compared to the natural world, where even a little mutation must take billions of years. Sometimes I feel maybe if all the terminologies are collected and organized, some will not be necessary as they will be redundant. Tech is a market, so every brand is trying to be unique, causing a swamp of terminologies.

What is MLOps?

MLOps is a short term for Machine Learning Operations. Just see it literally. It is simply the activities (operations) involved in machine learning, except it is carefully designed to meet industry standards more efficiently. It creates a standard method for developing ML models from start to finish to deployment and maintenance. The core reason for MLOps is integration. To not just build models but have a collaboration between ML practitioners and the general tech world.

This union between ML practice and the outside world becomes a very useful approach for creating machine learning and AI solutions. It also now allows data scientists and machine learning engineers to collaborate and improve the way new models are developed and old ones are maintained.

retrain model
Azure ML + Azure DevOps manages your datasets, experiments, models, and ML-infused applications.

The guidelines and best practices presented in MLOps help provide an atmosphere that improves a usual machine learning life cycle systematically. Since ML is done to improve human life, there has to be a way to introduce it into various works of life. This is where ML engineers shake hands with IT and various other tech fields. It is just a merging of DevOps and ML.

We can also see MLOps as a division of labor. Instead of the ML engineer worrying about building models from start to deployment and even maintenance and industry demands, he just focuses on ML. He joins hands with ‘Ops’ or the usual DevOps system.

Why MLOps?

We can see Machine Learning systems in group activities, including data collection, processing, feature engineering, labeling, model design, training and optimization, deployment, and maintenance. Just the last two stages alone are where the model finally leaves the experimental environment and touches the purpose it was started. Instead of allowing the ML engineer to do it alone, MLOps assists from here to provide smoother transition and maintenance. Imagine the ML engineer also bothering on version control? MLOps ease it all and shorten the time of development.

MLOps covers various disciplines accordingly, assisting enterprises to have an efficient workflow and avoiding many challenges that come with development. According to research, only a few ML companies have used it effectively.

MLOps Challenges

Machine learning models are striving in a very dynamic space. Variables take many possibilities and forms. The data coming in is continuous and needs more attention than hard coding operations. Although this, comparably little attention has been given to the practice of MLOps. Most beginners are still yet to start learning the concepts, and experts are mostly still trying to adapt, making it a challenge to strengthen this new standard.

Benefits of MLOps

The chief benefits of MLOps are simply developing more efficient, scalable, and controlled ML systems. The promises of ML become easily realistic in this standardized operation. It has created a pipeline that fosters data teams to save development time while achieving high-performing systems. Continuous testing, for instance, even deployed systems can be scaled to automatically meet new demands. Let’s try to pinpoint some more benefits of MLOps.

Version Control

Version control is a usual activity in software systems. The data and models developed will also require versioning in this area too. Introducing new data to the system after a model is deployed could also be beautifully versioned as it is either used as new data or merged with the history. This is can be done via metadata and other means. For some systems merging the old data may make sense and in some, it may not. In both cases, MLOps will provide a way out.

Working as a Team

Instead of allowing the ML engineer to work separately and the other integration space to work separately, the two can work more organized. This prevents many problems and enhances efficiency.

Data and Model Validation

The scientist now has a bigger team to verify and validate the results of models before they are deployed, when they are deployed, and after they are deployed. Validation doesn’t only figure out the truth. It can also bear new possibilities. Even the data presented to ML engineers can properly be validated by the MLOps.

ML Pipelines

Let us see the machine learning pipeline from this point and how MLOps comes in. MLOps sees ML more from the user end how it can be used best in production.

Data Collection and Problem Scope

This involves the first step of ML. The data has to be carefully recognized, not just as data but also based on the scope or problem domain. Apart from just the usual benefit of this step in ML, this can be further improved to save other issues like avoiding data swamps. Though lots of data is required for ML, we now handle this need carefully and avoid other challenges.

Data Verification and Validation

In verification, we check if the data is complete, in a good format, organized, clean, etc.; in validation, we still confirm if it meets the needs of the outside world. These are distinctly important to avoid waste of time.

Feature Specification and Extraction

Instead of just consuming, we can select the features that best fit the goals. Not everything may be needed for the final prediction, clustering, or optimization. Here you may discover the need to clean the data further.

Internal Configuration

A robust system, which is the goal of MLOps, should have efficient communication between its subsystems and external systems.

Coding

The codes should also follow best practices. Instead of just coding as a natural ML model, we consider other arts that will also support the goal MLOps. The coding way should facilitate good documentation and automatic testing and integration.

Project Management

here we follow a process of ensuring the ML pipeline achieves all project goals within the given constraints. This is done through project documentation and all other report writings created at the beginning of the development process and throughout the project lifecycle.

Monitoring

Lastly is the monitoring phase. The model is attached with a subsystem for monitoring to ensure enough platform is set to oversee the deployed model and the domain it serves. Below is an instance of a dataset monitor called Shapash.

Shapash Monitor

Conclusion

We have been able to see the MLOps concepts from a clear perspective. With all the benefits presented above, do you think MLOps is just another redundant tech terminology? Though it’s important may not quickly enter your mind at first thought, but by now, you should have been able to see that MLOps has come to stay and will very likely become more advanced than the initial idea of DevOps. There is a statement that ModelOps is the main category while MLOps is a subset. MLOps is focused on the operationalization of ML models, while ModelOps covers the operationalization of all types of AI models. AIOps is also another so-called ‘Ops’ case of systems in Operations. So many Ops have arisen recently, and many more will still come up.

Key takeaways:

  • MLOps is a short term for Machine Learning Operations. Just see it literally. It is simply the activities (operations) involved in machine learning, except it is carefully designed to meet industry standards more efficiently.
  • MLOps covers various disciplines accordingly, assisting enterprises to have an efficient workflow and avoiding many challenges that come with development. According to research, only a few ML companies have used it effectively.
  • Machine learning models are striving in a very dynamic space. Variables take many possibilities and forms. The data is continuous and needs more attention than hard coding operations.
  • The chief benefits of MLOps are simply developing more efficient, scalable, and controlled ML systems.

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

Mobarak Inuwa
Mobarak Inuwa

I am an AI Engineer with a deep passion for research, and solving complex problems. I provide AI solutions leveraging Large Language Models (LLMs), GenAI, Transformer Models, and Stable Diffusion.

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