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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

A Comprehensive MLOps Learning Path: 2025 Edition

Nitika Sharma
Nitika Sharma Last Updated : 10 Dec, 2024
4 min read

Introduction

With the global MLOps market projected to surge to USD 5.9 billion by 2027; it emerges as a highly coveted career choice for professionals like you. This article delves into the reasons why embracing MLOps is a career-defining decision. Moreover, it unveils the MLOps Learning Path for 2025—a meticulous, step-by-step guide tailored to transform you from an absolute beginner into a proficient MLOps professional. Whether you’re aiming to step into the field or elevate your existing skills, this roadmap is your comprehensive guide, ensuring you’re well-equipped for the journey ahead.

MLOps Roadmap

Table of contents

MLOps Learning Path 2025: Overview

Before we dive into the roadmap, let’s discuss the prerequisites. It is essential to have a solid grasp of a programming language, preferably Python, and a good understanding of data analysis. This includes learning data cleaning, wrangling, and exploratory data analysis with Python libraries such as Pandas, Numpy, and Matplotlib.

Quarter 1: Offline Model Development and Deployment

The goal of Quarter 1 is to learn how to develop and deploy machine learning models at an offline level. Here are the key areas to focus on:

  • Foundational Knowledge for MLOps: Start by revising the essential machine learning skills, including basic algorithms, evaluation metrics, and model selection techniques.
  • Version Control and Model Versioning: Learn the power of version control using Git and understand the importance of model versioning. Explore tools like MLflow, DVC, or Neptune for tracking experiments.
  • Model Packaging and Model Serving: Understand the concept of model packaging or serialization and learn Python libraries like Pickle or Joblib for easy deployment. Additionally, focus on building simple web apps with Flask to serve predictions through APIs.

Projects for Quarter 1

AQI Prediction: Build a model to predict the Air Quality Index (AQI) and deploy it as a Flask API or a Streamlit/Gradio App. This project will help you build a solid portfolio and showcase your skills.

Quarter 2: Online Model Deployment and Cloud Platforms

In Quarter 2, the goal is to deploy models at an online level or in the cloud. Here are the key areas to focus on:

  • Cloud Platform Basics: Choose a major cloud platform like AWS, GCP, or Azure, or a freemium platform like Heroku. Learn the basic functions of the chosen platform, including setting up a cloud environment, running Jupyter Notebooks, and optimizing for storage, security, and ML platforms.
  • Docker: Understand the concept of Docker, a platform for developing, shipping, and running applications. Learn how to package your ML models using Docker and deploy them to cloud platforms using services like Kubernetes or out-of-the-box solutions like Amazon Elastic Container Service (ECS), Azure Kubernetes Service (AKS), or Google Kubernetes Engine (GKE).
  • Cloud Monitoring & Logging: Implement monitoring and logging systems using tools like CloudWatch (AWS), Azure Monitor, or Stackdriver (GCP). This will help you manage your cloud infrastructure and applications effectively.
  • Continuous Integration and Continuous Deployment (CI/CD) for ML: Learn how to implement CI/CD in machine learning to automate code changes and deployments. Explore tools like Travis CI or Jenkins for seamless integration and deployment.

Projects for Quarter 2

Develop and deploy the projects from Quarter 1, but this time on the cloud. Train your models using a cloud-based ML platform and deploy them to your chosen cloud platform using CI/CD pipelines.

Quarter 3: MLOps Implementation for NLP or CV

In the final quarter, the goal is to implement MLOps in either Natural Language Processing (NLP) or Computer Vision (CV), depending on your business needs or personal interest. Here are the key areas to focus on:

MLOps for NLP

  • Data Management and Preprocessing: Learn text preprocessing techniques like tokenization, stemming, lemmatization, and entity recognition. Explore data augmentation techniques like back-translation, synonym replacement, and paraphrasing to address NLP’s data scarcity.
  • Model Training and Deployment: Familiarize yourself with NLP-specific frameworks like spaCy, Hugging Face Transformers, and TensorFlow Text. Explore various deployment options like APIs, microservices, and containerization for serving NLP models in real-world scenarios.
  • Monitoring and Evaluation: Focus on NLP-specific metrics like BLEU score, ROUGE, and F1-score for evaluating NLP models.

MLOps for CV

  • Data Management and Preprocessing: Learn image augmentation techniques like geometric transformations, color space augmentation, and advanced techniques like cutout and mixing images. Understand domain adaptation and transfer learning for adapting models trained on one domain to another.
  • Model Training and Deployment: Optimize cost by utilizing GPUs and TPUs for efficient training of large computer vision models. Leverage cloud cost management tools and explore techniques like model pruning and cost-aware scheduling. Understand task-specific metrics like IoU, mAP, and F1-score for evaluating computer vision models.

Projects for Quarter 3

Choose either Real-time Sentiment Analysis for Social Media Posts (NLP) or Medical Image Anomaly Detection for Diagnostics (CV) as your project. Build an MLOps pipeline that analyzes social media posts or medical images to assist in decision-making.

mlops roadmap

Conclusion

Congratulations! You have completed the 9-month MLOps Learning Path and are now a proficient MLOps professional. Remember to build a solid portfolio and showcase your projects on your resume and LinkedIn. Join the Analytics Vidhya community platform for further learning opportunities and access to live webinars and AMA sessions from industry experts.

You can speed up your MLOps journey with our AI/ML Blackbelt Plus program with 500+ projects, 1:1 mentorship, and dedicated interview preparation with placement support. Let us expedite your MLOps journey with the BlackBelt Plus Program!

Happy learning and best of luck in your MLOps journey!

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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