A Comprehensive MLOps Learning Path: 2024 Edition

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 2024—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 Learning Path 2024: 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.

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.

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Happy learning and best of luck in your MLOps journey!