Introduction
With the global MLOps market expected to rise to 5.9 billion dollars until 2027; It emerges as a highly coveted career choice for professionals like you. This article delves into the reasons why adopting MLOps is a career-defining decision. Additionally, it presents the MLOps learning path for 2024 – a meticulous step-by-step guide designed to transform you from an absolute beginner to a competent MLOps professional. Whether you want to enter the field or improve your existing skills, this roadmap is your complete guide, ensuring you are well equipped for the journey ahead.
MLOps Learning Path 2024 – Overview
Before we delve into the roadmap, let's discuss the prerequisites. It is essential to have a solid knowledge of a programming language, preferably Python, and a good understanding of data analysis. This includes cleaning, discussing, and exploring exploratory analysis of learning data with Python libraries such as Pandas, Numpy, and Matplotlib.
Quarter 1: Development and implementation of offline models
The goal of the first quarter is to learn how to develop and deploy machine learning models at the offline level. These are the key areas to focus on:
- Fundamental knowledge for MLOps: Start by reviewing 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 to track experiments.
- Model packaging and model service: Understand the concept of model packaging or serialization and learn Python libraries like Pickle or Joblib for easy implementation. Also, focus on building simple web applications with Flask to offer predictions via API.
Projects for the 1st quarter
ICA prediction: Create a model to predict Air Quality Index (AQI) and deploy it as a Flask API or a Streamlit/Gradio app. This project will help you create a strong portfolio and showcase your skills.
Quarter 2: Implementation of online models and cloud platforms
In the second quarter, the objective is to implement models online or in the cloud. These 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 features of your chosen platform, including setting up a cloud environment, running Jupyter Notebooks, and optimizing storage, security, and machine learning platforms.
- Stevedore: Understand the concept of Docker, a platform for developing, shipping and running applications. Learn how to package your machine learning models with 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 and logging: Implement monitoring and logging systems using tools such as 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 the second quarter
Develop and implement the first quarter projects, but this time in the cloud. Train your models using a cloud-based machine learning platform and deploy them to the cloud platform of your choice using CI/CD pipelines.
Quarter 3: Implementation of MLOps for NLP or CV
In the final quarter, the goal is to implement MLOps in natural language processing (NLP) or computer vision (CV), depending on your business needs or personal interests. These are the key areas to focus on:
MLOps for NLP
- Data Management and Preprocessing: Learn text preprocessing techniques such as tokenization, stemming, stemming, and entity recognition. Explore data augmentation techniques such as back translation, synonym replacement, and paraphrasing to address NLP data scarcity.
- Training and implementation of the model: Get familiar with NLP-specific frameworks like spaCy, Hugging Face Transformers, and TensorFlow Text. Explore various deployment options, such as APIs, microservices, and containerization, to deliver NLP models in real-world scenarios.
- Monitoring and evaluation: Focus on NLP-specific metrics like BLEU score, ROUGE, and F1 score to evaluate NLP models.
MLOps for CV
- Data Management and Preprocessing: Learn image augmentation techniques such as geometric transformations, color space augmentation, and advanced techniques such as cropping and blending images. Understand domain adaptation and transfer learning to adapt models trained in one domain to another.
- Training and implementation of the model: Optimize costs by using GPUs and TPUs for efficient training of large computer vision models. Leverage cloud cost management tools and explore techniques such as model pruning and cost-aware scheduling. Understand task-specific metrics like IoU, mAP, and F1-score to evaluate computer vision models.
Projects for the third quarter
Choose Real-Time Sentiment Analysis for Social Media Posts (NLP) or Medical Image Anomaly Detection for Diagnostics (CV) as your project. Create an MLOps channel that analyzes social media posts or medical images to aid in decision making.
Conclusion
Congratulations! He has completed the 9-month MLOps learning path and is now a competent MLOps professional. Remember to create a strong portfolio and showcase your projects on your resume and LinkedIn. Enjoy the Analysis Vidhya sharesPlatform for more learning opportunities and access to live webinars and AMA sessions from industry experts.
You can accelerate your MLOps journey with our ai/ML Blackbelt Plus program with 500+ projects, 1:1 mentoring, and dedicated interview preparation with placement support. Let us accelerate your MLOps journey with the BlackBelt Plus Program!
Happy learning and good luck on your MLOps journey!
