#1 [MUST] Basic 3-Stage Pipeline for MLOPS : Theory

MLOps

Introduction

If you’re creating machine learning models, you’ll find that they frequently get to the development stage but halt there. Using a static machine learning model might quickly make it outdated in the quickly evolving data ecosystem of today. It’s critical to scale and implement ML models into production to guarantee their applicability and endurance. MLOps, a fusion of “Machine Learning” and “Operations,” enters the picture here.

MLOps bridges the gap between development and production by integrating ML systems with DevOps methodologies. It guarantees prompt model deployment, efficient model monitoring, and long-term model maintenance.

3-Stage Pipeline

The fundamental three stages of the MLOps pipeline — the Data Pipeline, Model Pipeline, and Model Deployment Pipeline — will be discussed in this article. You’ll comprehend the fundamentals, industry best practices, and procedures by the conclusion. To facilitate debugging, make sure every step is in a different file. This article will be followed by a hands-on project, which implements the 3-stage pipeline for better understanding.

Stage 1: Data Pipeline

Understand this stage as the supplier of your data. You create automated code at this point to retrieve, process, and save data from a source. It forms the basis of the 3-stage MLOps process.

Data Pipeline

It can be further divided into 3 more parts as:

1. Data Ingestion (Mandatory)
2. Data Preprocessing (Optional, but suggested)
3. Feature Engineering (Optional)

The reason #1 is necessary because it retrieves the data from the source; without this, the model would not have any data. Possible sources for the data include Kaggle, the UCI repository, APIs, or even your personal data store.

The second reason is optional but recommended: the data being retrieved might not be cleaned, so you should observe some patterns in the data source itself and write a few lines to address any incorrect/Null/NaN/low quality data issuesthat may arise.

The use of feature engineering is entirely voluntary. This is not necessary if your data is sourced from a trustworthy source and contains only the features needed to train models. This might also be necessary, though, if your data has some features that are unnecessary or if you need to add new features that were created using older features.

Stage 2: Model Pipeline

This phase serves as MLOps’ central component. This is where the model testing, validation, and training are done. So, pay close attention to this phase since it will have a big impact on the result.

Model Pipeline

Here are key points to consider:

• To monitor an ML model’s performance over time, add logs.
• Prior to deployment, use hyper-parameter tweaking methods (GridSearchCV, RandomSearchCV, etc.).
• Make sure the models adjust to various data trends.
• Handle various model iterations for rollback and comparison.
• If you work for an organization, managing several models can be done through a Model Registry.

Any model that you design must first be tested and validated using the current data trend. The process of model validation starts here. You should bear the following tips in mind when performing model validation:

• Make use of validation strategies such as Leave One Out, Stratified KFold, and KFold Cross Validation.
• Depending on the nature of the problem, evaluate performance using measures such as accuracy, precision, recall, F1-score, MSE, RMSE, MAE, etc.
• Make an effort to keep the model’s bias-variance tradeoff in place. You don’t want to give up on one in favor of the other.

Lastly, use Continuous Delivery, Testing, and Improvement (CI/CT/CD) to automate the provision of new data versions and training. Make use of DevOps tools or cloud providers that facilitate CI/CT/CD.

Stage 3: Deployment Pipeline

This is the final stage of the project, where the finalized model from Stage #2 is deployed into production. It is crucial as it helps to ensure that the models are scalable and make the predictions as required. It can be further divided into 3 sub-parts as:

Deployment Pipeline

1. Deployment — Integrating the ML Model into production environment so it can be used by end user.
2. Monitoring and Logging — To identify problems early and preserve dependability, track and log model performance.
3. Retraining and Maintenance —If the model’s performance declines or its data drifts, retrain it using fresh data to maintain optimal outcomes over time without the need for human involvement.

Conclusion

The pipeline ends when the model is put into production. Although this simple pipeline automates MLOps, it has some drawbacks.

• Model not generalizing well, resulting in frequent training of model.
• Setting up the right infrastructure for model deployment and scaling.
• Management of data and its versions.
• Ensuring smooth collaboration among stakeholders.

As a result, producing and handling MLOps pipeline can be a tedious task. Here are some of the best practices you can use to tackle the challenges:

• Automate any stage to its best, reducing human intervention.
• Try each stage independently before combining it in a final product.
• Design a modular, scalable infrastructure for production and development.
• Continously monitor the development of models.
• Foster healthy team communication.

By following these procedures, your machine learning models will be put into production rather than remaining in Jupyter notebooks. We’ll build a simple project using this 3-Stage Pipeline Model in the upcoming article.

As MLOps develops, it will be essential to the success of machine learning in many sectors of the economy by converting abstract models into useful, scalable tools that generate actual commercial value.