Deploying NLP Models at Scale with MLOps: A Step-by-Step Guide

This blog will guide you through deploying NLP models at scale using MLOps, or Machine Learning Operations.

Published on:

April 29, 2024

MLOps or Machine Learning Operations enable us to create end-to-end ML pipelines from research to building the ML model to training and testing to training and monitoring, or in other words, the entire machine learning lifecycle. This field of MLOps is similar to DevOps but is specifically tailored for machine learning projects. 

MLOps enable organizations to build AI-powered solutions with two main components: continuous integration and continuous deployment. We can quickly build a seamless pipeline from deployment and customize them on the go. 

This blog will guide you through deploying NLP models at scale using MLOps, or Machine Learning Operations.

What is an MLOps Pipeline?

MLOps refers to the machine learning lifecycle or a data science project. This lifecycle consists of three critical components.

  1. Design
  2. Model Development
  3. Operations

Using this pipeline, you can easily automate the data collection, preprocessing, training, testing, deploying, and monitoring processes. It also allows you to detect new changes in the model and update it globally. Combining these three components will enable us to build an integrated system that can easily leverage the power of machine learning and software applications. 

Learn more: 

MLOps for the Modern CEO - A Detailed Analysis.

MLOps for Small to Medium Sized Businesses

Building MLOps Pipeline for NLP: Where to Start

Natural Language Processing (NLP) models are increasingly becoming popular and critical for businesses to stay competitive. However, deploying these models at scale is challenging, especially without proper infrastructure and tooling. 

Step 1: Data Preprocessing and Feature Engineering

Data Preprocessing and feature engineering are the critical steps in NLP. Preprocessing includes cleaning, normalizing, and transforming text data into a format that machine learning models can use. On the other hand, feature engineering involves selecting and changing features from the data to improve model performance.

These steps are usually performed before the model is trained, but they can also be integrated into the deployment pipeline if needed. By combining data preprocessing and feature engineering into the deployment pipeline, we can ensure that the same steps are applied to the data during production, thereby reducing the risk of errors and inconsistencies.

Step 2: Model Training and Validation

Once the data is preprocessed and the features are engineered, we can begin training the NLP model. The model should be trained on a representative dataset similar to the data it will be exposed to in production.

Once the model is trained, it should be validated on a holdout dataset to ensure that it performs well on unseen data. This step is essential to ensure that the model is accurate and reliable.

Step 3: Model Deployment

Model deployment involves making the model available to end users. Once the model is trained and validated, it is ready for deployment. There are several deployment options, including:

  • API: An API can be created to provide a RESTful (API) interface for the NLP model. This allows users to submit text data to the API and receive real-time predictions.
  • Batch Processing: Batch processing involves running the NLP model on a batch of data simultaneously. This is useful for applications that do not require real-time predictions.
  • Serverless Functions: Serverless functions, such as AWS Lambda or Google Cloud Functions, can deploy NLP models in a serverless environment. This approach provides scalability and reduces infrastructure costs.

Step 4: Monitoring and Maintenance

Once the NLP model is deployed, it should be monitored to ensure it performs as expected. Monitoring involves tracking key metrics, such as accuracy, latency, and throughput.

If the NLP model is not performing well, retraining or adjusting the deployment pipeline may be necessary. Maintenance is an ongoing process that involves keeping the NLP model up to date with the latest data and ensuring that it continues to meet business requirements.

Step 5: Scaling the Deployment

As the demand for the NLP model grows, it is essential to scale the deployment to handle the increased load. This can be achieved by adding more resources to the deployment infrastructure, such as increasing the number of servers or utilizing cloud-based services like AWS Auto Scaling.

Step 6: Continuously Improving the NLP Model

As more data becomes available and business needs evolve, it is essential to improve the NLP model continuously. This involves retraining the model on the latest data and updating the deployment pipeline to reflect the changes.

How to Deploy the NLP model using MLOps

Natural language processing (NLP) is one of the most exciting areas of ML, as large language models like GPT have entirely redefined the field. However, most focus is on the model themselves rather than the highly critical aspects of deploying these models in production. 

With the demand for NLP applications, deploying an NLP model into production is essential for any business. Deploying an NLP model into production can be complex. It involves developing and training the model, evaluating its performance, deploying it into production, and monitoring and maintaining its performance. By following these steps and establishing a model governance process, you can ensure that your NLP model is deployed efficiently, consistently, and at scale.

Let’s look at some of the best practices for NLP model deployments. 

Model Training Framework Vs. Model Deployment

Model training frameworks and model deployment are two critical stages of the machine learning pipeline. Each has its unique set of tools, techniques, and challenges. While model training focuses on developing and refining the accuracy and performance of the model, model deployment is concerned with making the model available for use in a production environment. Together, these two stages form a critical part of the overall machine-learning process, and their successful execution is essential for delivering impactful and reliable machine-learning solutions.

Model training frameworks are tools or libraries that provide an environment for developing, testing, and refining machine learning models. These frameworks allow developers to experiment with different algorithms, architectures, and hyperparameters to improve the accuracy and performance of the model. Some examples of popular model training frameworks include TensorFlow, PyTorch, and sci-kit-learn.

Model deployment requires different tools and processes from model training, often involving scalability, security, and latency challenges. On the other hand, model deployment makes a trained model available in a production environment. This involves taking the model developed and tested in the training stage and integrating it into an application or system where it can process in real time.

Choosing the NLP framework will directly influence how the model is deployed. Sklearn is one of the popular choices for simple classification models like logistic regression, SVMs, or Naive Bayes. Furthermore, it integrates well with Python backends. 

Deep learning models are often written in the PyTorch library as their autograd interface is ideal for building models that can create computational graphs in response to dynamic inputs. Numerous popular libraries like HuggingFace Transformers ( which works exceptionally well with pre-trained transformer models) are built on top of PyTorch. The python ecosystem is prevalent for NLP and ML. 

Backend Frameworks Vs. Model Deployment

While any combination of backend and language can efficiently work, using a backend developed in the same language as your model is often considered an efficient option. With this, you can easily import your model into your backend system without serving requests to different scenarios. This way, you can reduce the errors and keep your backend code clean and clutter-free. 

The popular backend solutions within the Python ecosystem are Django, Flask, and Node.js. 

Flask is recommended for quickly prototyping model microservices as it makes it easy to get a simple server up and running in a few lines of code. However, suppose you are building a production system. In that case, Django is more fully-featured and integrates the popular Django REST Framework for making complex, API-driven backends.

NLP models can also be deployed by:

  • Containerizing them by using tools like Docker
  • Deploying them over the cloud
  • Hands-on Deployment 

Challenges in Deploying NLP models

Deploying a natural language processing (NLP) model into production can be challenging, as several factors can impact the model's performance, scalability, and reliability. Here are some of the most common challenges in NLP model deployment:

  1. One of the critical aspects of NLP model deployment is ensuring the proper MLOps workflow. Having the right MLOps tools like Attri will ensure that your models can easily be reproduced by tracking all the steps needed in the model training and inference process. This involves hyperparameters, validation metrics, data versioning, etc. 
  2. Another critical challenge is NLP models need to be physically retrained. In such scenarios, it’s crucial to ensure that adding new training data and retraining does not degrade the existing model quality. For this reason, continuous model monitoring, as described above, of various NLP metrics is essential.
  3. NLP models are also required to be trained incrementally and while in production. In such cases, you would use different algorithms and libraries to deploy streaming NLP models in production. 

Related Reading:

How can Attri help you?

Attri is an extensible and open-source MLOps tool to automate and streamline your model development, deployment, and monitoring in the production environment. You can scale your machine learning development from research to production with our E2E solution giving your data science teams all tools they need in one place. 

This means you can now:

  1. Speed up your experimentation in building high-quality models and release them to production faster and securely. 
  2. Gain complete visibility with real-time insights on model performance. 
  3. Access the industry-specific AI-powered blueprints that can run on any infrastructure. 

Contact us to learn more about Attri and how we can help you streamline your ML workflows.