How to Deploy Your NLP Model to Production as an API with Algorithmia

Machine learning powers many intelligent applications we use daily, from product recommendations to chatbots to fraud detection. However, developing an accurate model is only half the battle.

According to Algorithmia, 55% of companies take over 7 weeks to deploy a machine learning model into production, with 38% taking over 2 months. The challenges include differences between research and production environments, integrating with existing systems, and continuous delivery.

Deploying models to deliver real business value is a crucial yet difficult part of the ML lifecycle. In this tutorial, we‘ll learn how to take an NLP model from research to production using Algorithmia, a popular MLOps platform.

We‘ll cover:

1. What is Algorithmia and how does it help deploy models?
2. Building a text summarization model in Python
3. Deploying the trained model on Algorithmia
4. Using the deployed model in your applications via an API
5. Best practices and tips for model deployment

Whether you‘re a data scientist learning to productionize models or a developer integrating ML into your applications, this guide will walk you through the process step-by-step. Let‘s dive in!

What is Algorithmia?

Algorithmia is a Machine Learning Operations (MLOps) platform that enables data scientists and developers to deploy models into production quickly and easily. It provides the infrastructure and tools to host, serve, scale, and manage models as web services.

Here are some key features of Algorithmia:

• Supports deploying models built in various languages and frameworks, including Python, R, Java, and common ML libraries
• Hosts models in scalable containers and exposes them as REST APIs for easy integration
• Provides a user-friendly web interface and CLI for managing models
• Offers versioning, collaboration, and governance features for the full model lifecycle
• Integrates with popular tools like GitHub, Azure ML, and Jupyter

By abstracting away the infrastructure complexities, Algorithmia allows data scientists and ML engineers to focus on building models rather than worrying about deployment mechanics. Over 90,000 developers and major companies like P&G, Merck, and Deloitte use Algorithmia to deploy their ML models.

With this overview in mind, let‘s see how to deploy a model to Algorithmia.

Building a Text Summarization Model

To demonstrate deploying an NLP model, we‘ll build an extractive text summarization model in Python. Extractive summarization involves selecting key sentences from the input text to form a concise summary. It‘s commonly used for news articles, reports, and long documents.

We‘ll use the CNN/DailyMail dataset, which contains news articles and their bullet point summaries. The model will learn to take a full article as input and output a summary.

Install Dependencies

First, let‘s install the required libraries. We‘ll use NLTK for text processing, scikit-learn for feature extraction, and gensim for word embeddings.

pip install nltk scikit-learn gensim

Prepare Data

Next, download and preprocess the CNN/DailyMail dataset:

import gensim.downloader as api

# Load dataset
dataset = api.load("cnn_daily_mail")

# Split into articles and summaries
articles = [" ".join(filter(lambda x: x != "<S>", article[0])) 
            for article in dataset]
summaries = [article[1] for article in dataset]

print(f"Loaded {len(articles)} articles and summaries.")

This loads the articles and summaries into separate lists. The articles need some additional cleaning to remove special tokens.

Extract Features

To select summary sentences, we need to convert the article text into numerical features. We‘ll calculate TF-IDF scores and TextRank scores for each sentence. TF-IDF finds the most informative terms, while TextRank identifies the most central sentences.

import nltk
from sklearn.feature_extraction.text import TfidfVectorizer
from gensim.summarization import summarize

def preprocess(text):
    return nltk.sent_tokenize(text)

def extract_features(article):
    sentences = preprocess(article)

    # TF-IDF features
    vectorizer = TfidfVectorizer()
    tfidf_matrix = vectorizer.fit_transform(sentences)
    tfidf_scores = tfidf_matrix.mean(axis=1) 

    # TextRank scores
    try:
        textrank_scores = summarize(article, ratio=0.2, split=True, scores=True)
    except ValueError:
        textrank_scores = [(sent, 0) for sent in sentences]

    features = [(sent, score[0], score[1]) 
                for sent, score in zip(sentences, zip(tfidf_scores, textrank_scores))]

    return features

The extract_features function takes an article, tokenizes it into sentences, calculates TF-IDF and TextRank scores for each sentence, and returns a list of sentence-score tuples.

Build Model

With the features extracted, we can now build a simple unsupervised summarization model. The model will rank the sentences by their average TF-IDF and TextRank scores and select the top N sentences as the summary.

def summarize(article, num_sentences=3):
    features = extract_features(article)

    top_sentences = sorted(features, key=lambda x: (x[1]+x[2])/2, reverse=True)[:num_sentences]
    top_sentences = sorted(top_sentences, key=lambda x: article.index(x[0]))

    summary = " ".join([sent[0] for sent in top_sentences])
    return summary

# Test model
print(summarize(articles[0]))

The summarize function takes an article and desired number of summary sentences. It extracts sentence features, ranks them by score, selects the top N, sorts them by their order in the original text, and joins them into a summary string.

That‘s it! We have a basic extractive text summarization model. Of course, this model can be improved in many ways – better sentence representations, supervised learning, abstractive approaches, etc. But it will serve our purpose of demonstrating deployment to Algorithmia.

Deploying the Model to Algorithmia

Now let‘s deploy our trained model to Algorithmia to make it available as an API. Follow these steps:

1. Create an Algorithmia Account

First, sign up for an Algorithmia account at https://algorithmia.com/signup. You can create a free account to get started.

2. Create a New Algorithm

In your Algorithmia dashboard, click the "Create New" button and select "Algorithm". Give your algorithm a name, select the language (Python 3.x), and choose your source code visibility.

3. Upload Model Files

In the "Source Code" editor, upload the following files:

• requirements.txt: List of Python dependencies
• summarizer.py: Python code for the model
• model.pkl: Serialized model object

Your requirements.txt should look like:

nltk==3.5
scikit-learn==0.24.1
gensim==4.0.1

And your summarizer.py should contain the model code we wrote earlier, wrapped in an apply() function that Algorithmia will call:

import nltk
from sklearn.feature_extraction.text import TfidfVectorizer  
from gensim.summarization import summarize
import pickle

# Download NLTK data
nltk.download(‘punkt‘)

# Deserialize model
with open("model.pkl", "rb") as f:
    model = pickle.load(f)

def apply(input):
    article = input["article"]
    num_sentences = input.get("num_sentences", 3)

    summary = model(article, num_sentences)
    return summary

Notice the apply function takes a dictionary as input, allowing us to pass both the article text and number of summary sentences. It then calls our model function and returns the generated summary.

4. Set Dependencies

In the "Dependencies" section, add the libraries from requirements.txt so Algorithmia installs them in the environment.

5. Compile and Test

Click the "Compile" button and wait for the algorithm to build. If everything goes well, you should see a success message.

Test your algorithm by passing it sample input in the format:

{
    "article": "some news article text...",
    "num_sentences": 3
}

The algorithm should return the summarized text. Debug any errors that occur.

6. Publish the Algorithm

Once you‘re happy with your algorithm, publish it by providing a version number, release notes, sample input/output, and royalty/access settings. Your model is now live and can be called via the Algorithmia API!

Using the Deployed Model

To use your deployed summarization model in your applications, you‘ll need to call it via Algorithmia‘s API. Here‘s how to do it in Python:

import Algorithmia

input = {
    "article": "some news article text...",
    "num_sentences": 3
}

client = Algorithmia.client(‘YOUR_API_KEY‘)
algo = client.algo(‘YOUR_USERNAME/summarizer/LATEST_VERSION‘)

print(algo.pipe(input).result)

Just replace ‘YOUR_API_KEY‘ with your Algorithmia API key and ‘YOUR_USERNAME‘ with your account username. The pipe function sends the input to the algorithm, which returns the result.

You can call the API from any language/framework that can make HTTP requests, like JavaScript, Ruby, Java, or shell scripts. This allows you to easily integrate the NLP model into your websites, applications, and workflows.

Deployment Best Practices

We‘ve walked through the mechanics of deploying a model on Algorithmia, but here are some additional tips to keep in mind:

Versions and Environments

Use Algorithmia‘s versioning system to manage different versions of your models. This allows you to rollback if needed and maintain a stable API while developing new versions.

Ensure your training and deployment environments match in terms of language version, library dependencies, and data pre-processing. Containerization helps achieve this consistency.

Model Monitoring

Log your model‘s inputs and outputs, along with key metrics like latency and error rate. Set up alerts to notify you of issues and monitor your model‘s performance over time.

Scalability and Security

Consider your model‘s resource requirements and scalability needs. Algorithmia can automatically scale your API but be aware of potential bottlenecks and costs.

Use API keys and algorithm-level permissions to secure access to your models. Implement authentication and rate-limiting as needed.

Testing and CI/CD

Write unit tests for your model code and integration tests for your API. Use continuous integration to automatically test, build, and deploy your models as you update them.

Conclusion

Deploying machine learning models into production is a critical but challenging task for organizations adopting AI. Platforms like Algorithmia simplify the process by providing the necessary infrastructure, tools, and integrations to take models from research to production quickly.

In this guide, we walked through the end-to-end process of building an extractive text summarization model in Python and deploying it to Algorithmia‘s scalable API platform. We also discussed best practices around versioning, monitoring, and testing models in production.

Of course, this just scratches the surface of NLP architectures, frameworks, and use cases you can deploy with Algorithmia – from sentiment analyzers to chatbots to translation systems. The general principles and workflow remain the same.

Hopefully this tutorial gives you a hands-on introduction to serving models at scale using Algorithmia. You can find all the code examples in this GitHub repo: [link to repo]. Give it a try and share what you build!

Now go forth and deploy some world-changing NLP models! And if you have any questions or insights, feel free to comment below or connect with me on Twitter [@yourusername]. Happy deploying!

Further Reading

If you want to dive deeper into machine learning deployment and operations, check out these resources:

• Algorithmia Docs: https://docs.algorithmia.com/
• Practical MLOps Book: https://valohai.com/mlops-ebook/
• ML in Production Course: https://madewithml.com/courses/mlops/
• Full Stack Deep Learning: https://fullstackdeeplearning.com/