Imagine you’re great at art. You have created some of the best portraits of your friends and family, and they have the utmost respect for your talents. However, you never present these fine art pieces to the outside world. Hence, you don’t get the deserved recognition. The machine learning (ML) realm is no different. Developing intelligent models is just the start. It’s only after model deployment that they create their magic. This blog covers the importance of model deployment in machine learning and its step-wise implementation process.
Table of contents
What is Model Deployment in Machine Learning?
In machine learning, model deployment occurs when a model trained to perform one or more tasks (like making predictions or sharing insights) is integrated with a real-world system or application. For instance, a restaurant is trying to understand the footfall it will receive during the holiday season.
Model deployment concerns using the trained model and executing it within the restaurant’s CRM or record systems. Post-deployment, the model would analyze their historical and holiday season data and share valuable insights that can help the restaurant plan its inventory and unique dishes they wish to serve.
Now that you know the basics of model deployment, let’s explore how an ML model can be deployed in Python.
8 Key Steps for Deploying a Machine Learning Model in Python
Many programming languages can facilitate model deployment. However, this section is all about machine learning model deployment in Python. It contains a complete walkthrough from data preprocessing to training, serialization, and creating an API to deployment.
For example, we’ll consider using FastAPI in Python to deploy a sentiment analysis model.
1. Data Preprocessing
- You can start with simple or advanced techniques like mean or regression amputation to handle missing values.
- Having all features on the same scale is imperative, and scaling techniques like standardization or normalization should be used as the next step.
- Next, you must use labeling or one-hot encoding methods to encode categorical variables.
- Concerning our example, you can start by preprocessing text data for sentiment analysis.
- In addition, apply techniques like lemmatization or stemming, perform tokenization, and remove unnecessary characters.
2. Training & Optimization
- Here, we select the perfect ML model, such as a Random Forest Classifier, which is congruent with our problem.
- Next, you must use ‘train_test_split’ from scikit-learn to split the training and testing data sets.
- You can promptly train the model using the training data with techniques like a grid or random search.
- Then, by tuning hyperparameters, such as the number of trees in a random forest the model should be optimized.
- Considering our example, we’ll use an algorithm like Naive Bayes or Support Vector Machine to train the sentiment analysis model.
- Lastly, you will implement techniques like cross-validation to optimize the model’s hyperparameters.
3. Serialization
- Once you are satisfied with your model’s performance, it’s critical to serialize and save it for future use.
- Serialization helps store the model on disk by converting it into a binary format. In Python, it’s commonly done using the ‘joblib’ library.
- As a final step, you should save the SVM classifier model with the dump function by giving the model object and the file path where you want to save (e.g., joblib.dump(model, ‘model.pkl’).
4. Assembling the Deployment Environment
- The next step would be creating a web API by installing the necessary libraries like FastAPI.
- Setting up a deployment environment to deploy the model is highly crucial.
- To offer the perfect environment, one must use ‘pip’ to install the required packages and build a virtual environment to manage dependencies.
- This ensures conflicts with your other Python projects by isolating the deployment environment and having the required packages.
- Concerning our example, you must install the required dependencies and build a new FastAPI application.
5. Creating the Deployment API
- It’s mandatory to have a Python script to define the API endpoint to create a deployment API for our sentiment analysis model.
- The script attends to your incoming requests, preprocesses the data, and shares insights or predictions utilizing the trained SVM classifier model.
- As a last step, one has to match the model’s input requirements by loading the serialized model into memory using ‘joblib.load’ and preprocessing incoming data.
6. Testing & Validation
- Testing and validation become crucial to ensure the deployment has adequate functionality.
- This includes using tools like requests or curls to send sample requests to the API.
- It’s followed by model monitoring, verifying the output against the expected results.
- The final step involves ensure consistency and accuracy across multiple models by comparing the predictions made during model development against the ones made post-deployment.
7. Deploying the Model
- Firstly, one must choose a cloud platform or server that provides the required infrastructure to host the deployment, like AWS, Azure, or Heroku.
- The next step is to configure your server or cloud to handle incoming requests and route them to the API endpoint defined in the last step.
- You must follow best practices for security, scalability, and availability when deploying your model in the production environment.
8. Maintenance & Monitoring
- Whether existing or new model, you must have logging & monitoring mechanisms that track API usage, performance metrics, and potential errors.
- As a precaution, it’s necessary to examine your model’s performance and invest in retraining if necessary.
- You must make the required upgrades to your model, whether it’s enhancing the API to handle more traffic or incorporating new model versions.
- Using continuous delivery to monitor and maintain your model ensures reliable and accurate predictions in real-world scenarios.
Conclusion
Whether you’re a data science or machine learning expert or aspiring to become one, possessing the skills in ML model deployment is essential. It equips professionals with more than theoretical knowledge and offers the skills to apply ML models effectively in real-world settings. Mastering deployment ensures that ML solutions are not just developed but also successfully implemented, delivering practical value and seamless integration into production systems.
If you’re someone who is looking for experts that help you brainstorm, develop, deploy, and maintain your businesses’ machine learning model, you’ve come to the right place. As a machine learning consulting company, we hold more than 14 years offering ML services for our clientele across the globe.
Get in touch with us to unlock the power of machine learning with Maruti Techlabs. From model development to seamless deployment, our expert ML services help you drive innovation, efficiency, and real business impact.
