Machine learning (ML) models need to be deployed in production because ML models in production provide business value.
ML models in production can also help provide real-life predictions like weather forecasts and stock predictions. This article discusses four ways to deploy your ML models in production.
Four Ways to Deploy Your ML Models In Production
1. Deploying ML Models As A Container Application
Containers are a common environment for deploying and developing machine learning models.
Container application improves ML model efficiency and machine learning ML model monitoring by reducing the risk of model downtime and providing maintenance capabilities.
Containers contain all the elements that machine learning code needs to work efficiently.
Containers provide a consistent environment for running and deploying ML models.
It provides this consistent environment by deriving resources from a variety of on-premises and cloud systems configurations.
Container orchestration platforms like Kubernetes help automate container management.
2. Deploying ML Models As A Web Service Using Cloud Services
The easiest way to deploy a machine learning model is to deploy it as a web service for forecasting.
Deploying machine learning as a web service requires at least four steps.
The procedure is to first create the ML model. The second step is to train a machine learning model, then the ML model performance needs to be validated.
Finally, the ML model can be deployed with some popular cloud services.
The most popular cloud services for deploying ML models are discussed below.
Heroku: Heroku is a cloud platform that enables developers to deploy applications. It helps manage applications without incurring infrastructure issues.
It is flexible and easy to use. It also supports several used programming languages.
The programming languages it supports are Python, Java, PHP, Node, Go, Ruby, Scala, and Clojure.
Deployment can be done from the command line using the Heroku CLI (available to Windows, Linux, and Mac users).
To use Heroku you first link your Github repository to your Heroku account. Then you can upload your trained machine learning model to Heroku.
Google Cloud Platform: GCP provides IAAS and server-less computing environments. GCP offers three ways to deploy an ML model, which are google AI, app engine, and cloud functions.
The Google AI platform provides a comprehensive machine learning service.
Machine learning engineers and data scientists can use this platform to build machine learning projects from ideas to deployments.
Google App Engine is a Platform as a Service (PaaS) that provides an auto-scaling feature.
It allocates resources and allows web applications to handle more requests.
Google Cloud functions: it includes all functions built and hosted on Google Cloud. GCP functions run on-demand in the cloud.
You can use various triggers to call your ML web application.
3. Deploying ML Models As An API
Deploying ML models as application programming interfaces (APIs) allows them to be easily consumed. The APIs can be used on different platforms via an endpoint.
APIs expose ML and data science models to customers and third parties all in a safe and scalable way. Deploying ML models as an API guarantees reusability.
Reusability is a concept that allows you to use the model in many applications from any language or framework.
Some popular API libraries to use for the deployment of ML models are discussed below.
- FastAPI: FastAPI is a fast web framework for building APIs using Python. It reduces query time. It also provides simple, reduced code that allows you to design an API in minutes.
- Deta: Deta platform comes with easy to deploy CLI. It has high scalability, secure API authentication keys. It also has an option to change subdomain, and logging of the web traffic.
- Django rest framework: Django Rest Framework is a Django module built with python. Django REST Framework includes support for versioning. It is fairly flexible, making the task a little less tedious. It allows users and developers to quickly and easily perform GETs and POSTs requests.
- Flask: Flask is a python web framework. Flask can achieve fast performance. It integrates well with NoSQL and MongoDB. It can be used to build scalable APIs for consumption.
- Microsoft Azure Functions: Azure Functions provides a serverless cloud service. It also provides functions as a service (FaaS). It is used for the quick deployment of APIs. It allows you to write snippets of code that run your model, deploy your code and model to Azure.
4. Deploying ML Models In Embedded Technologies
Machine learning models can run on embedded and Internet of things (IoT) devices.
Running a machine learning model on an embedded device is commonly referred to as embedded machine learning.
Embedded machine learning unleashes the computing potential of billions of ubiquitous microprocessors and embedded controllers.
These microprocessors can be used in environments such as industrial plants, manufacturing floors, smart buildings, and homes.
Embedded machine learning also facilitates the processing of data generated by embedded devices that are mostly currently idle (such as Internet of Things devices).
Deploying machine learning models to edge devices helps reduce delay.
This is because the device may be closer to the user than the remote server. It reduces data bandwidth consumption.
This is done by sending processed results back to the cloud instead of raw data. This requires a large size and more bandwidth.
A popular library for this deployment is Tensorflow lite. Tensorflow lite supports Linux (Including Raspberry Pi) and Android, iOS.
Programming languages are determinants of how efficient code runs on embedded devices.
Popular programming languages for deploying ML models in embedded technologies are discussed below.
CC++: This is the most popular and widely used suite of languages used for embedded development. CC++ remains the most relevant language in embedded development.
This is because it was developed in the 1970s, at a time when computers were not much better than embedded devices today.
Python: Python is a very popular scripting language and also the language of choice for many ML developers.
Python can be used across several operating system platforms. Python contains the ‘MicroPython’ module.
MicroPython is a recompiled Python module with bare-bones architecture to fit onto a microcontroller.
Conclusion
Deployment is the most important aspect of a data science project.
Deployment of ML models is necessary to test out how the ML model performs on real-life data.
Four ways to deploy ML models in production were discussed. ML models can be deployed as a web container, a web service, an API, and finally in embedded technologies.
