The concept of deployment in data science refers to the application of a model for prediction using new data. Building a model is generally not the end of the project. Even if the purpose of the model is to increase knowledge of the data, the knowledge gained will need to be organized and presented in a way that the customer can use it. Depending on the requirements, the deployment phase can be as simple as generating a report or as complex as implementing a repeatable data science process.
The deployment of a machine learning (ML) models to production starts with actually building the model, which can be done in several ways and with many tools. The approach and tools used at the development stage are very important at ensuring the smooth integration of the basic units that make up the machine learning pipeline. If these are not put into consideration before starting a project, there’s a huge chance of you ending up with an ML system having low efficiency and high latency. For instance, using a function that has been deprecated might still work, but it tends to raise warnings and, as such, increases the response time of the system. The first thing to do in order to ensure this good integration of all system units is to have a system architecture (blueprint) that shows the end-to-end integration of each logical part in the system.
One of the primary options for cloud-based deployment of ML models, along with others such as AWS, Microsoft Azure, Google Cloud Platform (GCP) etc.
Deployment
AWS – AWS is a cloud computing service that provides on-demand computing resources for storage, networking, Machine learning, etc on a pay-as-you-go pricing model. AWS is a premier cloud computing platform around the globe, and most organization uses AWS for global networking and data storage. The article is for those Machine learning practitioners who know the model building and even they have deployed some projects on other platforms but want to learn how to deploy on major cloud platforms like AWS.
Azure – Azure Machine Learning is an integrated, end-to-end data science and advanced analytics solution. It enables data scientists to prepare data, develop experiments, and deploy models at cloud scale.
The main components of Azure Machine Learning are:
- Azure Machine Learning Workbench
- Azure Machine Learning Experimentation Service
- Azure Machine Learning Model Management Service
- Microsoft Machine Learning Libraries for Apache Spark (MMLSpark Library)
- Visual Studio Code Tools for AI
Together, these applications and services help significantly accelerate your data science project development and deployment.
GCP – An AI platform that makes it easy for machine learning developers, data scientists, and data engineers to take their ML projects from ideation to production and deployment, quickly and cost-effectively. From data engineering to “no lock-in” flexibility, Google’s AI Platform has an integrated toolchain that helps in building and running your own machine learning applications. As such, end-to-end ML model development and deployment is possible on the Google’s AI Platform without the need for external tools. The advantage of this is that you don’t need to worry about choosing the best tool to get each job done, and how well each unit integrates with the larger system.
With GCP, depending on how you choose to have your model deployed, there are basically 3 options which are:
- Google AI Platform
- Google Cloud Function
- Google App Engine
Free Courses to upskill yourself:
- Intro to cloud computing – IBM Skills Network
- Google Data Analytics – Google
- AWS Cloud Practitioner Essentials – Amazon Web Services
- Microsoft Azure Machine Learning for Data Scientists – Microsoft
Summary
Deployment is a crucial step in the data science process that involves applying a model for prediction using new data. Smooth integration of the basic units that make up the machine learning pipeline is essential for efficient and low-latency performance of the deployed system. Cloud-based deployment of ML models provides a convenient and cost-effective solution, and major platforms like AWS, Azure, and GCP offer a range of tools and services to accelerate your data science project development and deployment. To upskill yourself, you can take advantage of free courses offered by IBM, Google, AWS, and Microsoft. Keep learning and advancing your skills to stay ahead in this fast-evolving field!
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