Deploying machine learning models is often a bottle neck in realizing the value from data science investments. Utilizing the cloud, combined with a microservices based infrastructure, to deploy machine learning models can make the process less complex, and make life easier for everyone involved. Let’s look into how analytic migration to the cloud can help the data science and IT teams specifically:
Data Science
As a data scientist, your main concern is obtaining an efficient way for an analytic to become a product and an application. The model’s mathematic integrity is paramount, and should not be impacted “where the model sits” in the infrastructure. A microservice approach like FastScore allows just this – to encapsulate the math in a “guaranteed to run” environment that allows flexible deployment across the infrastructure landscape.
In the end, data science teams will support any effort to create scale and enable cost effective use of their models. The cloud provides a fast, secure, and low cost environment for machine learning models to be deployed, making it a perfect option for data scientists and their models to consider.
IT
When it comes to machine learning operationalization, the IT team is looking for increased flexibility, low cost, and simple manageability. Obtaining these benefits for the growing analytics portfolio usually involves enabling a new and growing diversity of tools. The challenge IT faces is where and how to standardize processes for the implementation of production models. The adoption of standard APIs and containerization in the cloud is an important place to start. Once containerization is implemented into an organization’s infrastructure, the IT team can balance on premise, cloud, or hybrid wherever they see fit, as well as develop processes to promote, track and manage machine learning assets, leading to sustainable scale and value creation
Data Science & IT Handoff
When it comes to process flow, adopting microservices and cloud computing into your analytics allows you to create a standardized workflow that saves time and increases efficiency. Data science and IT will benefit from a standardized method to operationalize machine learning models, regardless of their use case or creation environment. Each team will have their own responsibilities in the process, and can collaborate to ensure rapid value creation from each model. Microservices allow both groups to be more independent, and helps each department function next to each other with less friction.
Utilizing the cloud and a microservices based architecture to deploy machine learning models provides a path to rapid deployment into critical business applications. Whether you are on the IT or data science team, utilizing microservices and the cloud provides a path to standardization, defined roles and responsibilities and the most flexibility for the growing portfolio of data science tools, techniques and applications in the enterprise.
