How to Simplify the Process of Building Production-ready AI Services and Reduce the Time for Resource Management in the Automotive Industry?

While the automotive industry is rapidly changing by adopting a software-first strategy, like in other sectors, automotive enterprises struggle with productionizing AI and ML R&D projects. Machine Learning and Data Science teams face numerous challenges, including determining the proper technology, automating workflows, managing computing resources, managing data, and building solutions meeting internal regulations. All these issues can complicate the project even before the kick-off.

So, how do we support AI teams to overcome typical challenges and enable ML engineers and Data Scientists to focus on creating and bringing artificial intelligence algorithms to production? 

The implementation of a dedicated deployment platform is a solution that is well suited for the automotive industry. In particular, it allows you to: 

• accelerate the productionization of AI and ML applications;
• provide an easy and quick project and user onboarding;
• simplify access to data and computing resources;
• ensure high scalability -even when the number of accounts far exceeds thousands of users.

To illustrate the process of working on the platform, let’s have a look at a project that the Grape Up expert team had the opportunity to implement.

Building AI and ML deployment platform using proven cloud-native technologies – practical use case

Our client – a well-recognized sports car manufacturer – set us the goal of designing a reliable and extensible architecture capable of handling hundreds of customer accounts for the platform. Tools were to be selected for the project to ensure the scalability and flexibility of operations. The idea was to provide fast and efficient production of AI/ML software.  

Along with building the platform architecture leveraging Terraform orchestrating Cloud Formation scripts, Grape Up ensured efficient migration of existing environments. The solution was integrated with Continuous Integration pipelines and the E2E tests set. To reap the benefits of high-quality performance in multiple regions worldwide, the platform was hosted on the AWS cloud. 

Results?

An AI Deployment Platform was delivered, which was capable of managing a huge number of AI/ML projects and allowed for streamlined processes to create, test, and deploy artificial intelligence and machine learning models into production for Data Science teams. 

Developers were guided through the company’s deployment processes and supported with reusable blueprints that could be leveraged at the initial steps of the development.

The cloud-native toolkit that was created provided flexibility and agility, at the same time supporting innovation in the vendor’s operations. After introducing improvements to the platform, the customer could reduce the code by 80%, while retaining high quality and testability.

All those solutions allowed AI software development teams to work more efficiently and reduce time-to-market for new products and services.

Do you want to more effectively leverage AI and ML tools in building scalable and flexible platforms for your automotive operations? Get in touch with Grape Up experts. We’ll help you choose the right tools and technologies, streamline your ongoing processes and identify the strengths and weaknesses of your platform.