Platform Engineering for LLMs”: Building the ‘Golden Path’ for Data Scientists to Deploy Safely

The African tech ecosystem is experiencing an undisputed AI boom. The AI integration race is accelerating fast. We have Agritech startups analysing crop health with computer vision and Fintechs applying Large Language Models (LLMs) to local customer support. But beneath these glamorous pitch decks and proof-of-concept demonstrations is a nightmare: implementation of machine learning models into production is a headache. A predictive model or a specially optimised LLM is basically useless when it requires an engineering team six months to make it available to a real user. The issue is not a lack of talent in data science; rather, it is the tension between Data science and Operations.

Startups have to go beyond deploying ad-hoc scripts in order to survive in this rapidly evolving market. The panacea is the development of standardised Internal Developer Platforms (IDPs) that are specifically developed to support AI and ML workloads. A “Golden path” is thus constructed.

The “Notebook to Production” Chasm

In order to understand the problem, we need to examine how data scientists operate. They are mainly mathematicians and researchers, not necessarily software engineers. Their preferred ecosystem is a Jupyter Notebook. This is an interactive and extremely adjustable workplace that is ideal to educate models, adjust hyperparameters and examine data.

But a Jupyter Notebook is not a production-ready application.

After a data scientist has completed the process of training a model, the standard approach is to simply toss it over the wall to the DevOps or software engineering team. The DevOps team is then left with the task of reverse-engineering the dependencies, understanding how to modelize in a container, securing the API endpoints, configuring the GPU-accelerated cloud instances and configuring model drift monitoring.

This back-and-forth handoff causes friction. It results in weeks of delays, misaligned environments, and frail deployment pipelines that fail the instant the model has to be updated.

Enter Platform Engineering: Paving the “Golden Path”

Platform Engineering is the science of creating and developing self-service software engineering organisations. When used in the field of AI (also called LLMOps or MLOps), it is the creation of an Internal Developer Platform (IDP) that hides the heavy lifting of infrastructure.

This platform will establish a Golden Path; a series of well-documented, highly automated, and secure guardrails, so that data scientists can run their work on their own.

When a data scientist remains in the Golden Path, there will be no need to craft complicated Kubernetes manifests, Terraform modules, or ask the DevOps team to spin up an AWS EC2 instance with an Nvidia GPU. It is all done on the platform automatically.

What Does an AI-Focused IDP Look Like?

An effective Internal Developer Platform around ML workloads will often offer data scientists a self-service interface (a simplified command-line interface) which has:

• Standardised Templates: Data scientists have pre-approved templates to use on common use cases (i.e. “Deploy an LLM via FastAPI” or “Batch Processing Model”).
• Automated Provisioning: The IDP can automatically send a single click command to deploy the appropriate cloud infrastructure, including injecting the appropriate GPU drivers and scaling settings.
• Built-In Security and Compliance: In the case of African fintechs and healthtechs dealing with sensitive information, compliance cannot be compromised. The Golden Path injects security sidecars, data anonymization layers, and role-based access controls automatically and does not require the data scientist to do it by hand.
• Out-of-the-box Observability: The platform will come with logging and monitoring tools that are automatically attached and will provide instant insight into the health of the infrastructure (CPU/Memory) as well as the health of the model (accuracy decay and hallucinations).

Empowering Lean Teams to Ship Faster

In the case of African startups, the idea of using Platform Engineering in place of ML is a huge competitive edge. Most startups here just simply cannot afford to employ specialised MLOps engineers on every single product team. They will run lean.

With an early investment in an Internal Developer Platform, a small team of two or three data scientists can work with the output of a team of ten. You take out the infrastructure bottlenecks and reduce worktime from months to hours.

Finally, Platform Engineering transforms the culture in a technology firm. DevOps engineers no longer perform the role of gatekeeper or ticket-resolver; they are now platform builders. Data scientists no longer have to wait on infrastructure, and can get back to doing what they are good at, which is extracting value out of data and developing the AI solutions that will characterise the next phase of African tech.

Oluwafemi Oluseki is a Cloud Engineer working across AWS and Microsoft Azure, with a focus on infrastructure, security, and DevOps practices. He has experience supporting cloud environments and helping teams adopt reliable deployment processes.

He is building LimeSoft Systems, where he explores cloud-native solutions and secure application infrastructure. He is also passionate about mentoring and supporting emerging tech professionals within the African tech community.