Author: Oluwafemi Oluseki
Africa’s technology ecosystem is accelerating the race to integrate artificial intelligence at a fast pace. Whether it’s a Nairobi-based agritech startup whose computer vision algorithms can scan and detect crop yields or a Lagos-based fintech whose risk assessment algorithms can be incorporated into final products, machine learning is no longer limited to laboratories. However, most companies falter in the transition from a promising prototype to a solid production system.
As engineering teams try to scale these efforts, they face huge architectural barriers. The infrastructure required to train and serve machine learning models is fundamentally different from traditional web applications. Attempting to impose AI workloads on more normal web-friendly deployment pipelines results in the classic disaster of rising cloud costs, idle GPUs, and overloaded site reliability engineers (SREs).
An architecture that truly scales requires great architectural breakthroughs. It’s a standardized internal developer platform (IDP) designed with the special needs of machine learning and AI workloads in mind.
Why standard CI/CD fails for machine learning
Traditional software engineering uses a relatively simple paradigm of writing, pushing to version control, running automated tests, building containers, and deploying.
Machine learning completely breaks the paradigm. Developing AI is not just about code. This is a three-dimensional process that includes: code, data, model. In contrast to traditional software, tests are highly deterministic (pass/fail) and ML models are probabilistic. It’s not just about whether the code compiles or not, it’s about running the output continuously against large, constantly changing data sets to ensure the statistical accuracy of the results.
Typical CI/CD pipelines don’t have the capacity to handle a 500GB training dataset. While traditional Kubernetes clusters are great for scaling weightlifting microservices, they weren’t originally intended to run long-running, stateful ML training loops with exclusive access to specialized hardware like Nvidia GPUs or Google TPUs.
Additionally, ML model deployment does not end the job immediately after the model is deployed. Models degrade over time as real-world data evolves. This phenomenon is called model drift. Traditional monitoring devices (like Datadog or New Relic) can tell you that your server’s CPU usage is spiking, but they can’t tell you that your neural network is producing biased or inaccurate predictions.
ML-focused IDP architecture
To address this, our advanced platform engineering team is developing a dedicated ML-IDP. These environments are built on the underlying cloud infrastructure and provide a standardized abstraction layer for data science teams on a self-service basis.
But what actually goes into building an IDP, and specifically AI? This involves deploying a number of specialized infrastructure elements, including:
1. Computational abstraction and GPU time slicing.
The most expensive cloud resource a startup can rent is a GPU. The biggest mistake with ad hoc ML infrastructure is that data scientists order a powerful cloud instance for their notebook, then run a two-hour training job, and then leave the instance idle over the weekend. Data scientists don’t have the luxury of startup budgets, and a single instance (like an Nvidia A100 or H100) can cost thousands of dollars per month. Advanced ML-IDPs employ high-level scheduling and time slicing of physical GPUs (such as Nvidia multi-instance GPUs) to partition a single physical GPU into multiple physically separated instances. IDP automatically spins up compute only when a job is currently running, and aggressively spins it down as soon as the job completes to maximize resource utilization.
2. Standardized MLOps orchestration
IDP implements a standard-form orchestration layer rather than allowing teams to rely on disjointed bespoke bash scripts or manual deployment. Platforms include Kubeflow and MLflow. This provides a single system to track experiments, provide a model registry (model versioning), and automatically deploy inference endpoints. This also ensures reproducibility. If a regulator or auditor asks what decisions a fintech company made regarding its credit scoring model six months ago, IDP will provide the specific code, data snapshot, and parameters that were applied.
3. Feature store and data pipeline
The quality of an ML model is not better than its data. Specialized ML-IDPs have a centralized feature store, a central repository where data engineers create, compute, and store reusable features for their data. There is no need to browse the data warehouse and write complex SQL queries to retrieve the same history of customer transactions in the data warehouse. All they do is access precomputed functionality within the IDP. This saves a lot of computation time and avoids training and service skew, which is a huge concern where models work well in the lab but fail in the real world because the actual data pipeline computes the features differently.
ROI for Lean Engineering Teams
For African startups, the rationale behind developing (or purchasing) an ML-centric IDP comes down to resource optimization, including human and computational resources.
We are in a talent market where it is very difficult and expensive to hire experienced MLOps engineers and Kubernetes experts. Separate data science and DevOps by centralizing complex ML infrastructure into a standardized platform.
Data scientists don’t need to open an IT ticket to request compute power or find out how to build Docker containers using CUDA drivers. They can simply log into their IDP, query the workspace, and focus on everything related to training their models. Meanwhile, instead of tracking shadow IT usage across different AWS accounts, platform engineering teams have a single, secure and cost-effective backbone infrastructure base. Security vulnerabilities are also greatly minimized because vulnerable customer data is no longer stored in unmonitored, unoutlined S3 buckets created on a whim to accomplish specific experiments.
The companies that succeed without the need for the most complex algorithms are not necessarily the ones that deploy AI as a competitive stake. The winners will be the companies with the infrastructure to turn chaotic ML experiments into predictable, scalable, and highly secure automated assembly lines. An important first step in that journey is standardizing your ML infrastructure with a dedicated IDP.
About the author
Oluwafemi Oluseki IHe is a Cloud Solutions Architect and Senior Platform Engineer, working across AWS and Azure with a focus on platform engineering, security, and AI infrastructure.
He is a co-founder of LimeSoft Systems, which develops cloud-native platforms and security-focused infrastructure to support modern application delivery. His work has supported production systems used by over 50 teams and organizations., Improves deployment consistency and operational reliability.
He writes and teaches within the African technology community.
