From Prototype to Product: Guidelines for Scaling an AI Project into Production

In the last two years, many companies have experimented with Artificial Intelligence projects. Often, these are proof-of-concept (PoC) developed in a few weeks, perhaps with limited datasets and exploratory goals. The problem is that a large portion of these projects never moves beyond the testing phase: they remain interesting experiments but do not become real and scalable products.

Bringing an AI project into production requires a shift in mindset: it’s not enough to have a model that “works” in the lab; an ecosystem of processes, tools, and governance is needed to ensure reliability, security, and sustainability.

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Statistics show that over 70% of AI projects do not make it to production. The main causes are:

• Absence of metrics: a prototype may seem effective, but without KPIs, the value cannot be demonstrated.
• Non-scalable data: datasets used for PoCs do not reflect the complexity of the real world.
• Missing infrastructure: models trained on laptops are not ready for enterprise environments.
• Weak governance: there are no security, compliance, and audit policies in place.
• Unrealistic expectations: management often expects miracles, without considering costs and limitations.

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The path from PoC to product

Scaling an AI project means following some fundamental phases:

1. Definition of Requirements

• Identify the concrete use cases and the involved stakeholders.
• Establish measurable KPIs: accuracy, response times, inference costs.
• Evaluate regulatory constraints (GDPR, DORA, NIS2, sector-specific).

2. Data Preparation

• Create pipelines for data collection, cleaning, and labeling.
• Define quality policies and continuous updating.
• Consider data governance tools and centralized catalogs.

3. Choice of Architecture

• Evaluate pre-trained models vs fine-tuning.
• Decide between external APIs (OpenAI, Anthropic, etc.) and self-hosted models (Llama, Mistral).
• Define scalability: serverless, Kubernetes, GPU on-demand.

4. MLOps Implementation

The heart of scalability is Machine Learning Operations (MLOps):

• Versioning of data and models.
• CI/CD pipelines for training and deployment.
• Continuous monitoring of performance and drift.
• Automation of rollbacks in case of regression.

5. Security and Compliance

• Anonymization and pseudonymization of data.
• Granular access controls and request logs.
• Complete audit trail for internal and external inspections.
• Policies for the ethical use of AI (no sensitive data in prompts, explainability).

6. Deployment and Monitoring

• Define clear SLAs: response times, uptime, costs.
• Use observability tools: tracing, logging, alerts.
• Implement A/B testing systems to compare model versions.

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Recommended Tools and Stacks

• MLOps: MLflow, Kubeflow, DVC, Airflow.
• Deployment: Docker + Kubernetes, serverless (AWS Lambda, Azure Functions).
• Vector DB (per RAG): Pinecone, Weaviate, Qdrant, Postgres+pgvector.
• Monitoring: Prometheus, Grafana, EvidentlyAI.
• Security: Vault, IAM co