Are your AI projects stuck in PoC purgatory?

Artificial Intelligence (AI) has become an essential driver of business transformation. However, many organisations struggle to move from AI proof-of-concepts (PoCs) to fully operational AI solutions.

Why do so many AI proof-of-concepts fall short of becoming production-level solutions?

  • Ill-defined use cases and underlying business cases
  • Lack of technical capability
  • Insufficient capacity to build and run productionised AI solutions

Model Operations (ModelOps) has emerged as a critical solution to these challenges. By providing a structured and scalable approach to managing AI models in production, The ModelOps framework bridges the gap between AI PoC failure and fully adopted, embedded AI programs within an organisation’s operations.

A staggering 90% of AI PoCs fail to reach production

Forbes

What is ModelOps?

ModelOps is a standardised service that enables the end-to-end management of your AI and machine learning (ML) models in production. It essentially ensures that your models remain relevant, secure, and optimised, enhancing service delivery through agility, automation, and governance.

Ultimately, ModelOps streamlines your AI operations whilst enhancing business value with new features and addresses several key challenges that organisations face when deploying AI solutions:

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Continuous model monitoring

Ensuring AI models are always performing as expected

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Governance and compliance

Managing data security and regulatory requirements

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Operational efficiency

Reducing downtime and improving service quality

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Scalability

Adapting AI models to evolving business needs

Excellence in service delivery

A structured approach ensures that all AI outcomes are planned and managed efficiently. For example, small enhancements are plotted through agile sprints, and changes are controlled via a clear, controlled change process. AI resolver groups ensure service restoration, incident resolution, and continual service improvement tracking while adhering to SLAs and KPIs.

By having a well-organised and systematic approach, you can leverage AI technologies to their full potential, drive innovation, and achieve your desired objectives with greater precision and confidence.

AI model enhancements

ModelOps enables seamless bug fixes, product/model upgrades utilising a simple ‘T-shirt size’ approach with all enhancements following a structured process:

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Identify and prioritise enhancements

Gather feedback and prioritise enhancements based on impact, urgency, and feasibility

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Develop and validate enhancements

Improve AI models through structured enhancement identification, working with key stakeholders and user communities

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Deploy enhancements

Continually streamline through the implementation of CI/CD pipelines, ensuring quick and reliable updates, while continuous performance monitoring allows for the early detection of any issues

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Feedback and iteration

Collect feedback after deployment (both quantitative: technical and qualitative: stakeholder and user communities) and make further adjustments based on the feedback and performance data

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Documentation and governance

Document and govern all enhancements through a structured process to ensure compliance and maintain detailed documentation

Building the diverse dream team

Establishing a collaborative team structure is crucial for the success of ModelOps. A multi-functional team (ModelOps POD) should comprise experts from various disciplines, for example:

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AI
Engineering

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Data Engineering

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Python Development

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DevOps Engineer

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Testing & UI Development

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Platform Engineering

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Delivery Leadership

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Business Analysis

This skills diversity drives a culture of innovation, ensures comprehensive problem-solving, and enhances the deployment and maintenance of AI models effectively.

AI Excellence with ModelOps

ModelOps provides a structured approach to managing AI models, ensuring that they are always performing as expected, secure from potential threats, and capable of adapting to new business needs. By automating many of these processes, ModelOps helps organisations avoid the pitfalls of PoC purgatory and achieve successful, full-scale AI deployments.

The benefits of ModelOps are considerable and include:

  • Increased efficiency
  • Better alignment with business objectives
  • Scalability of AI solutions
  • Improved AI governance and compliance

These collectively transform AI initiatives into successful, full-scale deployments that drive business value.

Our ASPIRE ModelOps service provides the people, process, and tools needed to implement a highly efficient AI delivery model. Whether you prefer an in-house approach, a hybrid model, or a fully outsourced service, we have the expertise to drive your AI success.

Don’t let your AI projects get stuck in PoC purgatory, embrace ModelOps and unlock the full potential of your AI solutions.

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