With all the discussion of security in AI systems, and the need to ensure machine learning (ML) models are robust and ready for deployment, some might mistakenly believe that the addition of a process for independent testing and validation is a barrier to efficient deployment. However, the truth is quite the opposite: independent testing can be a driver of business success, aligning MLOps teams with leadership to ensure alignment on the bounds for mission success.
Why the current MLOps pipeline can lead to faulty models
Due to the rapid acceleration of the global AI race, the pressure is on to not just develop the next generation of ML models but deploy them as soon as possible. However, gaps in current MLOps processes in many organizations create natural speed bumps on the road to adoption and deployment. This is for a variety of reasons:
1. ML models are often developed in siloed efforts
There is a knowledge gap between subject matter experts (SMEs) and the data scientists tasked with building and testing models. Most SMEs don’t have the mathematical knowledge to develop metrics related to the task, while data scientists struggle to communicate their work to the broader organization. It is hard to trust ML models when SMEs and data scientists don’t have a clear way to communicate.
2. Testing is not done prior to deployment, often only with training data
Algorithms always perform differently when they are deployed in a real-world environment. If not tested beyond training data, how can decision-makers trust the validity and accuracy of a model?
3. AI Testing methods can be ad hoc, based on the subjective expertise of an individual
There are few codified best practices for testing an algorithm. If, when, and how they are tested is determined by the organization or even an individual data science team. The lack of clear testing guidelines undermines the widespread social adoption of ML solutions.
4. Handoffs of models between data scientists and DevSecOps engineers are frequently messy, with low accountability
This slows down the model development pipeline and creates a disconnect on the road to AI technology deployment.
Bridging the gaps with independent testing and validation
The business edge
Organizations cannot afford to deploy models to production without being assured of their robustness. While the global ML market was sized at $15 billion in 2021—and is expected to reach $210 billion by 2029—research shows that only one in 10 data science projects ever make it to deployment. Lack of trust and confidence, scaling challenges, and uncertainty surrounding robustness are some of the obstacles that end up posing a serious business risk.
Independent TEVV provides organizations with a business edge, as users gain more trust in the system’s efficacy and robustness, ultimately improving the speed and percentage of model deployments. To some, the implementation of an independent testing process might sound like an additional step being inserted into the pipeline, or a barrier to rapid deployments. In reality, it is the opposite.
Your solution for trustable AI
VESPR Validate, CalypsoAI’s solution for independent testing and validation, can accelerate MLOps success by using independent testing and validation. VESPR Validate tests models against real-world stressors and risks, identifying the need to retrain models both before and after deployment.
Through this solution, MLOps teams and stakeholders can see AI deployed into production faster than ever before, by identifying vulnerabilities that training data and in-house testing would not be able to flag. Independent testing provides a distinct advantage both in business and strategy, severely mitigating the risks associated with deploying unreliable AI projects.
Contact us and accelerate MLOps success
Click here to connect with the CalypsoAI team and learn more about our platform for independent testing and validation.
