Rethinking AI Deployment: From Post-Deployment Monitoring to Efficient Compilation with Tensor Language Models

For years, organizations have relied on post-deployment monitoring to manage the inevitable degradation of machine learning (ML) models. These models, trained on specific datasets, are inherently prone to performance decline over time due to changes in real-world conditions1),2). This has led to a shift towards more nuanced approaches, such as capability-based monitoring—ensuring that AI systems maintain their intended functions within dynamic environments.

The degradation of ML models post-deployment is a given. Models will always degrade because populations and outcome distributions inevitably change compared to the data they were trained on1),2). This has led, sensibly, to model-specific post-deployment monitoring for expected degradation.

Traditionally, AI implementations in healthcare have been focused on bespoke ML models, each trained for a single task using datasets from defined, bounded populations (Fig. 1, ML Paradigm). These models assume that training and test data come from the same underlying distributions. When this assumption is violated, overfitting occurs, leading to degraded performance on new datasets1),2).

Because of this, performance degradation of ML models post-deployment is a given: models will always degrade because populations and outcome distributions inevitably change compared to the training data.

Vendors, and regulators should adopt capability-based frameworks to ensure safe, equitable, and sustainable deployment of generalist AI. These frameworks can help maintain model integrity and align with changing environments. The key takeaway is that post-deployment monitoring must evolve beyond traditional methods to reflect real-world complexities.

Simultaneously, advancements in tensor compilation using generative scheduling are revolutionizing how we deploy complex AI models like those used in healthcare and other critical domains. Traditional optimization techniques have included search-based auto-tuning, heuristic and hybrid compiler approaches, and learning-based scheduling models. More recently, generative large language models (LLMs) have emerged as powerful tools in this landscape.

The optimisation of tensor programmes has taken three main directions, including search-based auto-tuning, heuristic and hybrid compiler techniques, and learning-based scheduling models. Generative LLMs, however, can offer a more efficient approach by integrating operator structure, hardware context, and scheduling decisions within an autoregressive symbolic model.

The concept of TLM is rooted in the creation of a tensor language that encodes tensor programs succinctly and efficiently. This new paradigm aims to combine the efficiency benefits of heuristic-based methods with the performance guarantees of search-based approaches, all while avoiding runtime exploration dependencies1).

Our idea is to have a Tensor-based compiler named TLM, which incorporates large language models to substitute random sampling with an informed decision generation. The suggested method includes a training pipeline that uses offline pre-conceptualisation and then supervised fine-tuning on high-performance tensor programs.

The practical outcomes of using TLM are staggering. Compilations can be up to 61x faster than Ansor and MetaSchedule, and 2.25x faster than Roller (compiling 1x slower) at the same compile time. Extensive testing across ResNet, BERT, GPT-2, and LLAMA workloads has demonstrated broad scalability for both vision and NLP tasks.

Compilation is up to 61x faster than Ansor and MetaSchedule, and 2.25X faster than Roller (compiling 1x slower) at the same compile time. The suggested method includes a training pipeline that uses offline pre-conceptualisation and then supervised fine-tuning on high-performance tensor programs.

For organizations dealing with complex AI deployments, TLM offers a transformative solution. By combining robust oversight through capability-based monitoring and efficient deployment via generative scheduling, you can ensure your AI systems remain not only safe but also highly effective in dynamic environments.

By embracing both capability-based monitoring and tensor compilation using generative scheduling, organizations can achieve robust and scalable AI deployments that meet the needs of today's rapidly changing landscapes.