Machine Learning
From predictive models to LLM integration, CorrDyn builds ML systems that deliver measurable business outcomes.
CorrDyn builds machine learning systems that solve real business problems. We are not interested in building models for the sake of building models — every project starts with a clear business case and ends with a measurable outcome.
From Prototype to Production
The gap between a working Jupyter notebook and a production ML system is enormous. CorrDyn bridges that gap with engineering rigor: automated training pipelines, model versioning, A/B testing frameworks, and monitoring systems that alert you when model performance degrades. We build ML systems that your team can operate, not black boxes that only we can maintain.
Biotech & Life Sciences Specialization
Our team has deep domain expertise in biotech and life sciences ML applications: Bayesian optimization for experimental design, NLP for literature mining and patent analysis, computer vision for microscopy and imaging, and predictive models for clinical trial outcomes. We understand both the data science and the domain science.
LLM Integration & Strategy
Large language models are powerful tools, but they require careful integration. We help organizations evaluate LLM use cases against our proven framework, build RAG (retrieval-augmented generation) systems that ground LLM outputs in your proprietary data, and implement evaluation pipelines that ensure output quality meets your standards.
Technologies We Use
Frequently Asked Questions
- Do we need a large dataset to benefit from machine learning?
- Not necessarily. Many high-value ML applications work well with modest datasets — especially in biotech where each data point is expensive to generate. Techniques like transfer learning, Bayesian optimization, and active learning are specifically designed for low-data regimes.
- How do you handle ML model deployment and monitoring?
- We build ML systems for production, not just prototypes. Every model we deploy includes automated monitoring for data drift, prediction quality, and infrastructure health. We use MLOps tools like MLflow and cloud-native services to ensure models stay accurate and reliable over time.
- What is your approach to LLM and generative AI projects?
- We take a pragmatic approach: start with the business problem, evaluate whether an LLM is actually the right tool, and if so, build thin integration layers that minimize lock-in. We specialize in RAG architectures, fine-tuning, and evaluation frameworks that ensure LLM outputs meet your quality standards.