ML/AI Platform
Working in concert with the infrastructure and data platform, the ML platform includes tooling and integrations for developing machine learning and artificial intelligence features.
All the first class features in the data and infrastructure platforms are leveraged to maximum effect in the ML & AI platform: local development and consistent data access enable users to confidently and fearlessly iterate on models; the observability best practices built into the infrastructure platform are enriched with ML and AI specific model performance metrics and logging; the business performance metrics developed in the data platform can be directly linked to model performance data; and the flexibility of multiple deployment, rollback and hotfix strategies ensure only the most reliable models make it to and stay in production.
While the ML & AI platform exploits all the features of the other platforms, ML development makes some unique demands of its own. These demands tend to be around dynamic compute, data environments for experimentation and retraining, and strategies for coping with model performance issues in production.
The unique questions for ML & AI platforms that are not covered in other platforms are:
Data Preparation & Feature Engineering:
- how can I test data quality for ML model training?
- what tools are available for feature engineering and selection?
- how does feature engineering and selection get memorialized?
Model Development & Experimentation:
- how can I manage my experiments?
- what compute resources (GPUs, analytics stores, clusters) are available and how do I access them?
- how can I ensure reproducibility of my ML experiments?
- what frameworks and libraries are supported for ML/AI development?
- what options do I have for distributed training of large models?
Model Versioning & Deployment:
- how does versioning, storing and deploying a machine learning model differ from other software artifacts?
- how do I best integrate ML models into existing applications and workflows?
Monitoring & Maintenance
- how can I monitor model inputs, outputs and metrics for drift?
- how can I automatically trigger model retraining?
- how do I ensure models perform as expected in production?
- how do I handle cases when models fail to perform as expected in production?
Explainability & Interpretability:
- what tools are available for explainable AI and model interpretability?
LLMs & Prompt Engineering:
- how do I easily iterate, version, compare and measure the performance of prompts for LLM features?
- how can non-technical members collaborate on prompts
- how do the data and ML platforms work together for RAG and fine-tuning?
AI and ML features are some of the most challenging to develop, deploy, and maintain. At Composable Platforms, we’ve designed our platforms to address the most potent organizational and technical challenges that often obstruct AI development. We’ve built upon traditional platforms, enhancing them with the extra capacity, accessibility, modularity, and data-centricity that AI and ML teams require. Our goal is to empower these teams to deploy their features as confidently and fearlessly as they would a well-tested piece of deterministic code. By providing this robust foundation, we enable organizations to innovate faster, reduce risks, cut through the hype, and deliver on the real promise of ML and AI technologies. With Composable Platforms, you can transform AI and ML from buzzwords into tangible, value-driving assets for your business.