Stop trying and start deploying with the power of Sigmoidal.
Enterprise Machine Learning patterns
Architecture for reproducible research
Architecture for CI/CD
Data-Centric AI approach
Software design compliant with UML 2.5
Best practices and standards
- Sigmoidal developed a proprietary AI engine for real-time tracking and analysis of users’ sentiment across social media, enabling our client to make better-informed decisions concerning investment and asset allocation across their portfolio.
- For a client from a fintech industry Sigmoidal established a sophisticated benchmark to run a ML model against now and in the future. In combination with diligent version control, Sigmoidals’ data scientists designed a reproducible model running the entire research path, obtaining identical results.
Minimizing Total Cost of Ownership
Proof of Value, not Proof of Concept
Metrics definition and monitoring
- We don’t verify concepts, we verify values. Our data scientists in R&D project for one of the largest pharmaceutical companies used such a technical approach and operating model for a data science team to quickly explore the potential of machine learning in a client's area and implement the solution on production, and then iteratively extend and improve it. Here we used our proprietary architecture, which allowed us to deploy PoV to production in less than 5 days, without wasting time rewriting the solution into "production code".
Senior staff trainings
Early detection of potential pitfalls
Building communities of practice
- Sigmoidal goal is to foster the growth of your product by giving you access to hands-on data scientists with a strong engineering background. We provide you with feedback and technological insights - a valuable component in improving the collaboration, as we strive to build teams capable of developing innovative solutions.
Data Privacy and Security
Model as an scalable container
Data bias monitoring
Accuracy and A/B testing before release
- Sigmoidal team acutely versions different parts of a machine learning model to obtain the information on data, hyperparameters, parameters, algorithm choice, and architecture. We strive to achieve the optimal combination of those parameters, finding the right balance and allowing us to develop even the most complex machine learning projects.
- Sigmoidal considers machine learning models as a scalable container. It allows us to further scale the model allowing effortless and almost seamless continuous accessibility and improvement.