Stop trying and start deploying with the power of Sigmoidal.
Evolved with us as the company grew, and we learned from our experiences. Sigmoidal goes far beyond traditional cooperation – leverage creative executions to your advantage.
Sigmoidal focus solely on Data-Centric AI & ML, utilizing state-of-the-art patterns & technologies, as part of a cohesive strategy to deliver the most impactful solution.
With Design Authority comes Sigmoidal deep understanding of machine learning models, combing both data science research and engineering methodology. We adapt our refined infrastructure comprised of Machine Learning patterns, uniform data structures, and Software Architecture to match your project requirements and lead resources in the right direction.
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.
Sigmoidal employs its proprietary AI Production-focused technique, implementing our solutions in the earliest phase, effectively accelerating the entire project by 30%.
As 90 percent of machine learning models never make it into production, we revolutionize that by focusing solely on the product and devising strategies meant for reproducibility. Sigmoidal pinpoints the most important datasets, versioning machine learning models, establishing efficient structures in the earliest phase.
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".
Sigmoidal constantly transfers the best global practices, and Lessons Learned, providing you with skills & technical novelty, maximizing the effectiveness of each cooperation.
Our specialists complement your staff, overcoming any potential skill gaps. In the earlies phase, we pinpoint the best communication channels, making sure to convey technological insights – sharing our expertise and experience.
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.
Sigmoidal deep understanding of machine learning models allows us to channel resources in the right direction. We combine both data science, and engineering – taking your solution into production.
We outline a transparent plan of action, working closely in cross-functional teams, highlighting any possible challenges, setting up the right infrastructure at the beginning. We leverage these methods, effectively accelerating the project since its inception as we strive for sophisticated technological 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.