FAQs

Our MLOps approach focuses on automating the machine learning lifecycle, from data preparation to model deployment and monitoring. We implement versioning for both data and models, automated testing, and continuous monitoring of model performance in production.

We use industry-standard tools including GitLab, Jenkins, Docker, Kubernetes, TensorFlow Extended (TFX), MLflow, and Kubeflow. However, we can adapt to your existing tool stack or recommend solutions based on your specific needs. You can find more info about this on our Techstack page.

Our RLHF process involves creating structured feedback loops with domain experts, implementing reward modeling, and fine-tuning models based on human preferences. We provide tools and interfaces for efficient feedback collection and integration.

We implement comprehensive bias detection and mitigation strategies throughout the data preparation process. This includes diverse data sourcing, regular bias audits, and transparent reporting of model performance across different demographic groups.

Yes, modern no-code platforms can handle sophisticated business logic and workflows. We help design scalable architectures that combine no-code solutions with traditional development where necessary.

We implement best practices for database design, use efficient API integrations, and set up proper caching mechanisms. When needed, we create hybrid solutions that combine no-code platforms with custom code for optimal performance.

We establish clear KPIs before implementation, including time saved, error reduction, cost savings, and employee satisfaction. We provide regular reports on these metrics and continuously optimize the automated processes.

We follow a comprehensive change management approach that includes stakeholder analysis, training programs, documentation, and post-implementation support. We ensure smooth transition through phased implementation and regular feedback collection.