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Model Engineering
We harness the power of AI models to drive innovation, streamline operations, and unlock new revenue streams, enabling our clients to stay ahead in an increasingly competitive and regulated landscape.
Approach
At the forefront of our Model Engineering teams lies a deep expertise in Machine Learning, where we develop and deploy advanced algorithms to extract valuable insights from complex data. Our teams specialize in supervised, unsupervised, and reinforcement learning techniques, enabling us to tackle a wide range of challenges across various industries. Their mission is to drive and scale innovation and growth through advanced AI solutions.
We leverage state-of-the-art model architectures such as Large Language Models, Large Multimodal Models, or Vision-Language Models to build intelligent systems that can be fine-tuned for diverse applications or sectors. Combining multiple information sources such as text, images, audio, and video is essential to provide advanced human interaction and enable a seamless integration within corporate ecosystems, especially in industries where multimodal content plays a crucial role. Such models are designed to handle the complexity and ambiguity of real-world data, making them highly effective in a variety of business contexts.
Our Model Engineering teams work closely with clients to understand their unique challenges and requirements, ensuring that our solutions are tailored to their specific needs and aligned with their business objectives. What sets us apart is our ability to provide a cutting-edge fusion of advanced modeling technologies and traditional approaches to create highly customized solutions with the best performance-to-cost ratio.
Through our Model Operations expertise, we bridge the gap between data science and production, ensuring seamless collaboration, reproducibility, scalability, governance, and compliance in the end-to-end model lifecycle. We establish robust practices and implement tools that enable the efficient management, deployment, and monitoring of models throughout their lifecycle, encompassing training, versioning (for both data and models), testing, deployment, drift, monitoring, and retraining. For some workloads, we suggest decoupling infrastructure management from model deployment, using serverless model endpoints with on-demand scaling to streamline the deployment process and improve resource utilization.
Moreover, ethical considerations are paramount in model development and deployment. We advocate for transparent and explainable models. Our teams are dedicated to maintaining the highest levels of data privacy, security, and robustness, fostering trust and confidence in our solutions. By fostering a culture of responsible AI, we empower clients to build and deploy solutions that benefit society while minimizing risks and unintended consequences.
Our teams support clients with the highest privacy concerns through end-to-end computations on encrypted data without decryption ever on the server side (privacy-preserving training and inference scheme) using powerful but computationally intensive cryptographic techniques such as FHE.
Use cases
Our Model Engineering expertise drove the development of a sophisticated Network Forecasting Platform for an Energy Player, offering unparalleled accuracy and insights into network dynamics. We meticulously engineered short-term demand forecasting models to anticipate fluctuations in energy consumption, while our generation forecasting models predicted output from diverse energy sources. Furthermore, we integrated these models so that they provided a comprehensive view of the network, facilitating strategic decision-making. Through rigorous validation and optimization, we ensured the reliability and robustness of our models, empowering our client to optimize resource allocation, minimize downtime, and enhance overall operational efficiency in the energy sector.
Leveraging advanced mono- and multi-variate statistical techniques, our Model Engineering team crafted a sophisticated Customer Scoring System for an Insurance Player. Our models were not only accurate but also prioritized interpretability, ensuring that business users could grasp the rationale behind scoring outcomes. We meticulously orchestrated these models within DAG-type workflows, enabling seamless integration into existing business processes. By engineering models with a keen focus on interpretability and scalability, we empowered our client to enhance risk assessment, personalize customer interactions, and drive profitability in the competitive insurance market.
We engineered generic packages for machine learning models, streamlining the development process and accelerating time-to-market for new products. Embracing CI/CD practices and robust model versioning, we ensured the scalability and maintainability of the "Algo Factory" ecosystem. Through iterative refinement and optimization, we empowered our client to harness the power of machine learning for product development, customer engagement, and market expansion, driving innovation and competitive advantage in the cosmetics industry.