The Challenges: Bureaucracy hindered Collaboration

The business struggled with a tough challenge—siloed teams that hindered seamless collaboration. The Data Science and DevOps teams operated within the confines of their domains without the smooth interaction that's very much needed between these areas.

Aiming for an elusive, all-encompassing ML Engineer hire that united those areas could prove challenging, particularly for the multifaceted role of an ML engineer. Moreover, the teams found themselves in a constant state of flux, with managerial turnovers creating an additional layer of complexity.

The Solution: A Flexible Team

Enter our solution—a team marked by its flexibility and adaptability. We weren't just versed in both Data and DevOps; we could effortlessly pivot between them based on emerging requirements. Our team embodied a rare fusion of skills in Data, Machine Learning, and DevOps, effectively erasing the boundaries that had kept these functions apart.

Executing the Vision: A Commitment to Excellence

A commitment to good data practices marked our journey. We took models developed by the Data Science team and efficiently ushered them into production, emphasizing rigorous testing, automated deployment, and pipeline testing (creating reusable solutions transcending individual projects), and code management on GitHub.

We also ensure constant model performance monitoring, which you can learn more about here. Furthermore, the challenge of implementing reinforcement learning systems became an opportunity to identify and deploy the most effective recommender systems.

Flexibility was our cornerstone. If a skill set was unfamiliar, we embraced the learning curve, ensuring we could adapt to any requirement. Our efforts extended beyond our immediate responsibilities—we actively defined standards for inter-team interactions, simplifying the handoff of systems from one team to another.