As a seasoned Data Scientist and recently appointed manager overseeing a team of ten Data Scientists, I'm transitioning my focus towards addressing challenges at the team level, aiming to foster smoother operations within our mid-sized company. I'm eager to engage with individuals who have encountered similar obstacles and successfully navigated them.
Here's a breakdown of the specific pain points we're grappling with:
Fragmented Deployment Procedures: Our current deployment and integration processes across different business units lack cohesion, leading to delays in deploying models to production. We often find ourselves relying on only a couple of individuals proficient in deployment in each business unit, which limits scalability and efficiency.
Lengthy Model Updates: Implementing changes to models in production is proving to be a time-consuming endeavor. Due to complex integration with other existing software, we're experiencing bottlenecks in the update process, hindering our ability to swiftly adapt to evolving requirements and insights.
Lack of Standardization: The absence of standardized practices for integrating models exacerbates the learning curve for Software Engineers across various business units. Each team adopts disparate approaches, further complicating cross-unit collaboration and knowledge transfer.
Data Fragmentation: The ad-hoc design of our models has resulted in fragmented data storage practices. As a consequence, new team members often struggle to locate relevant data or understand the integration process, impeding their onboarding and productivity.
Perception Challenges: There's a prevailing perception among teams is they want to use off-the-shelf tools but sometimes those tools provides inaccurate insights, casting doubt on the reliability of our analyses. This skepticism undermines efforts to foster collaboration and establish trust with other departments, hindering our collective progress.
Please share which issue you have faced/facing it and how you are addressing it ?