Docker Essentials: Streamlining Multi-Service Application Orchestra

Author(s): Afaque Umer

Originally published on Towards AI.

Unlocking the Power of Compose for Seamless Machine Learning Workflows
Photo by Larisa Birta on Unsplash

In the ever-evolving landscape of machine learning experimentation and application development, navigating the coordination of diverse system components poses a significant logistical challenge. Whether overseeing user interfaces, managing API endpoints, or handling databases, developers consistently grapple with the complexities of deploying and seamlessly connecting these integral elements. Crucially, this challenge transcends the realm of machine learning, extending its reach into diverse environments where interconnected services create distinct universes of dependencies resembling their own solar systems.

Now, honing our focus on the data science domain, envision a scenario where an application is actively used, and our objective is to monitor experiments as users engage with it. A dedicated team of data scientists, driven by innovative insights, works tirelessly to continuously evolve models and conduct experiments, striving to enhance model capabilities. To effectively manage and track these experiments and evaluations, the need for a shared database becomes apparent — an environment where all logs are recorded and visualized in a centralized manner.

Considering the current challenge, one potential solution involves building individual Docker images for each essential component — Streamlit UI, FastAPI server, and MLflow server. While this approach allows for the separation of concerns, it introduces a cumbersome… Read the full blog for free on Medium.

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Published via Towards AI