Neptune's blog post provides insights into building an experiment tracking tool, emphasizing the importance of such a tool for MLOps engineers to enhance data scientists' workflows by managing complex experiments. The article draws on experiences from Neptune engineers to outline the essential components and architecture needed for an effective experiment tracker, including backend, frontend, and client library layers. It highlights the functional and non-functional requirements necessary for tracking experiment metadata, ensuring reproducibility, and providing explainability while also addressing the decision-making process between building or buying such a tool. The post stresses the significance of backend architecture in managing user sessions and suggests considerations like modularity and cost-effectiveness when developing an experiment tracking tool. Ultimately, it concludes that while building a custom solution might be necessary for unique requirements, off-the-shelf options like Neptune might be more efficient in terms of time and resources.