Open source is a key enabler for enterprise data science, both in terms of the growing ecosystem of open-source tools and the expanding number of complementary enterprise data science platforms that incorporate and build on open source languages and tools. The challenge is identifying which of those tools is relevant and valuable to your business. Assessing the maturity of these projects, grappling with any licensing issues and making sure your team has the correct skillset to use them are challenges that many companies are now facing.
A proof of concept (POC) is a popular way for businesses to evaluate the viability of a system, product, or service to ensure it meets specific needs or sets of predefined requirements. What does running a POC mean in practice specifically for data science? When it comes to the evaluation of data science solutions, POCs should prove not just that a solution solves one particular, specific problem, but that a system will provide widespread value to the company: that it’s capable of bringing a data-driven perspective to a range of the business’s strategic objectives.
Deep learning troubleshooting and debugging is really hard. It’s difficult to tell if you have a bug because there are lots of possible sources for the same degradation in performance. Furthermore, the results can be sensitive to small changes in hyper-parameters and dataset makeup. To train bug-free Deep Learning models, we really need to treat building them as an iterative process. To make this process easier and catch errors as early as possible, this article suggests steps you can follow.