Machine learning (ML) is transforming all business functions, and software development is no exception. There’s no doubt that the development of ML solutions is unique compared to that of other types of software, as it solves unsurmountable problems in every phase of the software development product lifecycle. One of the distinctive characteristics of ML software is that it is far more brittle than traditional software, because of its non-deterministic nature, and also due to it being highly sensitive to the characteristics of the trained data and tightly dependent on every other step.
The norm in the ML environment is “Change Anything Changes Everything.” While many engineers have deployed existing open source tools and integrated them together to create their own ML operational environment, but for some teams, the potential costs and complexity of this approach may not be a perfect fit. They seek a new category of products that provide an end-to-end ML operational environment. In an article for InformationWeek, Moshe Kranc, Chief Technology Officer, Ness Digital Engineering, shares some of the new products in this category that can massively streamline the process of creating and deploying ML algorithms with a few caveats. “ML is poised for explosive growth in the enterprise and ML workflow environment tools. It will be interesting to see how this product family matures in the coming months,” states Moshe.
Read the full article https://www.ness.com/machine-learning-workflow-new-product-category-born/