Comparative Evaluation of Machine Learning Development Lifecycle Tools

The ML development lifecycle is the SDLC equivalent of Machine Learning. While the ML code is at the core of a real-world ML production system, it frequently represents only 5% or less of the system's entire code. This study examines and contrasts the technologies utilised in the machine learning development lifecycle and focuses largely on the distinction between ML programming and ML development. According to Forrester Research, AI adoption is ramping up. 63% of business technology decision makers are implementing, have implemented, or are expanding use of AI. The main motivation behind this research is that the majority of the organizations do not have ML/AI solutions that have gone beyond the PoC / PoV stage, ML code in Jupyter notebooks cannot be distributed, and AI/ML solution deployment at scale is a challenge. Machine learning services are evolving at a dizzying rate, opening up a variety of opportunities for on-field applications, especially for brands and businesses with the infrastructure and resources required to integrate ML into their operational structures as a decision-making fulcrum. Nearly 65% of stock market swings may be predicted by Azure Machine Learning. By incorporating ML into its operational framework, Amazon has successfully decreased the “click-to-ship” time by 225%. Breast cancer can be identified with 89% accuracy using Google's Deep Learning. Thus, these commercial tools for ML life cycle support have been compared and a conclusion about which tool is most suitable is drawn.