Training machine learning model for production use is a hectic and time-consuming process. With MLOps, this narrative is changing. MLOps, a descendent of DevOps, provides the automation and scalability required to develop, train, and continuously deliver modern agile machine learning applications.Machine learning lifecycle management tools are important to implement DevOps practices in your machine learning environment. Using popular, efficient open-source tools such as those mentioned in this article is an excellent start to your machine learning MLOps journey.

In this special guest feature, Ronny Lehmann, CTO at Loom Systems, discusses how digital transformation has created challenges and benefits for DevOps and the constant pressure of picking up the pace in the endless ocean of complexity can often seem impossible. With machine learning and AI, the fundamental necessity to ensuring a high-quality customer experience can become more of a reality. Ronny began his career with 8 years in the IDF's elite 8200 unit leading R&D teams in cyber and algorithms projects and implementations. Prior to joining Loom Systems, Ronny was VP of R&D at BioCatch, a leading provider of behavioral biometric, authentication and malware detection solutions through machine learning and signal processing. As the IT industry struggles to elevate performance, companies are looking to DevOps to deliver on the promise of a newly efficient process that includes frequent release cycles to feed the higher demanding consumer.