Five Challenges of Machine Learning DevOps - DevOps.com

As organizations add machine learning (ML) to their workflows, it's tempting to try to squeeze model creation and deployment into the existing software development lifecycle (SDLC). However, ML is fundamentally different than traditional applications, and it's important to account for that in a new, unique process called the machine learning development lifecycle. We have identified five challenges every organization should keep in mind as they begin to support ML development. Machine learning is successful when the right tool is selected for a given job. Depending on the use case, a data scientist might choose Python, R, Scala or another language to build one model, and another language for a second model.