Despite the growing demand for automated machine learning workflows, an integrated body of knowledge for MLOps remains elusive. The over 60 available systems make it challenging for companies to compare them, according to Arxiv. Such fragmentation complicates decision-making for organizations seeking efficiency in their ML operations.
MLOps aims to standardize and automate the ML lifecycle, but its extensive scope and diverse challenges prevent a unified understanding and clear comparison of available solutions. This tension between aspiration and reality creates significant hurdles for implementers.
Companies seeking to implement MLOps solutions will likely face significant hurdles in selecting and integrating tools, potentially leading to increased complexity rather than streamlined operations, until clearer standards emerge.
What is MLOps, and Why Does it Matter?
MLOps is a broad, evolving discipline focused on standardizing and optimizing the entire machine learning lifecycle, from research to deployment. A multivocal literature review of MLOps practices, challenges and open issues analyzed 150 peer-reviewed and 48 grey literature sources. This extensive research sought to conceptualize MLOps and identify best practices, adoption challenges, and solutions.
The management of machine learning lifecycle artifacts is a key focus for MLOps, according to a survey that addresses specific research questions related to this topic, published in management of machine learning lifecycle artifacts: a survey - arxiv. MLOps aims to bring the principles of DevOps to machine learning, improving collaboration and automation across development, deployment, and operations phases. This approach helps manage the complexity inherent in ML model development and maintenance.
Navigating the Expansive MLOps Tool Options
The MLOps tool options are rich with diverse systems, each aiming to manage various aspects of ML lifecycle artifacts. An overview of systems and platforms supports the management of these artifacts. This wide array of tools reflects the many stages of an ML model's life, from initial data processing to model training, evaluation, and deployment.
