With the expanding business applications of LLMs, MLOps jobs are quickly become critical to the success of companies. However, transitioning ML projects from theory to production is fraught with challenges. This is where Machine Learning Operations comes into play. MLOps is a comprehensive approach that includes best practices, conceptual frameworks, and a unique development culture aimed at operationalizing ML projects. The term, however, has remained somewhat ambiguous, posing challenges for both researchers and professionals in the field.

To demystify MLOps, a mixed-method research approach, including literature review, tool analysis, and expert interviews, was conducted. The goal was to provide a clearer understanding of MLOps by detailing its essential principles, components, roles, and workflows, thereby offering a comprehensive definition and addressing the challenges in the field. This research is particularly valuable for ML practitioners aiming to automate and operationalize their ML products using specific technologies.

The Challenge of Real-World ML Applications

Despite the potential of ML in driving innovation, efficiency, and sustainability in business, many ML projects fall short in real-world applications. The research community has traditionally focused more on ML model development rather than on creating production-ready ML products and coordinating complex ML systems. This has led to a substantial number of ML projects failing to reach the production stage.

The paper highlights the need for MLOps to address issues such as manual management of ML workflows, which often lead to operational challenges. The goal is to automate and operationalize these processes to transition more ML concepts into production. MLOps is scrutinized from a holistic viewpoint, encompassing components, principles, roles, and architectures involved in ML systems design. Despite existing research on specific aspects of MLOps, a comprehensive understanding and generalization of the concept are lacking, leading to potential miscommunications and system errors.

Roles in MLOps Jobs

A research paper (cited below) identifies nine key principles essential for implementing MLOps effectively: CI/CD automation, workflow orchestration, reproducibility, versioning, collaboration, continuous ML training & evaluation, ML metadata tracking/logging, continuous monitoring, and feedback loops. They also described the technical components and MLOps jobs roles vital for business machine learning implementation, emphasizing the interdisciplinary nature of the field.

• Business Stakeholder (Product Owner, Project Manager): This role involves defining business goals for ML projects, managing ROI communications, and ensuring alignment with business objectives.
• Solution Architect (IT Architect): Responsible for designing the overall ML system architecture, this role selects and evaluates technologies for MLOps implementation, ensuring they meet project requirements.
• Data Scientist (ML Specialist, ML Developer): They translate business problems into ML challenges, focusing on model engineering, algorithm selection, and hyperparameter optimization.
• Data Engineer (DataOps Engineer): This role develops and manages data and feature engineering pipelines, ensuring efficient data ingestion into databases and feature store systems.
• Software Engineer: Software engineers apply design patterns, coding guidelines, and best practices to develop the raw ML problem into a well-engineered product.
• DevOps Engineer: Bridging the gap between development and operations, DevOps engineers ensure proper CI/CD automation, ML workflow orchestration, model deployment, and monitoring.
• ML Engineer/MLOps Engineer: A cross-domain role that involves skills from data science, engineering, software development, and backend engineering. They build and operate the ML infrastructure, manage automated ML workflow pipelines, and monitor both the model and the ML infrastructure.

These roles represent the collaborative and interdisciplinary nature of MLOps jobs, highlighting the need for a cohesive approach to ML project management and execution. As LLMs become more viable in the private sector, MLOps will be a critical enabler of the technology.

Reference

• Kreuzberger, D., Kühl, N., & Hirschl, S. (Year of Publication). Machine Learning Operations (MLOps): Overview, Definition, and Architecture. Institution/Organization. IBM, Ehningen, Germany & University of Bayreuth, Bayreuth, Germany.