MLOps defines practices that unify ML development, streamline continuous delivery of models, and enable collaboration between teams
What is MLOps?
MLOps, as its name indicates, is the combination of terms - machine learning and operations. It brings a set of practices to deploy and maintain ML models in production reliably and efficiently. These practices help unify ML system development, streamline and standardize the continuous delivery of performant models, and enable effective collaboration between ML and other teams.
MLOps implementation helps businesses with accelerated ML deployments and better compliance with regulatory requirements. It supports cross-functional organizational teams that include C-Suite leaders, ML experts, DevOps team, operational team, and risk-compliance professionals.
Organizations prefer MLOps for the following benefits:
- A sophisticated way to scale ML systems
- A better way to manage, monitor, deploy and scale ML models in a production environment
- Effective risk and compliance management for ML projects
- Ensure better collaboration between teams
- Reproducible workflows and models
- Helps plan competent AI strategy
Challenges Addressed By MLOps
Productionalizing ML models is a manual and error-prone process. Data scientists build models using preferred frameworks and hand off these to a software team for implementation. This process becomes error-prone and complicated. Automated tools and well-defined MLOps practices help keep work in tandem.
MLOps offers the following benefits:
AI strategy alignment
MLOps helps reflect changing business goals with AI strategy. Aligning AI strategy with new business objectives requires considering performance standards, data dependencies, and AI governance.
Better risk assessment
MLOps establishes practices to evaluate potential project failure risks. It helps ML professionals to overcome the black-box AI challenge and take proactive steps.
The communication gap and a lack of the right talent become two main challenges in any AI journey. ML lifecycle includes several stages that require collaboration and hand-offs across teams from research to deployment. MLOps drives automated collaboration practices, experimentation, and synchronous working among teams.
Practicing MLOps For AI Success
Machine learning is transforming businesses more than ever before. However, reaping the benefits of ML capabilities is not easy without appropriate toolsets, talent, and MLOps practices. We summarize the following best MLOps practices in this section:
- MLOps implementation requires the selection of tools to complete the ML stack. It can be a complete MLOps platform or an open-source library complementing in-house solutions.
- Using feature stores that allow sharable and reusable features across teams
- Create reviewable and deployable code using open-source libraries and automated ML tools
- Track model lineage, model versions, and transitions through their lifecycle
- Optimally handle the frequency of model refresh, inference request times, and similar production-specifics in the validation phase. Automate the pre-production pipeline using CI/CD tools like repos and orchestrators
- Automate permissions and cluster creation to operationalize models
- Set precise monitoring practices using ML monitoring tools. This helps take corrective actions for model degradation, drift, and other quality issues
- Establish appropriate collaboration practices among teams