MLOps

Machine learning operations (MLOps) involve applying development operations (DevOps) principles to artificial intelligence (AI)-infused applications. It focuses on making AI systems reliable, scalable, and automated. This guide highlights how Domino uniquely supports MLOps to help organizations deliver AI solutions effectively.

How Domino enables MLOps

MLOps is a systematic approach to integrating machine learning (ML) models into enterprise application development. It encompasses the deployment, monitoring, and maintenance of ML models in production environments. The goal of MLOps is to bridge the gap between the development of ML models and their operational deployment, enabling faster delivery and more robust solutions. Domino’s platform offers comprehensive support for MLOps through several key capabilities:

Seamless collaboration and version control: Domino provides tools for seamless collaboration among data scientists, engineers, and business analysts. The platform supports version control for both code and data, ensuring experiments are reproducible and that teams can revert to or compare different versions effortlessly.
Scalable experimentation and model management: Users can run multiple experiments simultaneously, leveraging Domino’s scalable infrastructure. The platform allows for easy comparison of models and manages the lifecycle of each model from development to deployment.
Continuous monitoring and deployment: Domino facilitates continuous monitoring of models in production, detecting issues like data drift or model degradation early on. The platform integrates with existing CI/CD pipelines, enabling continuous deployment and ensuring models are updated without downtime or loss of service quality.
Automated retraining and rollback: Automated workflows in Domino can trigger retraining of models based on performance metrics or data changes, with options to roll back to previous versions if the new model underperforms. This ensures that the production environment remains stable and reliable.
Transparent and governable AI: Domino’s platform emphasizes transparency and governance, with features designed to explain model decisions and manage compliance with regulatory requirements. This is crucial for industries where understanding model behavior is as important as the model’s predictions.
Customizable environments: Users can create and manage customizable environments in Domino, which helps maintain consistency across the development and production stages. This reduces conflicts and dependencies, streamlining the transition from model development to deployment.

MLOps best practices

Incorporate the following set of best practices to ensure efficient and effective management of MLOps:

Use version control for code, data, and experimentation outputs: Ensure all components of your ML projects, including code, datasets, and models, are version-controlled. This approach guarantees reproducibility, facilitates collaboration, and saves time and resources that would otherwise be spent recreating experiments. Domino integrates robust version control systems that automatically track changes in code, data, and models. The platform provides a unified environment where all assets are versioned, helping teams to revert, compare, and build upon previous work seamlessly.
Use multiple environments: Maintain separate environments for development, staging, and production to isolate changes and reduce the risk of introducing errors into production systems. Domino facilitates the creation and management of multiple Workspaces, each corresponding to different stages of the ML lifecycle. This separation enhances security and control, as access permissions can be managed per environment, ensuring that only authorized changes are deployed.
Manage your infrastructure and configurations as code: Utilize Infrastructure as Code (IaC) to manage and provision computing resources, ensuring consistency across environments and reducing manual configuration errors. Domino supports IaC practices by allowing users to define and execute ML pipelines and environments programmatically. These definitions can be stored as code and versioned, enabling easy replication of environments and experiments.
Track and manage ML experiments: Keep detailed records of experiments, including parameters, metrics, and outcomes, to analyze performance over time and replicate successful experiments. Domino offers comprehensive tracking features that automatically capture and store all experiment details, making it easy for teams to monitor progress and evaluate the impact of different variables on model performance.
Test code, validate data integrity, and ensure model quality: Test your code regularly for data handling and feature extraction accuracy, and validate the integrity of your data to ensure high-quality model outputs. Domino integrates testing and validation tools directly into the workflow, allowing users to automate tests for data integrity and model accuracy as part of the experiment process.
Continuous integration and delivery for ML: Implement continuous integration and delivery pipelines to automate testing and deployment of ML models, ensuring robustness and rapid iteration. Domino supports CI/CD for ML with automated pipelines that manage the entire lifecycle from data processing to model training and deployment. This automation ensures that only models that meet predefined quality thresholds are deployed to production.
Monitor services, models, and data: It is best practice to continuously monitor deployed models and underlying data for performance, compliance, and operational health to detect and respond to issues such as data drift or model degradation. Domino provides monitoring tools that track the performance and health of both models and their data inputs in real-time. The platform can trigger alerts and automate retraining processes based on custom thresholds, helping maintain model accuracy and reliability.

Domino best practices for MLOps

Domino supports the following best practices for MLOps:

People

Team collaboration and role flexibility: Domino encourages the formation of project teams that leverage specialist and domain knowledge effectively. The platform facilitates role-based access controls that allow for granular permissions, ensuring that team members can fulfill multiple roles based on the project’s needs.
Lifecycle and agile methodology: Domino supports standardized project lifecycles, integrating agile methodologies to enhance responsiveness and flexibility. This helps teams to adapt quickly to new insights and iterate on ML models efficiently.
Balanced teams: Domino’s environment is conducive to supporting all stages of MLOps, including exploration, development, and operational deployment, allowing teams to function effectively across the full spectrum of tasks.

Process

Standardization of code and processes: Domino promotes the use of standardized code templates, which accelerates project setup and ensures consistency across multiple projects. These templates can be used to quickly start new projects or to bring new team members up to speed.
Version control and experiment management: The platform provides comprehensive version control for code, data, and models, ensuring reproducibility and traceability. Domino’s experiment-tracking capabilities enhance collaboration and allow teams to monitor the progress and efficacy of their models over time.
Continuous integration and delivery (CI/CD): Domino integrates CI/CD practices into ML workflows, automating the testing and deployment of models. This ensures that models are robust and can be updated in production without downtime.
Strategic experimentation: Domino encourages a strategic approach to managing experiments, using a champion/challenger model where new models are rigorously tested against baseline models to ensure they deliver expected improvements before full-scale deployment.

Technology

Automation and orchestration: Domino automates various stages of the ML pipeline, from data processing to model training and deployment. This reduces manual effort and allows data scientists to focus on higher-value tasks.
Monitoring and retraining: It is crucial to continuously monitor model performance in MLOps. Domino provides tools to monitor model health, performance, and drift, and can trigger retraining workflows automatically when certain thresholds are met.
Scalable infrastructure: Domino offers scalable infrastructure options that can grow with your project needs. Whether deploying lightweight models or conducting massive-scale ML experiments, Domino ensures that your infrastructure can handle it efficiently.
Event-driven automation: Domino supports event-based triggers for ML workflows, allowing teams to automate responses to model performance issues or operational anomalies, ensuring high availability and performance continuity.

Enhanced debugging and deployment

Testing and validation: Domino allows for rigorous testing and integration of inference pipelines, which helps with debugging and validating model performance before deployment.
Staging environments: The platform supports the use of staging environments where models can be tested in conditions that closely mimic the production environment, thereby minimizing deployment risks.

Domino as an "AI factory" for MLOps

An AI factory refers to a system of standardized processes and artifacts designed to streamline the development and deployment of ML projects at scale. It allows an organization to manage multiple ML projects efficiently by optimizing team setups, recommending practices, architectural patterns, and reusable templates tailored to specific business requirements. The primary aim of an AI factory is to achieve high-quality, reliable, and maintainable ML solutions that can scale as the number of use cases increases, often from tens to thousands.

Domino provides a comprehensive platform that supports the establishment and operation of an AI factory by offering scalable solutions, enhanced collaboration tools, advanced monitoring and automation, and specialist support and integration. It provides project templates, version control, shared workspaces, role-based access control, automated workflows, and integration with enterprise systems to optimize ML operations.

Scalable and repeatable solutions

Project templates and architectural standards: Domino offers a range of project templates and supports the development of custom templates that align with organizational standards. These templates ensure that new projects start with a proven baseline, reducing setup time and increasing the likelihood of success.
Version control and experiment management: Domino’s robust version control capabilities ensure that all aspects of an ML project, including data, code, and models, are tracked. This enables reproducibility and helps maintain a clear lineage of project artifacts, which is critical for scaling operations.

Enhanced collaboration tools

Shared workspaces and repositories: Domino promotes collaboration by providing shared workspaces where teams can easily access and contribute to ongoing projects. This shared environment helps harness collective expertise and accelerates the iteration cycle.
Role-based access control: Access controls in Domino are designed to ensure that team members have appropriate access to projects and tools, aligning with their roles and responsibilities. This helps maintain security and governance while promoting efficiency.

Advanced monitoring and automation

Automated workflows: Domino automates key aspects of the ML workflow, including data processing, model training, and deployment. Automation not only speeds up the development process but also ensures consistency and reliability in operations.
Monitoring and retraining: Continuous monitoring of deployed models is facilitated in Domino, with features that detect performance degradation or data drift. The platform can automatically trigger retraining processes, ensuring that models remain accurate and relevant over time.

Specialist support and integration

Support for ML engineering roles: Domino is designed to support specialized roles, such as ML engineers, by providing tools that are essential to develop and maintain production-grade ML systems, including advanced pipeline management and deployment capabilities.
Integration with enterprise systems: Domino seamlessly integrates with existing enterprise systems, allowing data to flow securely and efficiently between systems and enabling ML models to leverage and enrich the broader information technology ecosystem.

Next steps

MLOps vs Devops.