Machine learning (ML) has become a critical part of many tech stacks and business operations. As organizations adopt ML, they need ways to manage the development, deployment, monitoring and updating of ML models. This is where Machine Learning Ops – commonly known as MLOps – comes in.
MLOps enables organizations to apply DevOps principles and processes to ML systems. Just as DevOps brings automation and collaboration to software development and infrastructure management, MLOps brings a systematic approach to managing the ML lifecycle.
The global MLOps market is forecast to grow at a CAGR of over 41% to reach $5.9 billion by 2027. (Source: Globenewswire)
In this post, we’ll look at what exactly MLOps entails, why it’s important, and 13 best practices for MLOps in production ML environments.
What is MLOps?
MLOps stands for Machine Learning Operations. It refers to the processes, tools and team workflows needed to deploy, monitor, maintain and update ML systems in production reliably and efficiently.
Some key concepts and goals of MLOps include:
- Automation – Automating as many ML processes as possible (data preprocessing, model training, evaluation, deployment etc.)
- Collaboration – Breaking down silos and enabling collaboration between data scientists, engineers and business teams.
- Reproducibility – Ensuring models can be rebuilt and retrained exactly the same way.
- Versioning – Tracking different model versions and experiments.
- Monitoring – Monitoring models in production to detect issues and drift.
- Deployment – Streamlining model deployment and serving infrastructure.
- Governance – Governance of models, data, experiments and quality assurance.
Basically, MLOps integrates ML model development into the broader software development lifecycle. This continuous integration and delivery (CI/CD) approach lets organizations rapidly iterate on ML models while maintaining stability, reproducibility and auditability.
Why is MLOps Important?
MLOps is becoming essential for any organization using ML in production, for several reasons:
- Productivity – Automating repetitive ML tasks allows data scientists to focus on higher value work and accelerates the development lifecycle.
- Reliability – With reproducibility and rigorous testing, models are less likely to fail unexpectedly in production. 40% of models trained never make it to production due to lack of MLOps. (Source: VentureBeat)
- Governance – MLOps provides model versioning, auditability and explainability required for regulated use cases.
- Collaboration – Cross-functional workflows break down silos between teams and improve development velocity.
- Efficiency – MLOps best practices optimize the use of compute resources and minimize technical debt.
- Scale – Automated pipelines allow ML systems to be rapidly deployed across many products, apps, and environments.
Without MLOps, ML systems become complex and brittle over time, making them prone to errors and difficult to maintain. MLOps prevents these issues at scale.
13 Best Practices for Production Machine Learning
Here are 13 recommended best practices for applying MLOps and DevOps principles to production ML:
1. Use ML Development Frameworks
Standardize model development on frameworks like TensorFlow, PyTorch or SciKit-Learn. This makes model code reusable, testable and consistent.
2. Modularize Model Training Code
Break model code into modules that can be open-sourced for reusability. Use libraries to package models for deployment.
3. Automate Training Pipelines
Automate data preparation, model training, evaluation and registration using pipelines like Kubeflow, MLflow or Azure Machine Learning.
4. Track Experiments with ML Metadata
Track model experiments, parameters, metrics and lineage metadata using systems like MLflow, DVC or TensorBoard.
5. Containerize Models for Portability
Containerize models using Docker so they can be reliably deployed and run on diverse platforms.
6. Leverage Git for Version Control
Use Git/GitHub to version control ML code, configurations, training scripts and documentation.
7. Continuously Test Models
Continuously test model quality, bias mitigation, data validation and runtime performance throughout the pipeline.
8. Monitor Models in Production
Monitor models in production for deteriorating performance, data drift and failures using logs and metrics.
9. Automate Redeployment on Changes
Automatically retrain and redeploy models when the model or data changes significantly.
10. Implement CI/CD Pipelines
Use CI/CD tools like Jenkins, GitHub Actions or GitLab to automate testing and deployment of model changes.
11. Abstract Model Serving
Serve models behind APIs so clients are isolated from implementation details. Use standards like ONNX Runtime.
12. Manage Models like Software
Treat ML models like software artifacts – use semantic versioning, issue tracking, code reviews, etc.
13. Ensure Reproducibility
Models should be reproducible from source code, data, configurations and weights. Use tools like DVC.
Challenges in Adopting MLOps
While the benefits of MLOps are clear, it also poses some new challenges for organizations:
- Skill Gaps – Data scientists may lack software engineering skills. IT teams may need to learn about ML tools and techniques. Cross-training is key.
- Legacy Systems – Moving from ad hoc ML scripts to automated MLOps pipelines requires upfront investment.
- Coordination – Aligning priorities between data scientists, engineers, ops teams and business users takes work.
- Tool Sprawl – The MLOps tooling landscape is complex. Avoiding tool overload is hard.
- Cultural Issues – Adopting DevOps values like collaboration and transparency is as much a cultural challenge as a technical one.
The Future is MLOps
MLOps enables organizations to scale ML efficiently and responsibly. Just like DevOps transformed software development, MLOps is critical to industrializing AI and integrating it into products and processes.
Adopting MLOps best practices leads to faster model development cycles, more reliable systems and closer alignment between data scientists and IT teams. To successfully leverage AI, prioritize MLOps in your machine learning initiatives.
Conclusion
MLOps brings DevOps principles like automation, collaboration and continuous delivery to machine learning. With MLOps, organizations can accelerate model development while ensuring those models remain stable, reproducible and auditable in production.
Effective MLOps processes can reduce time spent on model deployment by over 75%. (Source: Microsoft)
However, implementing MLOps also raises challenges around skill gaps, legacy systems, tooling choice, and organizational culture. Investing in cross-training, modern systems, and aligment will be key to successfully leveling up machine learning capabilities.
By embracing MLOps, companies can scale their ML systems and embed AI sustainably into their tech stack. Just as DevOps enabled faster software releases with more reliability, MLOps is critical to extracting value from machine learning in the real world.
