Skip to main content

MLOps best practices

MLOps (Machine Learning Operations) is a set of practices that streamline and automate the lifecycle of machine learning models. It involves collaboration across multiple teams, including data scientists, ML engineers, and DevOps, to efficiently manage the development, deployment, monitoring, and maintenance of ML models in production.

MLOps benefits

MLOps is important because it addresses the complexities and challenges of operationalizing machine learning models. By adopting MLOps practices, organizations can deliver more robust, scalable, and effective machine learning solutions, driving innovation and achieving better outcomes.

MLOps offers these benefits:

Faster time to market

MLOps facilitates the rapid deployment of machine learning models to production by automating and monitoring the ML lifecycle stages, including data preparation, model training, validation, deployment, and monitoring.

This automation helps reduce manual errors and speeds up the process of getting models into production, enabling organizations to leverage their data insights more quickly and gain a competitive edge.

Reproducibility and traceability

Machine learning models depend heavily on data, parameters, and code.

MLOps practices ensure that every aspect of the machine learning process, from data preprocessing to model training and inference, is version-controlled, documented, and reproducible.

This makes it easier to trace back the steps of the model development process, understand decision-making, and replicate successful models.

Quality and reliability

By incorporating continuous integration and continuous deployment (CI/CD) practices, MLOps ensures that models are rigorously tested and validated before being deployed.

This not only enhances the quality and performance of models but also ensures they are reliable and stable in production environments.


MLOps provides frameworks and methodologies for managing and deploying machine learning models at scale.

This includes managing resources efficiently, handling multiple models, and ensuring models can serve predictions under varying loads.

Scalability is crucial for businesses that rely on machine learning to process large volumes of data or require real-time insights.

Efficient resource utilization

Machine learning models, especially deep learning models, can be resource-intensive to train and deploy.

MLOps practices help optimize resource usage, reducing computational costs and ensuring that the infrastructure is efficiently utilized.

This includes selecting the appropriate hardware, managing cloud resources, and optimizing model performance.


MLOps fosters better collaboration between data scientists, ML engineers, and operations teams.

It standardizes processes and tools, enabling seamless communication and workflow management across teams with different expertise.

This cross-functional collaboration is vital for the successful deployment and maintenance of ML systems.

Compliance and governance

With increasing data privacy regulations and ethical considerations, MLOps ensures that machine learning processes are compliant with relevant laws and ethical guidelines.

This includes managing data access, ensuring model transparency, and implementing bias detection and mitigation strategies.

Continuous monitoring and improvement

MLOps involves continuous monitoring of deployed models to detect performance degradation, data drift, or concept drift.

This proactive approach allows for timely model updates or retraining, ensuring that the models remain effective and relevant over time.

MLOps best practices

By adopting these best practices, organizations can achieve more efficient, reliable, and scalable ML operations, leading to faster deployment of high-quality ML models and more impactful business outcomes.

Version control

Version control everything, including:

  • Code: Use version control systems like Git for all ML model code, scripts, and notebooks.
  • Data: Version control your datasets to track changes over time, enabling reproducibility and rollback if needed.
  • Models: Version your models along with their parameters, training scripts, and environment requirements.

Continuous integration (CI) and continuous delivery (CD) pipelines can automate testing, building, and deployment of ML models. This includes automated testing of code, data validation, model training, and deployment to production.

As part of CI/CD, it is especially useful to automate your testing. Ensure you include comprehensive tests for data validation, model validation, and integration tests to ensure reliability and performance of ML models. Employ techniques like A/B testing, shadow mode, and canary deployments to validate models in production environments.

There are a variety of CI/CD tools on the market today, including Harness CI and Harness CD.


Use containers (such as Docker) to package your ML models, libraries, and dependencies. This ensures consistency across different environments and facilitates easier deployment and scaling.

Kubernetes or similar container orchestration tools can manage containerized applications for scaling and high availability.

Tracking and monitoring

Track experiments with detailed logging of model parameters, metrics, training data, and outcomes to compare models and reproduce results.

Implement monitoring for model performance (accuracy, precision, recall, etc.), data drift, concept drift, and operational metrics (latency, throughput, errors).

Use observability tools to log, trace, and visualize model predictions and system performance, enabling faster debugging and optimization.

Data and model management

Ensure robust data management practices, including secure data storage, access controls, and data privacy compliance (e.g., GDPR, CCPA).

Manage the lifecycle of your models with strategies for updating, retraining, and deprecating models as needed.

Use a model registry to manage and catalog models, including versioning and metadata, facilitating easier rollback, audit trails, and governance.


Foster collaboration across teams (data scientists, ML engineers, DevOps) with shared tools, platforms, and practices.

Document models, experiments, and decision-making processes to build institutional knowledge and aid in compliance efforts.

Ethics and bias evaluation

Integrate ethical considerations and bias detection in your ML workflows.

Regularly evaluate models for fairness and unintended biases, and implement corrective measures as necessary.

Scalability and cost management

Design your MLOps architecture for scalability, considering both the infrastructure and the model complexity.

Monitor and optimize resource usage and costs, using cloud services effectively and choosing the right compute resources for training and inference.