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Machine learning operations (MLOps)

Machine learning operations (MLOps) aims to deploy and maintain machine learning models reliably, efficiently, and at scale. This article covers the core parts of MLOps:

Registering and tracking models

Registering and tracking models is crucial for version control, collaboration, monitoring, resource management, and documentation.

  • Version control — maintain a record of different versions of machine learning models. This is crucial for reproducibility, compliance and governance, deployment and rollback, and tracking changes over time.

  • Model collaboration — provide a central repository where data scientists and machine learning engineers can store and share their models.

  • Monitoring and accountability — monitor models by recording metrics and statistics. This monitoring makes sure that models continue to meet their performance criteria and helps identify deviations that may require intervention.

  • Resource management — manage resources efficiently by identifying underutilized or overutilized models. It allows organizations to allocate computational resources more effectively and reduce operational costs.

  • Documentation — store documentation and metadata about each model, including its purpose, hyperparameters, training data, and more.

MLFlow is available as a Python library and is a commonly used open-source tool to manage the entire machine learning lifecycle.

Orchestration and pipelines

Orchestration and pipelines automate repetitive tasks, ensure reproducibility, enable version control, facilitate scalability, allow for monitoring and optimization, streamline deployment, and aid in resource management and governance.

  • Automation — reduces human errors and speeds up the machine learning workflow.

  • Version control — maintain versions of both data and code. This is crucial for reproducibility, compliance and governance, deployment and rollback, and tracking changes over time.

  • Scalability — orchestratation allows you to scale pipelines to handle large datasets and complex workflows.

  • Monitoring and optimization — include monitoring and feedback loops that continuously assess the models' performance in production. Trigger retraining or alert the appropriate teams if there are any issues with the pipeline, for example, a drop in accuracy.

  • Resource management — manage computational resources efficiently to reduce infrastructure costs.

Use the data workflows service in Cognite Data Fusion (CDF) to effectively coordinate and manage workflows for MLOps.

Monitoring and alerting

Monitoring and alerting track model performance, detect data and model drift, monitor resource utilization, detect anomalies, prevent downtime, identify security threats, improve operational efficiency, ensure compliance, and enable continuous improvement.

  • Performance tracking — monitor metrics such as accuracy, precision, recall, and F1-score to ensure that models meet their performance targets.

  • Data drift detection — monitor data quality to detect data drift and ensure that models remain accurate and reliable.

  • Model drift detection — monitor predictions to detect any deviation from expected behavior.

  • Resource utilization — keep track of resource utilization, including CPU, memory, and GPU usage to optimize resource allocation and manage operational costs.

  • Anomaly detection — detect anomalies or unexpected behavior in model predictions or system performance. Trigger alerts to help you quickly mitigate potential problems.

  • Downtime prevention — detect system failures or downtime in real-time to minimize disruptions to services.

  • Security — identify security threats or vulnerabilities, for example, unusual data access patterns or unauthorized access attempts.

  • Compliance — ensure that machine learning systems meet compliance requirements. Timely alerts can help maintain audit trails and provide evidence of compliance.

  • Continuous improvement — get valuable insights into model behavior over time. The insights can inform decisions about model retraining, fine-tuning, or even redesign.

Learn more about monitoring and alerting in these guidelines: