MLOps core principles
These are the core principles and practices organizations can use to improve the efficiency and effectiveness of their data science and ML workflows.
Effective data science and MLOps involve a number of different practices and approaches that aim to improve the efficiency and effectiveness of data science and machine learning (ML) workflows. Some key principles and practices that can contribute to effective data science and MLOps include:
Collaboration:
Data science and MLOps involve a wide range of stakeholders, including data scientists, ML engineers, IT professionals, and business stakeholders. Effective collaboration between these stakeholders is key to ensuring that projects are aligned with business goals and that data science and ML workflows are efficient and effective.
Automation:
Automating key tasks and processes can help to reduce errors and improve the efficiency of data science and ML workflows. This can include automating data preparation, model training, and deployment processes.
Version control:
Version control systems like Git can help to track changes to code, data, and other assets, making it easier to collaborate with others and to roll back changes if necessary. For large datasets and model files, retain identifiable snapshots in appropriate storage and version their manifests or references alongside the code. A mutable storage path is not a reproducible dataset version.
Testing and validation
Testing and validation are important for ensuring the quality and reliability of data science and ML models. This can include both unit testing of code and functional testing of models to ensure they are behaving as expected. Separate validation used for model selection from a held-out final assessment, and inspect important cohorts as well as aggregate scores.
Monitoring and optimization:
Ongoing monitoring of data science and ML workflows can help to identify and address issues in a timely manner, while ongoing optimization efforts can help to improve the efficiency and effectiveness of these workflows over time.
Put the principles into one workflow
For a classifier refresh, the data owner publishes a retained dataset snapshot, the developer submits a versioned training component, and an evaluation operation compares the candidate with the baseline. A reviewer then decides whether to promote the model package. Production measurements feed the next investigation.
In Polyaxon, components and operations, tracking, and model versions make those handoffs inspectable. Keep the data identity, acceptance criteria, evidence, and decision owner with each change; automation is useful only when the team can explain what it executed and why the result was accepted.
By following these principles and practices, organizations can improve the efficiency and effectiveness of their data science and ML workflows, and better leverage the power of these technologies to drive business value.