Early validation is crucial for successful machine learning project outcomes.
Operationalizing machine learning involves addressing technical and organizational challenges beyond pure ML research.
Deep dives
The importance of early validation in machine learning
Early validation in machine learning is crucial to prevent the deployment of faulty models or waiting until the final stages of testing to discover issues. By validating models early, developers can identify and address problems before they become critical. This allows for increased velocity in the development process and ensures better overall performance. Validating as early as possible is key to successful machine learning project outcomes.
The challenges of operationalizing machine learning
Operationalizing machine learning systems involves more than just the ML research itself. It requires addressing technical and organizational problems such as on-call processes, system reliability, and tools. ML engineers often face surprises when transitioning from academia to industry, including the need to focus on system integration, feature management, and sharing knowledge among multiple data scientists. The evolution from early experimentation to production models presents challenges that go beyond pure ML research.
The importance of monitoring and response in production ML
Monitoring and response play a critical role in operationalizing machine learning models. ML engineers need to ensure that models perform well and address issues promptly to avoid customer complaints and regressions. The ability to detect data corruption or other problems and respond quickly is essential. While monitoring for data drift is important, focusing on monitoring for data corruption is even more crucial to prevent negative impact on predictions and business outcomes.
Considerations for ML tooling and stack selection
When building machine learning systems, it is advisable to start with established tools and platforms such as AWS or similar cloud services. Setting up an AWS account and utilizing services like EMR clusters for Spark can provide a solid foundation. Workflow schedulers like Airflow can help manage data pipelines, enabling easy definition and execution of dependencies. While there are many options available, starting with tried and tested tools and gradually building the ML stack can provide a good starting point for organizations.
Shreya Shankar is a computer scientist, PhD student in databases at UC Berkeley, and co-author of "Operationalizing Machine Learning: An Interview Study", an ethnographic interview study with 18 machine learning engineers across a variety of industries on their experience deploying and maintaining ML pipelines in production.
Shreya explains the high-level findings of "Operationalizing Machine Learning"; variables that indicate a successful deployment (velocity, validation, and versioning), common pain points, and a grouping of the MLOps tool stack into four layers. Shreya and Lukas also discuss examples of data challenges in production, Jupyter Notebooks, and reproducibility.
Show notes (transcript and links): http://wandb.me/gd-shreya
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💬 *Host:* Lukas Biewald
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