The many facets of ML development
As we’ve already touched on in our ML chapters, there are several dimensions to consider when thinking about ML at any level. Let’s recap some of the aspects of ML development as they relate to data stewardship:
- Mastering feature engineering: Features form the bedrock of ML models. Hence, preserving and making them accessible is crucial. Using a feature store not only aids in model development but also provides lineage tracing, ensuring clarity in data transformation and eliminating potential discrepancies between training and real-time applications.
- Perfecting data handling: Often, the cornerstone data for model training gets misplaced, complicating the recreation of model parameters. However, with tools such as Managed MLflow, datasets are meticulously logged, ensuring a seamless ML model development cycle.
- Refining model training: The journey from ideation to production in ML is seldom straightforward. Model selection involves rigorous evaluations, methodological considerations, and constant fine-tuning. Using platforms such as MLflow, each iteration (and the associated metrics) are captured, ensuring a transparent model training process.
Beyond training – model deployment and monitoring
Ensuring model accuracy doesn’t end with deployment; it demands continuous oversight, especially as models adapt to real-world scenarios. Monitoring encompasses several aspects:
- Concept drift: Real-world variables, such as market shifts or evolving business strategies, can drastically affect model outcomes.
- Data adjustments: While deliberate data changes might be easy to track, inadvertent shifts in data collection or representation can introduce model inconsistencies.
- Bias: Beyond statistical imbalance, bias can manifest as the unequal treatment of distinct groups, necessitating rigorous checks against potential disparities. We have already seen how techniques such as synthetic labeling can lead to biases bubbling up to the surface.
For successful ML governance, establishing performance thresholds, monitoring frequencies, and using problem-alert procedures is pivotal. Many companies offer an ecosystem of tools, from automated dashboards and lineage tracking to data quality checks, ensuring models remain accurate, unbiased, and compliant.
A guide to architectural governance
Architectural governance is the cornerstone of ensuring the seamless integration of IT infrastructure, and it supports core business processes. Its principal objectives encompass the following:
- Cataloging current architectural layouts
- Establishing guidelines, principles, and benchmarks
- Aligning business and IT visions
- Crafting a target infrastructure blueprint
- Identifying the value proposition of the target framework
- Highlighting disparities between the current and desired architecture
- Crafting a comprehensive architectural roadmap
The five pillars of architectural governance
With these five pillars in mind, we can also turn to some principles on how to modernize your governance architecture and optimize it’s effectiveness.
- Consistency: Ensuring harmonious and integrated workflows without hitches
- Security: This is paramount for protecting sensitive data and upholding regulatory compliance
- Scalability: A forward-looking approach, taking into account the growing data needs
- Standardization: Embracing universally accepted standards for flexibility and interoperability
- Reuse: Promoting efficiency by creating and reutilizing components across the architecture