Day155 - MLOps Review: Introduction to Machine Learning Systems Design (1)
Designing Machine Learning Systems: Business and ML Objectives & Requirements for ML Systems
Aligning Business and ML Objectives for Real-World Impact
Machine Learning (ML) promises great potential, but delivering business value through ML remains a challenging endeavor. A common reason ML projects fail to generate meaningful outcomes is a disconnect between ML objectives and business objectives. Let’s explore how to bridge this gap and build ML systems that are not only technically sound but also business-relevant and production-ready.
Business vs.ML Objectives: Speaking the Same Language
ML Objectives: The Data Scientist’s View
When data scienctists work on ML projects, their primary concern often revolves around model performance metrics like:
- Accuracy
- Precision / Recall
- F1 Score
- Inference Latency
- AUC-ROC
While these metrics are essential for measuring model behavior, they do not directly translate into business impact. Improving model accuracy from 94% to 94.2% may excite an ML team – but may mean little to a business decision-maker unless it results in tangible value.
Business Objectives: The Executive View
In contrast, businesses care about metrics like:
- Revenue growth (e.g., through increased conversions or click-through rates)
- Cost reduction (e.g., by reducing manual operations or fraud)
- Customer retention (e.g., reducing churn)
- Engagement (e.g., time spent on that platform)
In essence, businesses aim to maximize profit – either directly through monetization or indirectly via user satisfaction, operational efficiency, or long-term retention.
A Common Pitfall: Misaligned Priorities
A frequent pattern in short-lived ML projects is this:
The ML team optimizes models for better metrics, but fails to tie those improvements to real-world business KPIs. The business, not seeing value, eventually pulls the plug.
Example: A recommender system improves its prediction accuracy by 1%, but unless that leads to higher purchase-through rates, it doesn't justify the engineering investment.
Bridging the Gap: How to Align Objectives
Tie ML Metrics to Business Outcomes
To demonstrate real value, ML teams must map model improvements to business metrics. This often involves running A/B experiments or longitudinal studies that show causal links between:
- A lift in ML accuracy
- A lift in business KPIs (e.g., conversions, revenue, satisfaction)
Examples of Successful Alignment:
Some ML use cases naturally tie into revenue:
- Ad click-through rate (CTR): A 1% improvement directly translates to increased ad revenue.
- Fraud detection: Every prevented fraudulent transaction equals money saved.
Other use cases require intermediary metrics. For instance, Netflix uses a metric called take-rate – the number of “quality plays” divided by the number of recommendations shown. Higher take-rate correlates with:
- More total viewing hours
- Lower churn rates
This establishes a traceable link between model performance and revenue stability.
However, it’s not always a clear-cut position. Some ML imperatives are directed to a better personalization engine to make users happier (long-term value). Still, if it also helps them solve problems more quickly, it might reduce the time spent on the platform.
In such cases, experiments are crucial for understanding the trade-offs. Business stakeholders don’t just want accuracy— they want results.
Realism Over Hype
Companies often expect machine learning (ML) to work like magic. Media hype doesn’t help. While machine learning can bring significant returns, those returns usually come after years of investment in infrastructure, tooling, and team maturity.
A 2020 Algorithmia survey found that companies using ML for over 5 years deploy models in under 30 days, and newer companies take over a month per deployment.
This maturity curve matters. Efficient ML systems pay off- but only if the company has the right processes and tools in place.
Requirements: Building ML Systems That Work
Technical excellence isn’t enough. Real-world ML systems must also meet the following requirements.
- Reliability: They must perform correctly, even when components fail. Unlike traditional software, ML models can fail silently —producing outputs that appear fine but are entirely incorrect.
- Scalability: Systems must be able to handle growth with increasing numbers of users, larger models, more frequent predictions, and multiple versions of the same model. (e.g., one per customer) This means building for cloud-native deployment, autoscaling, and model lifecycle management.
- Maintainability: As teams grow and shift, others must be able to understand past decisions, reproduce results, and update models safely. That requires versioned datasets, clear documentation, and modular pipelines.
- Adaptability: Data changes, users change, businesses change. Then, ML systems must evolve as well. This involves monitoring data drift, automating retraining, and deploying updates without service interruptions.
ML projects thrive when they are grounded in business value and engineered with production in mind. It’s not just about building innovative models—it’s about creating intelligent systems that move the needle.
If you’re starting a new ML project, ask early:
- What business metric are we trying to improve?
- How will we measure success beyond validation accuracy?
- Can we run an experiment to prove value?
That’s the path to lasting impact.
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