Why Your 99% Kaggle Model Fails in Production: A Practical Guide to ML Engineering
In this article, we have explained why models made on Kaggle like platforms are not enough for real world and we have also discussed about the major elements in Machine Learning Engineering.
1)Introduction to Machine Learning Engineering
Machine Learning Engineering (MLE) is not just the art of building machine learning models. It is the discipline of designing, developing, and maintaining scalable and reliable machine learning systems that operate in production. In this article, we will walk through the complete lifecycle, mindset, tools, phases, and real-world case examples that make up the role and responsibilities of a Machine Learning Engineer.
1.1)Why Machine Learning Engineering?
The primary goal of Machine Learning Engineering is to solve real-world problems using statistical models, data processing, and predictive algorithms in a way that is scalable, testable, and maintainable. Many projects fail not because the model was poor but because the system around the model wasn’t robust.
A few reasons why MLE is essential:
End-to-End Ownership: From research to deployment and monitoring
Collaboration: Working with product managers, software engineers, and stakeholders
Scalability: Building systems that perform under real-world conditions
Maintainability: Ensuring models are versioned, monitored, and reproducible
Case Study: A leading logistics company implemented a delivery time predictor. While initial accuracy was high in lab conditions, the lack of proper deployment and monitoring led to poor real-world results. Adding monitoring and real-time retraining loops fixed the issue, boosting delivery reliability by 20%.
1.2)Just-Enough Engineering Rules
MLEs don’t need to be expert software engineers, but certain engineering practices are non-negotiable:
1)Software Dev: Write clean, modular code and basic unit tests.
✖ No need to dive into advanced async systems or broker setups.
2)Data Engineering: Build and schedule ETL jobs for features.
✖ Skip building massive, real-time streaming systems.
3)Visualization: Create simple, clear plots to explain model behavior.
✖ No need for fancy, interactive UX-heavy dashboards.
4)Project Management: Define, scope, and steer ML projects with focus.
✖ No PMP certification required, clarity beats credentials here.
1.3)Major Elements of Machine Learning Engineering
Core Tenets of MLE: From Planning to Evaluation
Let’s walk through each phase of the MLE process with practical details and a case study to ground our understanding:
1. Planning Phase
This phase involves strategic foresight and thorough investigation before touching any code:
Establish a clear understanding of business objectives with stakeholders.
Conduct discussions with subject matter experts (SMEs) to gain domain-specific insights.
Translate business goals into technical KPIs and define clear problem boundaries.
Conduct data quality audits: identify gaps, anomalies, and readiness for modeling.
Formulate project roles and align expectations across the team.
Case Study: For a healthcare startup predicting patient readmission, planning involved interviews with doctors and nurses. This led to identifying 12 key patient features not initially present in the dataset.
2. Research & Scoping Phase
This is a critical pre-modeling step to map the technical landscape:
Explore prior work:- academic papers, open-source repositories, and whitepapers.
Evaluate tools and frameworks that align with your project scope and team expertise.
Draft several solution architectures with trade-offs around accuracy, complexity, and scalability.
Case Study: An ed-tech company found through paper research that transformer models outperformed LSTMs in content recommendation, which influenced their pipeline design.
3. Experimentation Phase
In this phase, we validate our assumptions with tangible outputs:
Develop a basic prototype with reduced or sample data to test feasibility.
Estimate infrastructure needs and potential technical debts.
Consult SMEs with early results to confirm practical validity.
Solidify performance metrics and acceptable thresholds for success.
Case Study: A fintech startup used a public dataset for building credit scoring models. This PoC avoided premature costs and proved feasibility before cloud deployment.
4. Development Phase
Here we move from experiment to production-ready foundations:
Create a minimal viable product (MVP) that encapsulates core functionality.
Conduct internal testing against edge cases and domain-specific scenarios.
Implement robust code quality practices, unit tests, CI/CD pipelines, and data contracts.
Case Study: A pest detection model MVP was built for a farmer network. SMEs asked for a “confidence score” to be displayed alongside predictions.
5. Deployment Phase
Deployment is not just about pushing to production, it’s about stability and scale:
Decide the most suitable form of model exposure: REST APIs, batch processing, or stream inference.
Set up logging, dashboards, and real-time alerts to capture performance issues.
Case Study: A popular ride-hailing service launched a real-time surge pricing model that adjusted rates based on traffic, user demand, and available drivers. The initial deployment used a REST API, but during high-demand periods, the model experienced significant latency. This delay in serving updated prices disrupted user experience. To resolve the issue, the team redesigned the deployment pipeline to use a message queue system along with a caching layer. Prices were then updated every few minutes in batches, ensuring consistent delivery and significantly improving system stability and responsiveness during peak hours.
6. Evaluation Phase
Post-deployment evaluation is about ensuring continuous value:
Measure the model’s impact on core business metrics like revenue, retention, or operational savings.
Monitor changes in input data or performance to detect drift.
Set a retraining cadence and refresh mechanisms.
Incorporate feedback loops from end-users or business stakeholders.
Case Study: A voice assistant consistently failed to interpret regional accents. Evaluation led to retraining on regional datasets, which increased acceptance by 28%.
MLE is inherently iterative. If evaluation exposes flaws, loop back to experimentation.
3)Goals of Machine Learning Engineering
Solve real-world business problems using ML
Build systems that are scalable and reproducible
Maintain model performance over time
Balance technical depth with effective communication
Provide clear ROI and business impact
4)Summary of the Machine Learning Engineering Lifecycle
Let’s recap the phases as a flow:
Planning: Define the problem, understand the data
Research & Scoping: Survey existing methods and tools
Experimentation: Test ideas and get feedback
Development: Build and validate the MVP
Deployment: Serve models and monitor usage
Evaluation: Assess performance and impact
If evaluation fails, return to experimentation. The loop is essential for model success in production.
What’s Next?
In the upcoming article, we will explore:
The difference between Data Scientists and ML Engineers
How to reduce risk early in ML projects
Applying Agile principles to ML workflows
A comparison between DevOps and MLOps
Stay tuned, and as always, feel free to leave feedback or questions in the comments!
You may also be interested in watching the lecture on Introduction to Machine Learning Engineering on Vizuara’s Youtube channel for better Understanding.
Interested in learning AI/ML LIVE from us?
Check this out: https://vizuara.ai/live-ai-courses