Production experience is required and I don't have any! What should I do?

How to Do It?

Take a personal project you've worked on in the past and break it down through the lens of production. Identify potential issues and propose solutions using the four detailed steps below.

When presenting the project in a job interview, integrate these considerations throughout the project discussion. If one of the steps wasn't relevant because the project didn't reach production, say:

"If I were to deploy this project to production, I would also address the following considerations."

This approach demonstrates to the interviewer that you are aware of all the necessary factors and implications.

🔹 Step 1: Data Collection and Processing

Every good ML project starts with high-quality data. Before moving to production, ensure the following:

✅ Define defect types and classification criteria.

✅ Capture images of both defective and non-defective products under varying conditions (e.g., different lighting).

✅ Properly split data into train/validation/test sets to prevent model bias.

🔹 Step 2: Model Architecture Selection and Training

The model selection directly impacts production performance.

✅ Use a CNN-based model with segmentation capabilities for precise defect detection.

✅ Adapt the architecture to defect size and required resolution.

✅ Define relevant success metrics (precision/recall) based on the cost of false positives/negatives.

✅ Apply appropriate data augmentation to improve model generalization.

🔹 Step 3: Optimization and Fine-Tuning Before Production

To ensure the model performs well in real-time and under changing conditions, apply optimizations:

✅ Calibrate the optimal decision threshold to balance recall and precision.

✅ Improve robustness to variations in lighting and environmental conditions.

✅ Reduce processing time to meet real-time requirements.

✅ Implement monitoring mechanisms to track model performance over time.

🔹 Step 4: Deployment to Production – 4 Key Considerations

📌 Response Time & Performance (Latency & Throughput)

✅ Optimize the model (Quantization, Pruning) to reduce size and improve speed.

✅ Use hardware acceleration (GPU/FPGA) for faster inference.

✅ Align scheduling with production rate, including task queue management.

📌 Robustness – Ensuring the Model Survives in the Real World

✅ Handle invalid inputs and implement fallback mechanisms.

✅ Adapt to environmental changes (e.g., different camera types or data sources).

✅ Maintain comprehensive logging of model decisions for tracking and improvements.

📌 Maintainability – Keeping the Model Up and Running

✅ Provide thorough system and model documentation.

✅ Implement an automated model update mechanism.

✅ Continuously monitor performance with alerts for anomalies.

✅ Enable an interface for parameter tuning and result evaluation.

📌 Integration with Business Systems

✅ Connect the model to production systems.

✅ Build an alert system to detect failures and performance deviations.

✅ Generate automated performance reports for long-term tracking.

📌 What Else Can You Do?

✅ Gain hands-on experience with tools like AWS/GCP/Azure, MLflow, Kubernetes, Airflow, and Feature Stores.

✅ Listen to podcasts on MLOps – one of the most in-demand fields today!

✅ Attend meetups and coding workshops to gain practical experience.