# 5 Things I Wish I Knew Before Starting My ML Career
After working on various machine learning projects, from career prediction systems to real-time dashboards, I've learned valuable lessons that I wish someone had shared with me earlier.
## 1. Data Quality Matters More Than Model Complexity
When I started, I was obsessed with trying the latest models and architectures. But I quickly learned that **clean, well-labeled data beats a fancy model every time**.
### What I learned: - Spend 80% of your time on data quality, 20% on models - Start with simple models (logistic regression, random forests) to establish baselines - Invest in data validation and monitoring from day one
## 2. Production ML is Different from Kaggle
Kaggle competitions are great for learning, but production ML has different challenges:
### Real-world considerations: - **Latency**: Your model needs to return predictions in milliseconds, not minutes - **Scalability**: Can your system handle 10x the traffic? - **Monitoring**: How do you detect when your model starts failing? - **Maintenance**: Models degrade over time and need retraining
## 3. Domain Knowledge is Your Secret Weapon
Understanding the business problem is often more valuable than knowing the latest research papers.
### Example: When building Skillmap AI, my understanding of career development and job markets helped me: - Engineer better features - Design more meaningful evaluation metrics - Identify edge cases the model needed to handle
## 4. Start with the Simplest Thing That Could Work
Don't build a complex deep learning pipeline when a simple rule-based system might solve 80% of the problem.
### My approach now: 1. Start with rules or heuristics 2. Add a simple ML model if needed 3. Only move to complex models if there's clear ROI
This saved me months on several projects.
## 5. Communication is Half the Job
The best model is useless if stakeholders don't trust it or understand when to use it.
### Skills to develop: - Explain model predictions in business terms - Visualize model behavior and performance - Set realistic expectations about what ML can and can't do - Document everything clearly
## Bonus: Learn MLOps Early
I spent too long focusing only on model development. Learning about: - Model versioning and experiment tracking - CI/CD for ML - Feature stores - Model monitoring
...would have saved me countless hours debugging production issues.
## Conclusion
Machine learning is an exciting field, but success requires more than just understanding algorithms. Focus on the fundamentals, learn from production experience, and always keep the business goal in mind.
What lessons have you learned in your ML journey? I'd love to hear from you!