𝗙𝗿𝗼𝗺 𝗜𝗱𝗲𝗮 𝘁𝗼 𝗜𝗺𝗽𝗮𝗰𝘁: 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗠𝗟 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄

#aiml #devops #workflow

As I take intentional steps into the world of AI and Machine Learning, one thing has become clear: 𝘮𝘢𝘤𝘩𝘪𝘯𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘪𝘴 𝘯𝘰𝘵 𝘫𝘶𝘴𝘵 𝘢𝘣𝘰𝘶𝘵 𝘸𝘳𝘪𝘵𝘪𝘯𝘨 𝘤𝘰𝘥𝘦 𝘰𝘳 𝘵𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘮𝘰𝘥𝘦𝘭𝘴 -it’s about solving real problems with clarity, structure, and purpose.

Here’s a breakdown of the ML workflow I’ve been studying and practicing -not just from a technical view, but from a problem-solving mindset that aligns with real business needs:

𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗙𝗶𝗿𝘀𝘁, 𝗡𝗼𝘁 𝘁𝗵𝗲 𝗠𝗼𝗱𝗲𝗹
Before touching data or algorithms, the first step is always asking:
“What problem are we trying to solve, and why does it matter?”
Are we predicting customer churn? Detecting fraud? Forecasting demand?
This clarity influences everything that follows, from the type of data we collect to the model we build and how we measure success. A project that starts with a vague goal often leads to wasted effort. But one that starts with a 𝘄𝗲𝗹𝗹-𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗼𝗯𝗷𝗲𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗺𝗲𝗮𝘀𝘂𝗿𝗮𝗯𝗹𝗲 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗰𝗿𝗶𝘁𝗲𝗿𝗶𝗮 is positioned to make real impact.
𝗚𝗮𝘁𝗵𝗲𝗿 𝗮𝗻𝗱 𝗣𝗿𝗲𝗽𝗮𝗿𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮
Once the problem is clear, the next step is sourcing quality data from databases, logs, APIs, or even unstructured sources like text or images. But raw data is messy.
We clean it, remove duplicates, handle missing values, and organize it for analysis. Then comes 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗼𝗿𝘆 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 (𝗘𝗗𝗔) -using visualizations and statistics to understand patterns, correlations, and outliers. This step is critical. It helps us uncover insights and make smarter choices about feature engineering and model selection.
1. 𝗦𝗲𝗹𝗲𝗰𝘁 𝗮𝗻𝗱 𝗧𝗿𝗮𝗶𝗻 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝗹 Model selection isn’t about choosing the most advanced algorithm -𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘁𝗼𝗼𝗹 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗷𝗼𝗯. If the data is tabular, we might use decision trees or gradient boosting. For text or sequences, maybe transformers or RNNs. And sometimes, the simplest model works best. It’s all about balancing accuracy, interpretability, and efficiency, especially in business scenarios where transparency and speed matter as much as results.
𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗠𝗲𝘁𝗿𝗶𝗰𝘀

You can’t improve what you don’t measure and not all problems use the same yardstick.

• For classification, we look at accuracy, precision, recall, F1-score, and AUC-ROC.

• For regression, we use RMSE, MSE, and R².

• For anomaly detection, we focus on recall vs. precision trade-offs.

It’s not just about getting high scores. It’s about understanding what those scores mean in the real world because catching fraud or diagnosing disease has consequences beyond metrics.
𝗧𝘂𝗻𝗲, 𝗗𝗲𝗽𝗹𝗼𝘆, 𝗮𝗻𝗱 𝗠𝗼𝗻𝗶𝘁𝗼𝗿

After training, we fine-tune hyperparameters (like learning rates or tree depths) to boost performance without overfitting.

Then comes deployment -serving the model via APIs or integrating it into an application. But it doesn’t stop there. The real world changes. Data drifts. So, we 𝗺𝗼𝗻𝗶𝘁𝗼𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗼𝘃𝗲𝗿 𝘁𝗶𝗺𝗲, retrain when needed, and keep the system adaptive.

𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀
What I’ve learned is this: 𝗔 𝗴𝗼𝗼𝗱 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹 𝗶𝘀𝗻’𝘁 𝗷𝘂𝘀𝘁 𝘀𝗺𝗮𝗿𝘁 -𝗶𝘁’𝘀 𝘂𝘀𝗲𝗳𝘂𝗹, 𝗱𝗲𝗽𝗲𝗻𝗱𝗮𝗯𝗹𝗲, 𝗮𝗻𝗱 𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗿𝗲𝗮𝗹 𝗴𝗼𝗮𝗹𝘀.
This workflow has helped me connect the dots between technical skills and real-world impact and it’s a big step in my AI/ML learning journey. I'm excited to keep building, exploring, and learning how to use ML to solve meaningful problems.
Keep Learning!