Comment avez-vous évité les pièges courants du Machine Learning ?

For specialized domains like medical datasets, fraud detection datasets etc, it is worth consulting a domain expert when assessing data quality.

One thing I have found helpful is to understand the data thoroughly with respect to the business objective. For achieving the same, I have worked with SME to work on the statistics of the data. Though there are techniques to understand the underfitting and overfitting of the model from the model performance but it helps a lot if we can collect quality data covering edge cases and not focused only on quantity of the data.

One of the most relevant factors for a successfully project in machine learning is the quality of data. For me as data scientist data quality means: - the data has to be the actual data (you need to start always from raw data) to see how actually the things looks like. - the history data needs to make sense from multiple points of view as: business, statistics and modeling. - the data outliers has to be meaning somehow and you need to assign them some events and to understand how they has been generated - the missing values has to be understand if there is null values or nan values (no records because there is no events or there is events but there are not records) - start with a EDA for data understanding of your raw data.

In the intricate dance of machine learning, data quality takes the lead. A meticulous tango with your data involves uncovering errors, outliers, and missing values. A waltz through exploratory data analysis unveils distribution secrets, while preprocessing techniques perform a cha-cha of cleaning, scaling, and transforming—ensuring your models samba gracefully with accuracy and avoid the missteps of bias or overfitting.

AutoML can also reduce the risk of choosing an ill-suited model, optimizing for performance across various dimensions. But AutoML, while efficient, can sometimes favor complexity over simplicity. At the end of the day, nothing beats good ol' domain expertise in choosing the right model.

Model selection and data processing can interact. For instance scaling data is generally not impactful for models that are built on decision trees.

I have found it extremely helpful to ensure the train and test distributions are similar especially in cases where cross validation is not an option (due to resource constraints). Stratified split is a good way to ensure that the distributions are similar thus providing a good estimate of the models performance on unseen data.

Data leakage is a pitfall when creating training and validation and test sets. One way to avoid this is to complete steps like scaling after splitting data.

Monitor both your model and the data it processes. Watch out for both data drift and operational drift, the latter potentially impacting enterprise infrastructure as opposed to model prediction performance.

Deploying a model is like launching a ship—it's not just about the maiden voyage. Before unleashing your model into the wild, test it in various environments and scenarios. Continuous monitoring, akin to navigating stormy seas, is essential to catch performance issues. Regular updates based on feedback ensure your model stays shipshape. Documenting the model's journey is the compass for future navigators, providing clarity through design, code and results. Smooth sailing in model deployment requires a vigilant and well-documented approach.

Steer clear of the ethical pitfall in machine learning by embracing a sneaky ally – conscience. While crafting models, sneak a peek into the ethical realm. Infuse fairness, accountability, transparency, privacy, and security into your model's DNA. Don't just build; concoct responsibly. Consult with the ethical experts, regulators, and users to ensure your model isn't just intelligent but virtuously smart too.

A good way to put your work out there is to make your work publicly accessible on GitHub and post it on social media. Contributing to open-source work is a great way to work on a meaningful project while getting good feedback from experienced engineers.

In the realm of machine learning, don't just build a model—craft a perpetual learning journey. Avoid stagnation by inviting critiques and insights from peers and mentors. Stay ahead by devouring the latest trends and research, and boldly tackle fresh challenges and techniques. Embrace mistakes as stepping stones, making every failure a lesson in your ever-evolving mastery of machine learning.

In navigating uncharted territories of examples and stories, consider the unspoken nuances that defy conventional categories. Delve into unexplored corners, where insights lurk in unexpected narratives. To sidestep Machine Learning pitfalls, embrace curiosity: question assumptions, seek diverse datasets, and cultivate a relentless spirit of experimentation, uncovering gems where others might find stumbling blocks.