Top 6 ML Engineer Interview Questions (with Snapchat MLE)

Summary

In this insightful interview, machine learning engineer Raj from Snapchat discusses fundamental concepts such as training and testing data, hyperparameter tuning, and optimization algorithms like batch gradient descent. He addresses the challenges of non-convex loss functions, the importance of feature scaling, and the distinction between classification and regression. Raj also shares practical insights on model deployment, monitoring for concept drift, and strategies to handle exploding gradients, emphasizing the importance of domain-specific considerations in machine learning.

Takeaways

• 📘 Training data is the portion of data used by a machine learning algorithm to learn patterns, while testing data is unseen by the algorithm and used to evaluate its performance.
• 🔧 Hyperparameters, such as the number of layers or learning rate in a neural network, are tuned using a validation set, which is a part of the training data.
• 🔍 The final model evaluation is performed on the test data set, which should not influence the learning process or hyperparameter tuning of the model.
• 🛠 Gradient descent optimization techniques include batch gradient descent, mini-batch gradient descent, and stochastic gradient descent, each with different approaches to updating model parameters.
• 🧩 Batch gradient descent uses the entire training set for each update, mini-batch gradient descent divides the training set into smaller groups, and stochastic gradient descent involves random shuffling and smaller batches.
• 🔄 The choice between different gradient descent techniques often depends on memory requirements and the desire to introduce noise to prevent overfitting.
• 🏔 Optimization algorithms do not guarantee reaching a global minimum in non-convex loss functions, often settling in a local minimum or saddle point.
• 🔄 Feature scaling is important for algorithms that use gradient-based updating, as it helps to stabilize and speed up convergence by normalizing different scales of features.
• 🔮 Classification predicts categories, while regression predicts continuous values; the choice depends on the nature of the outcome variable and the problem context.
• 🔄 Model refresh in production is triggered by a degradation in performance, which can be monitored through various metrics and by comparing with the training set performance.
• 🔁 Concept drift is a common reason for performance degradation in production, where the relationship between input features and outcomes changes over time.
• 💥 Exploding gradients in neural networks can be mitigated by gradient clipping, batch normalization, or architectural changes like reducing layers or using skip connections.

Q & A

What is the purpose of training data in machine learning?

Training data is used by a machine learning algorithm to learn patterns. It helps in choosing the parameters of the model, such as those of a logistic regression algorithm, to minimize error on the training set.

Why is testing data important in machine learning?

Testing data is crucial as it is data that the algorithm has not seen before. It is used to evaluate the performance of the model without bias, ensuring that the model's performance is gauged on data other than what it was trained on.

What are hyperparameters in the context of machine learning?

Hyperparameters are parameters that are not learned from the data but are set prior to the training process. They include aspects like the number of layers in a neural network, the size of the network, or the learning rate. They are tuned using a validation set to maximize performance.

How does the validation set differ from the training set and test set?

The validation set is a portion of the training data used to tune hyperparameters. It is not used in the learning process of the algorithm but to adjust the model's hyperparameters. The training set is used to learn the model, and the test set is used to evaluate the final model's performance.

What is the difference between batch gradient descent, mini-batch gradient descent, and stochastic gradient descent?

Batch gradient descent uses the entire training set to compute the gradient and update parameters at once. Mini-batch gradient descent divides the training set into smaller batches and updates parameters using each mini-batch. Stochastic gradient descent shuffles the training set and updates parameters using small random batches, introducing more randomness.

Why might one choose to use mini-batch gradient descent over batch gradient descent?

Mini-batch gradient descent can be chosen over batch gradient descent due to memory requirements, as it allows for the processing of smaller subsets of data that can fit into RAM or a GPU. It also adds noise to the gradient computation, which can act as a regularizer and help prevent overfitting.

Are optimization algorithms guaranteed to find a global minimum for non-convex loss functions?

No, optimization algorithms are not guaranteed to find a global minimum for non-convex loss functions. They often converge to a local minimum or a saddle point, which may still be a good solution depending on the performance on validation and test sets.

Why is feature scaling important in machine learning?

Feature scaling is important because it helps in normalizing the range of independent variables or features of data. This ensures that the features contribute equally to the result and helps in faster convergence of gradient-based machine learning algorithms.

What is the difference between classification and regression in machine learning?

Classification predicts a discrete outcome, often a category such as yes or no, while regression predicts a continuous numerical value. The choice between them depends on the nature of the problem and the type of outcome variable being predicted.

How can you tell when it's time to refresh a machine learning model in production?

A model may need to be refreshed when its performance degrades, which can be detected by monitoring metrics like precision, recall, loss, or accuracy. If the performance in production does not match the training performance, it might be time to update the model.

What is concept drift, and how can it affect a machine learning model's performance?

Concept drift refers to changes in the relationship between input features and the outcome variable over time. This shift in the underlying data distribution can cause a model's performance to degrade as the assumptions it was trained on no longer hold true.

How can exploding gradients be managed during the training of neural networks?

Exploding gradients can be managed by gradient clipping, which limits the value of gradients to a certain threshold, or by using batch normalization to stabilize the gradients. Additionally, adjusting the network architecture, such as reducing the number of layers or using skip connections, can help mitigate this issue.