ML Case-study Interview Question: Unified Multitask Learning for Diverse Machine Learning Tasks

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Case-Study question

A global online platform has several machine learning tasks involving user interactions with job listings, recruitment data, and skill extraction from text. These tasks each have separate training pipelines and distinct labeled datasets. The platform wants to combine these tasks under a single multitask learning framework. Different data distributions, separate feature sets, and diverse model architectures create major complexity. The platform’s objective is to boost model performance and reduce cold-start issues by sharing learned parameters and representations across tasks. How would you design and implement a multitask learning solution to handle these heterogeneous tasks while ensuring gains in their overall performance metrics?

Detailed solution

Different tasks may have non-overlapping data distributions, features, and model architectures. Multitask learning addresses these challenges by building shared components that capture generalized representations, along with task-specific components for domain-specific refinements. Training can happen jointly or iteratively. Either approach can unify signals from related tasks and transfer useful knowledge to tasks with scarce training data.

Task unification

Different tasks may only share a small set of features. Some tasks might have unique domain-specific features. A unified input schema can handle sparse or missing features by filling them with default placeholders. A shared feature encoder extracts general information, while additional task-specific encoders handle unique features. This unification supports synergy between domains such as job search, skill extraction, and recruiter search.

Shared and task-specific architectures

A single model structure can be split into a shared bottom and task heads. Shared layers learn broad patterns. Each task head tailors the output to its loss function and domain. Some tasks use a classification head with cross-entropy loss. Others might use regression or ranking losses. Training them together improves generalization because the shared bottom sees more varied data.

Joint vs iterative training

Joint training merges data from all tasks into combined batches. The overall loss is a weighted sum of each task loss. Iterative training updates one task at a time in a round-robin manner. Joint training helps when tasks need to align on the same batches, such as knowledge distillation scenarios. Iterative training may be easier if tasks have significantly different data distributions or frequency.

Here, L_t is the loss for task t, alpha_t is the weight for task t, and T is the number of tasks. Each alpha_t can be tuned or found via hyper-parameter search. If tasks have differing scales or importance, the weighting can be critical. Hyper-parameters like learning rates and batch sizes can be specific to each task to avoid interference.

Example code snippet

import torch
import torch.nn as nn
import torch.optim as optim

class SharedBottom(nn.Module):
    def __init__(self, input_dim, shared_dim):
        super(SharedBottom, self).__init__()
        self.shared_layers = nn.Sequential(
            nn.Linear(input_dim, shared_dim),
            nn.ReLU(),
            nn.Linear(shared_dim, shared_dim),
            nn.ReLU()
        )
    def forward(self, x):
        return self.shared_layers(x)

class TaskHead(nn.Module):
    def __init__(self, shared_dim, output_dim):
        super(TaskHead, self).__init__()
        self.task_layers = nn.Sequential(
            nn.Linear(shared_dim, output_dim)
        )
    def forward(self, shared_output):
        return self.task_layers(shared_output)

shared_model = SharedBottom(input_dim=200, shared_dim=64)
task1_head = TaskHead(shared_dim=64, output_dim=1)
task2_head = TaskHead(shared_dim=64, output_dim=1)

optimizer = optim.Adam([
    {'params': shared_model.parameters(), 'lr': 1e-3},
    {'params': task1_head.parameters(), 'lr': 1e-3},
    {'params': task2_head.parameters(), 'lr': 1e-3}
])

for epoch in range(epochs):
    # Example joint training
    for (x1, y1), (x2, y2) in dataloader_joint:
        out_shared_1 = shared_model(x1)
        out_shared_2 = shared_model(x2)

        out1 = task1_head(out_shared_1)
        out2 = task2_head(out_shared_2)

        loss1 = loss_fn(out1, y1)
        loss2 = loss_fn(out2, y2)

        # Weighted sum of losses
        loss_total = alpha1*loss1 + alpha2*loss2

        optimizer.zero_grad()
        loss_total.backward()
        optimizer.step()

The shared model captures general embeddings from shared input features. Task heads add domain-specific transformations. For iterative training, use separate task loaders in a round-robin manner. Joint training merges data into combined batches.

Practical applications

Skill extraction tasks and job-application tasks share contextual text embeddings to interpret domain-specific tokens. The shared embeddings learn language features across multiple tasks, improving classification accuracy when data is sparse. Recruiter-candidate affinity tasks can also benefit from job search signals and user activity features, producing better user-company embeddings that generalize beyond a single product domain.

Performance gains

Multitask learning typically shows improvements for tasks with limited labeled data. When tasks are related, the shared component helps them learn a richer representation. An internal A/B test comparing single-task vs multitask setups might show higher click-through rate and user engagement metrics.

Follow-up question: How do you handle data imbalance across tasks?

Different tasks might have skewed labels, such as a small fraction of positive events. Down-sampling or up-sampling can control the class ratios. Task-specific weighting factors alpha_t in the total loss function L_total can be tuned to avoid overshadowing of minority labels. Regularization and careful sampling are common strategies when tasks have widely different scales.

Follow-up question: How do you manage feature drift when tasks evolve separately?

Maintain a unified data schema that accepts all features relevant across tasks. Mark missing fields as zero or placeholders. Retrain or incrementally train the shared model on new distributions. A schema registry can track changes. If tasks diverge strongly, consider partial sharing or updating the architecture to keep a stable shared core while letting each task head adapt.

Follow-up question: How do you interpret task interactions in a shared model?

Visualize or log hidden-layer activations and gradients for each task. Identify any interference where one task’s gradients conflict with another’s. Use gradient-balancing techniques if necessary. Examine error curves separately to see if shared layers remain beneficial or if tasks need deeper task-specific branches.

Follow-up question: Could negative transfer happen?

Yes. Negative transfer occurs when learning multiple tasks together hurts certain tasks’ performance. Deep networks might overfit to tasks with abundant data and fail to serve small tasks. Monitoring validation metrics for each task is essential. Techniques like iterative training or advanced task-weight scheduling can address negative transfer.

Follow-up question: How would you scale to hundreds of tasks?

Store metadata about which tasks share data distributions or features. Group tasks likely to benefit from shared representations. Automated clustering or correlation analysis of label distributions can discover beneficial groupings. A large-scale multitask model with a huge shared bottom might become too general. A hierarchical approach or mixture-of-experts can keep tasks from interfering with each other.

Follow-up question: How do you decide between joint or iterative training?

Choose joint training when tasks must use the same batch and have strong dependence in losses, such as knowledge distillation or tasks with an explicit combined objective. Use iterative training for tasks that share few features or have different label distributions. Experiment with both and compare overall performance using each approach’s hyper-parameter tuning.

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