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Is there an unspoken glass ceiling for professionals in AI/ML without a PhD degree?

In the search for Machine Learning Engineer (MLE) roles, it’s becoming evident that a significant portion of these positions — though certainly not all — appear to favor candidates with PhDs over those with master’s degrees. LinkedIn Premium insights often show that 15–40% of applicants for such roles hold a PhD. Within large organizations, it’s(Read More)

In the search for Machine Learning Engineer (MLE) roles, it’s becoming evident that a significant portion of these positions — though certainly not all — appear to favor candidates with PhDs over those with master’s degrees. LinkedIn Premium insights often show that 15–40% of applicants for such roles hold a PhD. Within large organizations, it’s also common to see many leads and managers with doctoral degrees.

This raises a concern: Is there an unspoken glass ceiling in the field of machine learning for professionals without a PhD? And this isn’t just about research or applied scientist roles — it seems to apply to ML engineer and standard data scientist positions as well.

Is this trend real, and if so, what are the reasons behind it?

Last reply by Miley. 0 0 1112 1 year ago

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Julian

May 15, 2025

When does feature engineering become data leakage?

I recently saw a discussion where a model achieved surprisingly high validation performance, only to fail badly in production. The root cause wasn’t the algorithm, it was feature engineering. Some of the engineered features were technically available during training but relied on information that wouldn’t exist at prediction time. The line between clever feature engineering(Read More)

I recently saw a discussion where a model achieved surprisingly high validation performance, only to fail badly in production.

The root cause wasn’t the algorithm, it was feature engineering.

Some of the engineered features were technically available during training but relied on information that wouldn’t exist at prediction time.

The line between clever feature engineering and subtle leakage isn’t always obvious.

How do you evaluate whether a feature is introducing future information, target leakage, or unrealistic signals?

Have you encountered cases where leakage survived code reviews and validation checks?

0 1 83 4 weeks ago

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Nicola

September 30, 2026

Anyone dealt with a churn model overfitting after retraining?

Looking for some advice from people who’ve run into this before. I retrained a customer churn model using the latest six months of data and saw a noticeable jump in training performance, but validation metrics dropped significantly. The model now performs extremely well on the training set and much worse on unseen data. I’ve already(Read More)

Looking for some advice from people who’ve run into this before.

I retrained a customer churn model using the latest six months of data and saw a noticeable jump in training performance, but validation metrics dropped significantly. The model now performs extremely well on the training set and much worse on unseen data.

I’ve already checked for data leakage and haven’t found anything obvious. Feature engineering hasn’t changed much, although customer behavior has shifted slightly over the last quarter.

Current setup:
• Binary churn prediction
• Gradient boosting model
• Monthly retraining cycle
• Class imbalance handled through weighting

Has anyone experienced something similar after retraining? What would you investigate first—data drift, feature importance changes, retraining window, regularization, or something else?

Would appreciate any suggestions or lessons learned.

0 3 101 4 weeks ago

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Erin

September 30, 2026

Will Small Language Models (SLMs) become the next big machine learning breakthrough?

For years, the race in machine learning has focused on building larger and more powerful models. But recently, there’s been growing interest in Small Language Models (SLMs) that can run faster, cost less, and operate on local devices while still delivering strong performance for specific tasks. For many organizations, the question is no longer whether(Read More)

For years, the race in machine learning has focused on building larger and more powerful models. But recently, there’s been growing interest in Small Language Models (SLMs) that can run faster, cost less, and operate on local devices while still delivering strong performance for specific tasks.

For many organizations, the question is no longer whether they need the biggest model.

It’s whether they need the most efficient one.

SLMs are opening new possibilities for edge AI, privacy-sensitive applications, real-time inference, and cost-effective deployment.

But there are still trade-offs around reasoning, generalization, and scalability.

I’m curious what the community thinks:

Are SLMs the next major shift in machine learning, or will larger foundation models continue to dominate most real-world applications?

0 2 131 2 months ago

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Tom Zerega

September 2, 2026

How do you handle concept drift without frequent retraining?

I’m maintaining a machine learning model that’s deployed in production, and over the past few months I’ve noticed a gradual decline in performance. The data distribution has shifted, but not enough to justify retraining the model every few days. Right now, I’m monitoring prediction confidence and basic evaluation metrics, but I’m unsure when retraining should(Read More)

I’m maintaining a machine learning model that’s deployed in production, and over the past few months I’ve noticed a gradual decline in performance. The data distribution has shifted, but not enough to justify retraining the model every few days.

Right now, I’m monitoring prediction confidence and basic evaluation metrics, but I’m unsure when retraining should actually be triggered.

Here’s a simplified version of the monitoring logic:

from evidently.report import Report

report = Report(metrics=[
    DataDriftPreset(),
    ClassificationPreset()
])

report.run(
    reference_data=train_df,
    current_data=production_df
)

if drift_score > threshold:
    retrain_model()

I’m curious how teams handle this in production.

Do you rely on statistical drift detection alone, or do you also monitor business KPIs?
How do you distinguish between temporary distribution shifts and genuine concept drift?
Have you had success with online learning, rolling retraining windows, or champion/challenger models?

I’d love to hear how experienced ML engineers balance model stability with keeping predictions accurate over time, especially in high-volume production environments.

0 4 269 3 months ago

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Arindam

July 30, 2026

Why do AI-generated product images still get flagged as AI after post-processing?

I’ve been experimenting with AI-generated product images and even after applying post-processing techniques like sharpening, color correction, and minor retouching, some detection tools still flag the images as AI-generated. I’m curious why these models can still detect AI images and whether there are reliable ways to make AI-generated visuals appear more natural while maintaining quality.

I’ve been experimenting with AI-generated product images and even after applying post-processing techniques like sharpening, color correction, and minor retouching, some detection tools still flag the images as AI-generated.

I’m curious why these models can still detect AI images and whether there are reliable ways to make AI-generated visuals appear more natural while maintaining quality.