Why My ML Models Took Forever to Train—Until I Fixed This
Beginner mistakes cost hours in model training. Here’s how I fixed it with 3 simple changes.
Ever waited 7 hours for a basic ML model to train?
I have. It wasn’t a complex transformer. Just a movie recommender. Ratings in, genre tags out. Nothing fancy.
But training? Slower than dial-up. No bugs, no errors—just bad decisions.
What followed wasn’t a GPU upgrade. It was a humbling design correction that cut the time from 7 hours to 18 minutes.
This post breaks down the three fixes. None require advanced math. Just fewer assumptions and a bit of restraint.
The Model That Refused to Finish
The first version used a multi-layer neural net to recommend movies. Straightforward dataset—user ratings, genres, and a few tags.
Seven hours to train.
I assumed the issue was the usual suspects: Colab lag, missing GPU, too little RAM. But the problem wasn’t external. It was me.
Like most beginners, I overbuilt. I fed it raw, messy data. I ran full-dataset passes like I was training GPT-4, not testing a pet project.
Turns out, complexity isn't intelligence. It’s just noise if you don’t know what you’re solving.
So I simplified. And suddenly, everything moved faster—without losing accuracy.
Why ML Models Take Forever to Train (Even Simple Ones)
Most small ML projects shouldn’t feel like enterprise builds. Yet they often do. Here's why.
1. Overcomplicated Architectures for Simple Problems
I used a 6-layer neural network for what was essentially a classification task.
That’s like using a space shuttle to deliver your groceries.
Smaller models train faster, are easier to debug, and usually perform just fine for basic tasks.
What I should’ve done:
Started with fewer layers
Tuned with simple baselines
Checked validation loss early
Instead, the model kept adjusting thousands of weights that didn’t need to be there.
2. Feeding Raw, Untouched Data
I didn’t batch or normalize anything. Just threw it all in at once.
It backfired. A bottleneck at the very first layer. The model was constantly recalibrating because the data had wild ranges—from 1-star to 5-star reviews, from binary genre flags to multi-hot encoded tags.
One line of preprocessing—MinMaxScaler()—dropped my first-epoch loss by 40%.
A gentle reminder: data prep isn’t optional. It’s your model’s oxygen.
3. Training on the Entire Dataset, Every Time
No batching. No validation splits. Just... full data, every pass.
This is the ML equivalent of trying to learn French by reading the entire dictionary—daily.
Adding batching (batch_size=32) immediately slashed my training time. Accuracy actually improved. Turns out, models don’t like being overwhelmed either.
Here’s What Actually Worked
Let’s break them down properly—with what they are, why they work, and how to apply them even if you’re just starting out.
✅ Fix 1: Batch Your Input Data
Instead of sending 10,000 records at once, break them into chunks.
model.fit(X, y, batch_size=32, epochs=10)Why it helps:
Your model updates weights more often with smaller sets
Faster feedback loop
Works well on lower memory machines
Result: 7 hours → 1 hour. Model was more stable too.
✅ Fix 2: Preprocess and Normalize Everything
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
X_scaled = scaler.fit_transform(X)Why it helps:
Prevents the model from over-adjusting
Keeps input values in the same range
Improves convergence
Result: First-epoch loss fell from 1.8 to 0.7. Accuracy rose faster.
✅ Fix 3: Start Smaller Than You Think You Should
I rebuilt the model with only two dense layers:
model = Sequential()
model.add(Dense(16, activation='relu'))
model.add(Dense(1, activation='sigmoid'))Why it works:
Fewer weights to update
Easier to debug
Less risk of overfitting
Result: 90% faster training. Only 2% drop in accuracy.
What Happened Next?
The final model trained in under 20 minutes. And the predictions? Surprisingly sharp. Top-5 movie recommendations made sense.
More importantly, I stopped blaming my laptop.
It wasn’t the tools. It was how I used them.
TDLR: If It’s Slow, You’re Probably Doing Too Much
Three quiet killers of your training time:
Overcomplicated models
Raw, unprocessed data
Full-dataset passes without batching
Strip these out and your model won’t just train faster. It’ll make more sense.
Takeaway: Don’t start smarter. Start simpler—and train less like a show-off.
Next time your model drags for hours, ask: am I solving a problem, or just playing architect?
Conclusion: The Fastest Fixes Are Often the Simplest
The model didn’t need more power. It needed fewer assumptions.
Once I dropped the extra layers, cleaned the inputs, and added batching, everything improved—training time, accuracy, and sanity.
You don’t need cutting-edge hardware or weeks of fine-tuning. You just need to stop doing too much too early.
If your model’s still crawling, take a breath. Strip it back. Start small. Then build smarter.
Remember: The best models aren’t just accurate. They respect your time.
If this saved you even one wasted training hour,
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