Day 86 Fast: Common AI Challenges – The 12 Bugs That Break Everyone

The day every AI engineer fears: the 12 most common failures that silently destroy projects. We break your Day 85 app on purpose — then fix everything. Overfitting, data leakage, vanishing gradients, class imbalance… nothing is safe.
Tomorrow Day 87: full debugging masterclass!

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Transcript

00:00Your model gets 100% on training data but fails on new images.

00:03Classic overfitting, happens to every beginner.

00:06My day 80 model memorized the training set, looked perfect, was useless.

00:12Train accuracy 99%, validation 60%, red flag.

00:17You accidentally let test data leak into training, model cheats and you don't notice until production.

00:22I once got 99.9% accuracy because the same images were in both sets.

00:28The most dangerous because it looks like success.

00:32I'll create leakage live, then show how to detect it.

00:35Your deep network stops learning after layer 3, gradients become zero, common with sigmoid.

00:40My 10-layer model learned nothing, spent 3 days crying.

00:45ReLU and proper initialization fix this.

00:48I'll show a broken sigmoid model versus working real you.

00:51Ethan, bring my gradients back.

00:5399% of your sentiment data is neutral.

00:56Model just predicts neutral and gets 99% accuracy but is useless.

01:01My fraud detection model said no fraud every time.

01:0499.9% accurate, completely broken.

01:07Accuracy is meaningless.

01:09Look at F1's score.

01:10I'll create a 99.1 dataset and break the model live.

01:14Anastasia, save the minority class.

01:17Your loss suddenly stops moving or explodes to NAN, classic gradient nightmare.

01:21I once trained for 6 hours and got NAN at Epoch 3, cried real tears.

01:27Too high learning rate, exploding.

01:29Sigmoid plus deep nets, vanishing.

01:32I'll change the learning rate live, watch it explode, then fix with 0.0001.

01:37Anastasia, bring my gradients back to life.

01:39You split by random but same patient appears in train and test.

01:43Instant leakage.

01:44I did this with medical data, 99% accuracy, completely fake.

01:48Always split by patient ID, timestamp, or stratified.

01:52I'll break the day 85 app live with bad split fix with stratified shuffle split.

01:56Anastasia, save me from fake accuracy.

01:5810% of your training data has wrong labels, model learns garbage.

02:04I was trained on CIFAR 10 with swapped cat-dog labels, hilarious disaster.

02:08Real datasets are 5 to 15% noisy.

02:12You must handle it.

02:14I'll flip 10% of labels live, watch accuracy drop from 75% to 60%.

02:1899% accuracy sounds amazing, until you realize it predicts no fraud every time.

02:24My fraud model had 99.9% accuracy and caught zero frauds.

02:30Accuracy lies when classes are imbalanced.

02:31Always use F1, AUC, or precision slash recall.

02:35Your data is accidentally sorted by label.

02:37First 90% of every batch is negative.

02:40I did this for three days.

02:41Model only learned the first class.

02:43Always shuffle with shuffle equals true, or shuffle dataset.

02:47I'll sort the day 85 data live.

02:49Watch it learn only one sentiment.

02:51You forgot to normalize pixel values.

02:53Some features 0255, others 01.

02:56Optimizer goes crazy.

02:58My model wouldn't train at all, until I scaled.

03:01Four hours wasted.

03:02Always normalized to similar ranges.

03:04Standard scaler, or slash 255.

03:07I'll remove scaling from day 85 image model.

03:09Watch training die.

03:11Anastasia, scale me properly.

03:13Your model was perfect in January.

03:15By June it's useless because the world changed.

03:18My sentiment model hated new slang.

03:21Accuracy dropped 20% in three months.

03:24Monitor predictions and retrain regularly.

03:26Concept drift is inevitable.

03:28I'll simulate six months of drift live.

03:30Watch accuracy collapse.

03:31Anastasia, keep my model young forever.

03:34Your beautiful model is 300 membros.

03:36Crashes on phones and costs 100 month to serve.

03:39My first mobile app took eight seconds to load.

03:42Users deleted it.

03:43Quantization, pruning, distillation, reduce size 10 times with less than 1% accuracy loss.

03:49I'll quantize day 85 model from 120 megabytes to 12 megabytes live.

03:54Ethan, make me lightweight and fast.

03:56First user after deploy waits 45 seconds while model loads.

03:59They leave forever.

04:00My streamlit app was perfect.

04:02Except the first person always bounced.

04:05Pre-warm the model or use lazy loading with spinner.

04:07I'll show cold start right pointing arrow add street spinner and pre-load trick.

04:12Anastasia, warm me up instantly.

04:14User uploads 100 mem by image.

04:16Streamlit eats all memory and crashes for everyone.

04:19I killed my shared app with one big photo.

04:21Felt terrible.

04:23Resize images early.

04:24Use ST cache wisely.

04:25Limit upload size.

04:27I'll upload a 50 megabytes image live.

04:29Watch it die.

04:30Then fix with resize.

04:31Ethan, don't let me crash the party.

04:33You used binary cross entropy instead of categorical.

04:37Model becomes arrogantly overconfident.

04:40My sentiment model said, I'm 100% sure on everything.

04:43Total clown.

04:44Always match lost output.

04:46Binary versus categorical versus focal.

04:49I'll swap the loss live.

04:50Watch confidence go from 70% to 99.999%.

04:54Ethan, teach my model some humility.

04:57Your model learns fast at first, then stops dead.

04:59No learning rate decay.

05:00I trained for 50 epochs and wasted the last 40.

05:04Classic.

05:05Reduce LR on Plateau or Cosine Decay, essential for deep nets.

05:10I'll add Reduce LR on Plateau Live.

05:12Watch it suddenly start learning again.

05:13Anastasia, make my model keep improving forever.

05:17Your validation loss jumps up and down like crazy

05:19because validation data isn't shuffled.

05:21I thought my model was drunk.

05:23Turns out it was just the validation order.

05:26Always shuffle both train and validation every epoch.

05:28I'll turn off validation shuffle.

05:30Watch the chaos, then fix it.

05:32Ethan, sober up my validation.

05:35Your app is perfect locally.

05:36Deploy and it's 500 error city.

05:38The ultimate betrayal.

05:39I've cried at 2 a.m.

05:41Because of this exact curse.

05:42Every developer's nightmare.

05:44Requirements.txt, Docker, or exact environment replication.

05:47No excuses.

05:48I'll show local success, right-pointing arrow production,

05:52fail, right-pointing arrow fix with exact requirements.txt plus Docker.

05:56Anastasia, make it work everywhere.

05:58I'm tired of this curse.

05:59You thought your test set was clean,

06:01but 5% of images appear in training with different labels.

06:04I got 98% accuracy.

06:07Felt like a genius until I discovered the overlap.

06:10Use hashing or image similarity checks before training.

06:14I'll inject 5% duplicates live.

06:16Watch accuracy lie through its teeth.

06:18Sophia, clean my dirty test set.

06:21Your sentiment model cuts reviews at 200 words,

06:23loses the punchline every time.

06:25My 500-word movie review became I loved, suddenly negative.

06:30Truncate from the end or use sliding windows.

06:33I'll truncate from start versus end, watch sentiment flip.

06:36Your predictions are different every time,

06:38because batch norm is still in training mode.

06:41My app was literally random, terrifying.

06:44Model.eval in PyTorch, or compile with correct mode in TF.

06:48I'll forget to freeze batch norm.

06:50Watch predictions dance.

06:52You upgrade one package, suddenly five others break.

06:54Welcome to dependency hell.

06:56I once spent 8 hours fixing Numpy plus TensorFlow version war.

07:01Pin exact versions, TensorFlow equals equals 2.15.0, Numpy a 1.24.

07:06I'll show a working app, upgrade Numpy, total crash, fix with exact pins.

07:12Your app works fine for 10 users.

07:14By user 100, it crashes from GPU memory leak.

07:17My hugging face space died after two hours.

07:19So embarrassing.

07:21Clear session, delete variables.

07:23Use tf.keras.backend.clear underscore session.

07:25I'll run 200 predictions.

07:28Watch memory explode, then fix with clear session.

07:31You run the same code twice, different accuracy.

07:34No one can reproduce your work.

07:36My boss asked for reproducible results.

07:38I had nothing.

07:40Set all seeds, Python, number py, TensorFlow, and tf.deterministic ops.

07:45I'll run without seeds, different results.

07:47Add four lines, identical every time.

07:50Your images are float64 instead of float32, 10x slower and eats all RAM.

07:55My app was dog slow.

07:57Turns out it was data type.

07:59Always use float32 for images, and eight for quantized models.

08:03I'll change to float64.

08:05Watch it crawl, then fix with S type, float32.

08:09Your app is perfect locally.

08:10Deploy in its 500 error city, the ultimate betrayal.

08:13I've cried at 2 a.m. because of this exact curse.

08:16Requirements.txt, docker, or exact environment replication.

08:20No excuses.

08:22I'll show local success.

08:23Production fail.

08:24Fix with exact requirements.

08:26Txt.

08:27You now have the complete survival kit.

08:30No more blind failures.

08:32These 12 fixes saved my career multiple times.

08:36Professional AI engineers master these exact issues.

08:40You're no longer a beginner.

08:42You're battle tested.

08:43Next time something breaks, run through our 12-question checklist.

08:4995% of issues are covered.

08:52I printed this flowchart.

08:54It's above my desk.

08:57Tomorrow, day 87.

08:59Full debugging master class with this exact system.

09:0365% of not parfait.

09:0561% of knowledge.

09:05Do!

09:0695% ofowanie.

09:06diu.

09:0786% ofias.

09:08The time.

09:08I printed this weather.

09:09From 787.

09:09The time.

09:10By the.

09:10ə.

09:12The Air Erik.

09:14The Air mortality.

09:14Yeah!

09:15The Big Four.

09:16Is wrong!

09:1683% of upright.

09:16It's the helfen.

09:16I printed this bug.

09:17ously well switchedisonfully.

09:18Number seven.

09:19We didn't.

09:19Look.

09:20They daddy's bored.

09:20About 6-6 ia.

09:21Yong caughtamento.

09:22Enemy Bettler.

09:23Batters.

09:23In direct.

09:23Night.

09:24하기悪at.

09:24bow al Stuff Marshall.

09:25App 있습니다 breeze.

09:25deeper in down.

09:26ch counselor.

09:26ичесcincks t quest.

Day 86 Fast: Common AI Challenges – The 12 Bugs That Break Everyone | #DailyAIWizard

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