Day 86 Audio-Podcast: 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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1970s jazz, morning coffee, Python, TensorFlow, deploy model, Streamlit, FastAPI, Hugging Face Spaces, TensorFlow Serving, model deployment, AI web app, DailyAIWizard, computer vision, NLP, sentiment analysis, image classification

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Transcript

00:00Sexy Wizards, welcome to Day 86, the day we stop pretending everything works perfectly.

00:07After launching your beautiful app yesterday, today we face the 12 most common AI failures that happen to everyone.

00:15Coffee only on the last slide. You'll need it.

00:19We'll cover overfitting, data leakage, vanishing gradients, deployment disasters, all using your Day 85 app.

00:28Tomorrow, Day 87, Full Debugging Masterclass.

00:34I am ready to break Ethan's perfect models on purpose.

00:38These are the bugs that made me cry at 3 a.m., now we fix them together.

00:43Yo Wizards, Ethan here to show you how even my perfect code breaks, and how to fix it fast.

00:49Olivia reporting. I'll ask Anastasia the questions you're scared to ask.

00:5590% of AI projects never make it to production, not because of bad models, but because of these silent killers we'll cover today.

01:04I've been in the 90%. It hurts.

01:08Knowing these 12 issues separates hobbyists from professionals.

01:13I'll show you each one live, and the fix.

01:15These are the 12 challenges we'll crush today.

01:20Overfitting, data leakage, vanishing gradients, class imbalance, wrong metrics, and 7 more.

01:28I've hit every single one, sometimes in the same project.

01:33We'll show each with your Day 85 app, real examples, real fixes.

01:37Same dream team. Anastasia, Sophia, Irene moderating, Ethan and Sophia breaking and fixing code, Olivia asking the brutal questions.

01:51We've all been burned. Now we teach you to never get burned.

01:56Today is pure experience transfer.

01:58I'll show you my biggest failures, and how I survived.

02:02Anastasia, protect me from these bugs.

02:04Your model gets 100% on training data, but fails on new images.

02:09Classic overfitting. Happens to every beginner.

02:13My Day 80 model memorized the training set, looked perfect, was useless.

02:21Train accuracy 99%, validation 60%, red flag.

02:27You accidentally let test data leak into training.

02:31Model cheats, and you don't notice until production.

02:34I once got 99.9% accuracy, because the same images were in both sets.

02:43The most dangerous, because it looks like success.

02:46I'll create leakage live, then show how to detect it.

02:51Your deep network stops learning after layer 3.

02:55Gradients become zero, common with sigmoid.

02:58My 10-layer model learned nothing, spent 3 days crying.

03:04ReLU and proper initialization fix this.

03:08I'll show a broken sigmoid model versus working real you.

03:12Ethan, bring my gradients back.

03:1499% of your sentiment data is neutral.

03:19Model just predicts neutral and gets 99% accuracy, but is useless.

03:26My fraud detection model said no fraud every time.

03:3099.9% accurate.

03:32Completely broken.

03:34Accuracy is meaningless.

03:36Look at F1's score.

03:38I'll create a 99.1 dataset and break the model live.

03:43Anastasia, save the minority class.

03:47Your loss suddenly stops moving or explodes to NAN.

03:50Classic gradient nightmare.

03:52I once trained for 6 hours and got NAN at Epoch 3.

03:58Cried real tears.

04:00Too high learning rate.

04:02Exploding.

04:03Sigmoid plus deep nets.

04:06Vanishing.

04:07I'll change the learning rate live, watch it explode, then fix with 0.0001.

04:13Anastasia, bring my gradients back to life.

04:17You split by random but same patient appears in train and test.

04:21Instant leakage.

04:23I did this with medical data.

04:2599% accuracy.

04:27Completely fake.

04:29Always split by patient ID, timestamp, or stratified.

04:34I'll break the day 85 app live with bad split fix with stratified shuffle split.

04:40Anastasia, save me from fake accuracy.

04:4410% of your training data has wrong labels.

04:47Model learns garbage.

04:48I was trained on CIFAR 10 with swapped cat-dog labels.

04:53Hilarious disaster.

04:56Real data sets are 5 to 15% noisy.

05:00You must handle it.

05:03I'll flip 10% of labels live.

05:05Watch accuracy drop from 75% to 60%.

05:0899% accuracy sounds amazing, until you realize it predicts no fraud every time.

05:17My fraud model had 99.9% accuracy and caught zero frauds.

05:23Accuracy lies when classes are imbalanced.

05:26Always use F1, AUC, or precision slash recall.

05:30Your data is accidentally sorted by label.

05:34First, 90% of every batch is negative.

05:37I did this for 3 days.

05:39Model only learned the first class.

05:42Always shuffle with shuffle equals true, or shuffle dataset.

05:48I'll sort the day 85 data live.

05:50Watch it learn only one sentiment.

05:51You forgot to normalize pixel values.

05:55Some features 0255, others 01.

05:59Optimizer goes crazy.

06:01My model wouldn't train at all until I scaled.

06:054 hours wasted.

06:07Always normalize to similar ranges.

06:10Standard scaler, or slash 255.

06:14I'll remove scaling from day 85 image model.

06:17Watch training die.

06:19Anastasia, scale me properly.

06:20Your model was perfect in January.

06:25By June, it's useless because the world changed.

06:28My sentiment model hated new slang.

06:32Accuracy dropped 20% in 3 months.

06:36Monitor predictions and retrain regularly.

06:38Concept drift is inevitable.

06:41I'll simulate 6 months of drift live.

06:44Watch accuracy collapse.

06:46Anastasia, keep my model young forever.

06:48Your beautiful model is 300 membros, crashes on phones, and costs 100 months to serve.

06:56My first mobile app took 8 seconds to load.

07:00Users deleted it.

07:02Quantization, pruning, distillation, reduce size 10 times with less than 1% accuracy loss.

07:09I'll quantize day 85 model from 120 megabytes to 12 megabytes live.

07:15Ethan, make me lightweight and fast.

07:19First user after deploy waits 45 seconds while model loads.

07:23They leave forever.

07:25My streamlet app was perfect.

07:27Except the first person always bounced.

07:30Pre-warm the model or use lazy loading with spinner.

07:33I'll show cold start right-pointing arrow, add street spinner, and pre-load trick.

07:40Anastasia, warm me up instantly.

07:43User uploads a 100 memby image.

07:45Streamlit eats all memory and crashes for everyone.

07:49I killed my shared app with one big photo.

07:52Felt terrible.

07:54Resize images early.

07:56Use ST cache wisely.

07:57Limit upload size.

07:59I'll upload a 50 megabytes image live, watch it die, then fix with resize.

08:06Ethan, don't let me crash the party.

08:09You used binary cross-entropy instead of categorical.

08:13Model becomes arrogantly overconfident.

08:17My sentiment model said,

08:18I'm 100% sure on everything.

08:21Total clown.

08:23Always match loss to output.

08:25Binary versus categorical versus focal.

08:29I'll swap the loss live.

08:31Watch confidence go from 70% to 99.999%.

08:35Ethan, teach my model some humility.

08:39Your model learns fast at first, then stops dead.

08:43No learning rate decay.

08:45I trained for 50 epochs and wasted the last 40.

08:49Classic.

08:51Reduce LR on plateau or cosine decay.

08:54Essential for deep nets.

08:55I'll add Reduce LR on plateau live.

08:59Watch it suddenly start learning again.

09:02Anastasia, make my model keep improving forever.

09:06Your validation loss jumps up and down like crazy because validation data isn't shuffled.

09:12I thought my model was drunk.

09:15Turns out it was just the validation order.

09:18Always shuffle both train and validation every epoch.

09:22I'll turn off validation shuffle.

09:24Watch the chaos, then fix it.

09:27Ethan, sober up my validation.

09:30Your app is perfect locally.

09:32Deploy and it's 500 error city.

09:34The ultimate betrayal.

09:35I've cried at 2 a.m. because of this exact curse.

09:40Every developer's nightmare.

09:43Requirements.txt, Docker, or exact environment replication.

09:47No excuses.

09:50I'll show local success right-pointing arrow production.

09:53Fail right-pointing arrow fix with exact requirements.txt plus Docker.

09:58Anastasia, make it work everywhere.

10:00I'm tired of this curse.

10:02You thought your test set was clean, but 5% of images appear in training with different labels.

10:10I got 98% accuracy.

10:13Felt like a genius until I discovered the overlap.

10:17Use hashing or image similarity checks before training.

10:22I'll inject 5% duplicates live.

10:25Watch accuracy lie through its teeth.

10:28Sophia, clean my dirty test set.

10:30Your sentiment model cuts reviews at 200 words, loses the punchline every time.

10:37My 500-word movie review became I loved, suddenly negative.

10:44Truncate from the end or use sliding windows.

10:47I'll truncate from start versus end.

10:49Watch sentiment flip.

10:52Your predictions are different every time.

10:54Because batch norm is still in training mode.

10:57My app was literally random.

11:00Terrifying!

11:02Model.eval in PyTorch or compile with correct mode in TF.

11:08I'll forget to freeze batch norm.

11:10Watch predictions dance.

11:12You upgrade one package, suddenly five others break.

11:16Welcome to dependency hell.

11:17I once spent eight hours fixing NumPy plus TensorFlow version war.

11:25Pin exact versions.

11:26TensorFlow equals equals 2.15.0.

11:30NumPy a 1.24.

11:32I'll show a working app.

11:34Upgrade NumPy.

11:36Total crash.

11:37Fix with exact pins.

11:39Your app works fine for 10 users.

11:42By user 100, it crashes from GPU memory leak.

11:46My hugging face space died after two hours.

11:49So embarrassing.

11:51Clear session.

11:52Delete variables.

11:53Use tf.keras.backend.clear underscore session.

11:58I'll run 200 predictions.

12:00Watch memory explode.

12:02Then fix with clear session.

12:04You run the same code twice.

12:07Different accuracy.

12:08No one can reproduce your work.

12:11My boss asked for reproducible results.

12:14I had nothing.

12:16Set all seeds.

12:17Python, number py, TensorFlow, and tf.deterministic ops.

12:23I'll run without seeds.

12:25Different results.

12:26Add four lines.

12:27Identical every time.

12:30Your images are float 64 instead of float 32.

12:3410x slower and eats all RAM.

12:37My app was dog slow.

12:39Turns out it was data type.

12:42Always use float 32 for images and 8 for quantized models.

12:47I'll change to float 64.

12:50Watch it crawl.

12:51Then fix with ask type.

12:53Float 32.

12:55Your app is perfect locally.

12:57Deploy in its 500 error city.

12:59The ultimate betrayal.

13:00I've cried at 2 a.m.

13:02Because of this exact curse.

13:06Requirements.txt, docker, or exact environment replication.

13:10No excuses.

13:12I'll show local success.

13:14Production fail.

13:15Fix with exact requirements.

13:18Txt.

13:18You now have the complete survival kit.

13:22No more blind failures.

13:24These 12 fixes saved my career multiple times.

13:29Professional AI engineers master these exact issues.

13:32You're no longer a beginner.

13:34You're battle tested.

13:35Next time something breaks, run through our 12-question checklist.

13:4195% of issues are covered.

13:44I printed this flowchart.

13:46It's above my desk.

13:49Tomorrow, day 87.

13:51Full debugging masterclass with this exact system.

13:56Tonight, intentionally create three of these 12 failures in your day 85 app.

14:01Then fix them and send us proof.

14:04I want to see exploding memory and 100% fake accuracy.

14:10Document before and after.

14:12Best fixes featured tomorrow.

14:15Every single one of us has been stuck for days on these exact issues.

14:19Now you're immune.

14:21I still have the GitHub issue where I cried for three days.

14:24This is what set the rates juniors from seniors.

14:28You're now in the 10% who actually ship.

14:33You face the 12 most common AI failures and lived.

14:36Support us.

14:38HTTPS.

14:39We'll buymecoffee.com.

14:41Malta.

14:41DailyiWizard.

14:43Tomorrow, day.

14:4487.

14:45We become debugging gods.

14:47Incredibly proud.

14:49You're no longer beginners.

14:51You're survivors.

14:52See you tomorrow.

14:54Your AI can now survive the real world, darlings.

14:57Let's master debugging on day 87.

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

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