Day 87 Audio-Podcast: Debugging AI Models – Fix Any Bug in Minutes

The day every AI engineer needs: the complete debugging toolkit. We break your Day 85 app with real bugs (vanishing gradients, memory leaks, dead layers) — then fix them in seconds using TensorBoard, the 7-step system, and pro tricks.
Tomorrow Day 88: real-world AI projects!

☕ Support our coffee vibe
https://buymeacoffee.com/dailyaiwizard

#1970sJazz #MorningCoffee #PythonForAI #TensorFlow #DeployAI #Streamlit #FastAPI #HuggingFace #ModelDeployment #DailyAIWizard #AIWebApp #ComputerVision #NLP

Tags:
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

Drop your live deployment link below — best ones get featured tomorrow on Day 85! 🚀

Transcript

00:00Sexy Wizards, welcome to Day 87, the day you stop crying at 3 a.m. because your model broke.

00:08After Day 86's 12 common failures, today we give you the complete Senior Engineer Debugging Toolkit.

00:15Coffee only on the last slide. This is pure gold.

00:20Sophia's back and ready to hunt bugs with Ethan.

00:23These are the exact tools that save my sanity and my job.

00:27We'll break and fix your Day 85 app live. By tonight, you'll debug faster than 99% of developers.

00:39Yo, Wizards. Ethan here with the sharpest debugging weapons. I'll break and fix your app in seconds.

00:46Olivia reporting.

00:49I'll ask Anastasia the questions you're afraid to ask.

00:5290% of AI engineers spend 80% of their time debugging, but they use print and hope.

01:00Today, you graduate to Pro Tools.

01:03I once spent three days on a bug that was one wrong line. Never again.

01:09Professional debugging equals systematic, fast, repeatable.

01:13This exact 7-step system finds 95% of bugs in under 10 minutes, used by every Senior AI Engineer.

01:23I printed this and taped it above my desk. Life-changing.

01:27We'll apply it live to your Day 85 app all day.

01:31I'll use it on every bug we create.

01:33Anastasia, Sophia, Irene moderating.

01:38Ethan and Sophia breaking and fixing live.

01:40Olivia asking the brutal questions.

01:43We've all lost sleep to bugs.

01:45Now we teach you to sleep like babies.

01:49Today is pure experience transfer.

01:52I'll create the worst bugs and fix them in seconds.

01:55TensorBoard is your MRI for neural nets.

01:59See gradients, weights, activations live.

02:02I found a dead layer in 30 seconds.

02:05Would have taken days without it.

02:08Log everything, loss, accuracy, weights, gradients.

02:13I'll add callbacks and show you a dying gradient live.

02:17GradCam shows exactly where your model is looking.

02:20Is it the cat or the carpet?

02:21My model was predicting cat because of the carpet.

02:26GradCam caught it instantly.

02:29Essential for computer vision debugging.

02:32I'll run GradCAM on your Day 85 image model live.

02:36Tef data bugs are silent.

02:39Wrong shuffling.

02:40No prefetch caching disasters.

02:43I trained for 12 hours because prefetch was off.

02:47Data starvation.

02:47Starvation.

02:49Always inspect your pipeline with .as underscore numpy underscore iterator.

02:54I'll show a broken pipeline fix with prefetch and cache.

02:58One command shows if your model is built wrong.

03:01Input, output shapes, param count.

03:04I once had a model with zero parameters.

03:07Summary, caught it.

03:09Always run model.summary after building.

03:12I'll break the Day 85 model shape live, then fix it.

03:16Add asserts to catch impossible values.

03:20Nan, negative probabilities, wrong shapes.

03:23I once had negative probabilities.

03:26Assert saved me.

03:29Fail fast, fail loud.

03:31I'll add asserts to Day 85.

03:33Watch them scream when I break it.

03:37Weights and biases logs everything.

03:39Compare 50 runs in one click.

03:41I found my best model from three weeks ago, Wand B Remembered.

03:47Free tier is amazing.

03:50I'll log your Day 85 training live.

03:53Your training is slow.

03:55Profiler shows exactly where.

03:57Data, GPU, CPU.

03:59I was blaming the model.

04:02It was tf.data the whole time.

04:06Built into TensorBoard.

04:07I'll profile your Day 85 app live.

04:12Shapp tells you exactly which words made it positive or negative.

04:16My model hated the word but.

04:19Shapp showed me.

04:21Explainable AI gold standard.

04:24I'll explain a wrong prediction live.

04:28Write tests for your pre-processing model output predictions.

04:32Never break silently again.

04:34I added tests after a disaster.

04:38Never going back.

04:40CI slash CD for AI.

04:43I'll write five tests for Day 85 app live.

04:47Automatically validate every new batch.

04:50Catch corrupted data before training.

04:52I once trained on all NAN images.

04:55Great expectations would have saved me.

04:59Data tests are as important as code tests.

05:01I'll add three expectations live.

05:06We start with your perfect Day 85 app, then inject eight real-world bugs one by one.

05:12I'll add vanishing gradients, data leakage, memory leaks, the works.

05:18Each bug is one of the 12 from Day 86.

05:21Now we fix them properly.

05:23I'll break it live, then we'll use the 7-step system to fix everything.

05:27Watch, we're about to destroy your beautiful app on purpose.

05:32I'm adding vanishing gradients first.

05:35Watch training stop.

05:37Each bug is real, happens in production daily.

05:40I'll inject them one by one, then we fix with today's tools.

05:45Loss stopped moving after epoch 3, classic vanishing gradients.

05:49I once wasted 12 hours on this exact curve.

05:55Sigmoid plus deep net equals death.

05:58Switch to re-LU.

06:01I'll change activation live.

06:03Watch it come back to life.

06:05Anastasia, save our baby.

06:0899.9% accuracy.

06:11But test images were in training.

06:13Classic leakage.

06:13I once celebrated this, then cried in production.

06:20Always check for overlap, image hashing, or ID split.

06:24I'll inject leakage fix with proper split.

06:28Look, layers 4, 8 weights aren't changing at all.

06:31Dead layers.

06:33I once had a 12-layer model with 9 dead.

06:37Tensorboard saved me.

06:39Wrong initialization for re-LU, using normal.

06:42I'll change init from glorot to he underscore normal.

06:46Watch them wake up.

06:49Anastasia, resurrect my dead layers.

06:52User uploads big images.

06:54Streamlit eats all memory and dies.

06:57My shared app crashed for everyone because of one user.

07:02Resize early.

07:03Limit upload size.

07:04Clear cache.

07:06I'll upload 100 embolder image.

07:09Fix with resize and Dell.

07:11Anastasia, don't let it explode.

07:15First user after deploy waits forever.

07:17They leave.

07:18My bounce rate was 80% because of cold start.

07:23Pre-warm model or show spinner with message.

07:27I'll add snake spinner and pre-load trick.

07:30Anastasia, warm me up instantly.

07:33Model says I'm 100% sure on everything.

07:36Used wrong loss.

07:37My app looked confident, but was totally wrong.

07:43Binary underscore cross entropy versus categorical.

07:46Huge difference.

07:48I'll swap loss live.

07:50Watch confidence become realistic.

07:52Same review.

07:54Different sentiment every refresh.

07:56Batch norm still in training mode.

07:58My app was literally random.

08:01Terrifying.

08:03Model dot trainable equals false or compile with training equals false.

08:07I'll freeze batch norm.

08:10Prediction's now identical.

08:12One weird input.

08:14Model outputs NAN and crashes the app.

08:17I once had NAN for 5% of users.

08:21Silent disaster.

08:23Add TF dot debugging dot check underscore numerics or clip underscore by underscore value.

08:28I'll trigger NAN, add check, graceful error.

08:34Open tensor board.

08:36Boom.

08:36We see vanishing gradients in layer 5.

08:3930 seconds instead of 3 days.

08:42I love this tool.

08:44This is why pros use tensor board on every project.

08:48I'll open it live.

08:50Watch the diagnosis.

08:52Anastasia, show me the magic.

08:54Change initialization from Glorot to He Normal.

08:59Dead layers instantly wake up.

09:01My 10-layer model went from 50% to 78% accuracy.

09:08Real Uniti initialization, always.

09:12I'll change it live.

09:14Watch the histogram explode with life.

09:17Add reduce LR on plateau.

09:19Model suddenly starts improving after Epoch 8.

09:22I thought I reached the limit.

09:25It was just the learning rate.

09:27Cosine decay or reduce LR on plateau.

09:30Essential for long training.

09:33I'll add the callback live.

09:35Watch it breathe new life.

09:38Loss went to NAN.

09:39Gradient clipping saves the day.

09:41My RNN was exploding every 5 Epochs.

09:45Clipping fixed it.

09:47Clip by norm or value.

09:49Prevents explosions.

09:50I'll add clip norm or 1.0.

09:54Watch stability return.

09:57Enable mixed precision.

09:58Same accuracy.

09:593x faster.

10:00Half the memory.

10:02My 24-hour training became 8 hours.

10:05Magic.

10:07TensorFlow does it automatically.

10:10I'll enable it live.

10:12Watch the speed.

10:14Fix the split.

10:15Accuracy drops from fake 99% to honest 78%.

10:18Now it's real.

10:20I finally got real numbers.

10:23Painful but honest.

10:25Stratified split.

10:27No overlap.

10:28Always.

10:30I'll fix the split live.

10:32Watch the truth emerge.

10:35Your complete debugging checklist.

10:37Print it.

10:37Save it.

10:38Live by it.

10:39I have this taped above my monitor.

10:42Saved me hundreds of hours.

10:44This plus tensor board plus the 7 steps equals unstoppable.

10:50Download link in description.

10:52Free forever.

10:54Every single one of us has lost days to these bugs.

10:57Now you're immune.

10:59I still have the slack message where I cried for 6 hours straight.

11:03This is what separates juniors from seniors.

11:08You're now in the 1% who actually debug fast.

11:13Take your Day 85 app.

11:14Inject 5 bugs from Day 86.

11:17Fix them using today's tools.

11:19Send proof.

11:20I want videos of exploding memory and vanishing gradients.

11:25Document your 7-step process.

11:28Best ones featured tomorrow.

11:29I'll share my broken version.

11:33Copy the chaos.

11:35From Day 1 Notebook to Day 87 Debugging Master, you are officially senior level.

11:41I'm so proud.

11:42Look at all these fixed apps.

11:45This is just the beginning.

11:47Tomorrow Day 88 Real World AI Projects.

11:51You crushed it.

11:52You mastered debugging.

11:55Support us.

11:55HTTPS, Osh, BuyMeACoffee.com, Daily iWizard, tomorrow day.

12:0088 We Start Real World AI Projects.

12:03Incredibly proud.

12:05You're now in the top 1% of AI engineers.

12:09See you tomorrow.

12:11Bugs run from you now, darlings.

12:14Let's build amazing things on Day 88.

12:16monkey music.

12:271

12:29210

12:30210

12:31160

12:3190

12:31200

12:32360

12:33900

12:34200

12:34220

12:34設 energy.

12:35180

12:36300

12:36200

12:37200

12:37300

12:38600

12:39200

12:39900

12:40140

12:40300

12:4246

12:42800

12:43400

12:43200

12:43400

12:44500

12:44300

12:44400

12:45400

12:45800

12:46300

Day 87 Audio-Podcast: Debugging AI Models – Fix Any Bug in Minutes | #DailyAIWizard

Category

Transcript

Be the first to comment

Recommended