Classical ML/DL Practitioner Curriculum
Subject: Classical ML/DL Practitioner Curriculum
17 chapters
1. 1 Supervised Learning
[Verse 1]
Data points scattered on the plane tonight
Linear regression draws the best fit line
Minimize the squares, that's our guiding light
Ridge adds L-two to keep the weights in line
Lasso brings L-one to make features sparse
Elastic Net combines them, playing both their parts
[Chorus]
Supervised learning, labels guide the way
Ridge Lasso Elastic, regularization stays
SVM margins, kernels transform space
Trees and forests, boosting sets the pace
From Bayes to k-NN, algorithms we embrace
Supervised learning finds the hidden patterns place
[Verse 2]
Support vectors mark the boundary clear
Maximum margins separate what's near
Kernel trick maps to dimensions high
Soft margins let some points slip by
SMO breaks the problem into pairs
Sequential minimal optimization cares
[Chorus]
Supervised learning, labels guide the way
Ridge Lasso Elastic, regularization stays
SVM margins, kernels transform space
Trees and forests, boosting sets the pace
From Bayes to k-NN, algorithms we embrace
Supervised learning finds the hidden patterns place
[Verse 3]
Decision trees split on features best
Information gain puts nodes to test
Bagging samples, builds a forest strong
Random features keep the trees from going wrong
Bootstrap aggregating reduces the variance
Multiple learners give us better performance
[Bridge]
XGBoost gradient boosting fast and true
Learning rates and subsampling too
LightGBM leaf-wise growing scheme
CatBoost handles categories clean
Monotonic constraints keep logic sound
Hyperparameter tuning makes performance bound
[Verse 4]
Naive Bayes assumes independence pure
Conditional probability makes predictions sure
K nearest neighbors votes from closest friends
Distance metrics show where similarity ends
Discriminant analysis finds the class divide
Linear quadratic boundaries decide
[Chorus]
Supervised learning, labels guide the way
Ridge Lasso Elastic, regularization stays
SVM margins, kernels transform space
Trees and forests, boosting sets the pace
From Bayes to k-NN, algorithms we embrace
Supervised learning finds the hidden patterns place
[Outro]
When the data speaks and labels show
These algorithms help the knowledge grow
From regression lines to boosted trees
Supervised learning holds the keys
2. 2 Unsupervised Learning
[Verse 1]
When data's got no labels to guide the way
No teacher telling right from wrong today
We dive into the patterns hiding deep inside
Unsupervised learning is our faithful guide
K-means draws circles, finds the center point
Groups similar data at each cluster joint
Initialize centroids, then watch them move
Until convergence finds the perfect groove
[Chorus]
Cluster, reduce, detect what's strange
Find the hidden patterns that the data can arrange
DBSCAN for the noise and density's call
PCA reduces dimensions, standing tall
Unsupervised learning, let the data speak
Find the structure that we seek
[Verse 2]
DBSCAN doesn't need to know the count
Density-based clusters, epsilon amounts
Core points, border points, and outliers too
Hierarchical dendrograms show us what to do
Agglomerative builds from bottom up
Divisive splits the data, fills the cup
Gaussian mixtures with the EM way
Expectation maximization saves the day
[Chorus]
Cluster, reduce, detect what's strange
Find the hidden patterns that the data can arrange
DBSCAN for the noise and density's call
PCA reduces dimensions, standing tall
Unsupervised learning, let the data speak
Find the structure that we seek
[Verse 3]
When dimensions overwhelm and curse us all
Principal components answer reduction's call
Eigenvalues dancing, variance explained
T-SNE preserves the neighbors, locally maintained
UMAP keeps both local and global too
Factor analysis finds the latent view
Manifold learning in a lower space
Visualization gives insights their place
[Bridge]
But what about the outliers standing alone?
Isolation forests leave them on their own
Random splits until they're isolated fast
One-class SVM draws boundaries that last
Autoencoders learn to reconstruct
Anomalies fail, their errors self-destruct
[Chorus]
Cluster, reduce, detect what's strange
Find the hidden patterns that the data can arrange
DBSCAN for the noise and density's call
PCA reduces dimensions, standing tall
Unsupervised learning, let the data speak
Find the structure that we seek
[Outro]
No labels needed when the patterns shine
Unsupervised methods work by design
From clustering groups to dimensions few
Anomalies detected, insights breaking through
3. 3 Model Selection & Evaluation
[Verse 1]
When your model seems perfect on training day
But test data makes it fall away
That's the bias-variance playing games with you
Let me break down what you need to do
Bias is the distance from the truth we seek
Variance shows how much predictions leak
When we change the data that we train upon
The decomposition keeps us strong
[Chorus]
Split and validate, stratify the classes
Group your data when the structure passes
Time series needs sequential flow
AUC-ROC and PR curves show
Cross-validate to find the way
Lambda regularizes the overfitted day
Test your models pair by pair
Statistical significance everywhere
[Verse 2]
Stratified keeps your proportions right
Grouped CV when clusters bite
Time series splits respect the order
Never peek across that border
K-fold standard, leave-one-out
But what's your validation route about?
The strategy depends on data type
Don't fall for the random hype
[Chorus]
Split and validate, stratify the classes
Group your data when the structure passes
Time series needs sequential flow
AUC-ROC and PR curves show
Cross-validate to find the way
Lambda regularizes the overfitted day
Test your models pair by pair
Statistical significance everywhere
[Bridge]
Log loss punishes the overconfident mind
Calibration plots show if probabilities align
Precision-recall when classes are skewed
AUC-ROC when balanced data's viewed
McNemar's test for classification pairs
Bootstrap intervals show the confidence we wear
Paired t-test when metrics are continuous
Statistical testing keeps us rigorous
[Verse 3]
Regularization adds a penalty term
L1 makes sparse features firm
L2 keeps the weights from growing wild
Lambda controls how much we've styled
Grid search or random for the hyperparameter dance
Cross-validation gives each setting a chance
The bias-variance tradeoff guides our hand
Too simple or complex, we must understand
[Chorus]
Split and validate, stratify the classes
Group your data when the structure passes
Time series needs sequential flow
AUC-ROC and PR curves show
Cross-validate to find the way
Lambda regularizes the overfitted day
Test your models pair by pair
Statistical significance everywhere
[Outro]
Model selection's an art and science combined
With proper evaluation, truth you'll find
From bias-variance to statistical tests
These tools will help you do your best
4. 4 Feature Engineering (The Craft)
[Verse 1]
Raw data comes messy, categories run wild
Target encoding maps the mean, frequency compiled
Count the occurrences, let the numbers tell their tale
Hash those features when dimensions start to fail
[Chorus]
Engineer the signal, craft what matters most
Target, frequency, hashing coast to coast
Interactions brewing, polynomials rise
Feature engineering opens up your eyes
MCAR, MAR, MNAR - know your missing dance
Mutual information gives selection a chance
[Verse 2]
Multiply your features, interactions bloom
X times Y reveals what single vars can't assume
Polynomial expansion, squared and cubed we go
Binning cuts continuous into buckets that we know
[Chorus]
Engineer the signal, craft what matters most
Target, frequency, hashing coast to coast
Interactions brewing, polynomials rise
Feature engineering opens up your eyes
MCAR, MAR, MNAR - know your missing dance
Mutual information gives selection a chance
[Bridge]
Missing completely at random, MCAR's the game
Missing at random MAR, depends on what we can name
Not missing at random, MNAR's the hardest call
Don't just impute blindly, understand them all
[Verse 3]
Recursive elimination walks backward through the door
L1 sparsity makes coefficients hit the floor
Information theory measures what each feature brings
Selection is an art, not just statistical things
[Chorus]
Engineer the signal, craft what matters most
Target, frequency, hashing coast to coast
Interactions brewing, polynomials rise
Feature engineering opens up your eyes
MCAR, MAR, MNAR - know your missing dance
Mutual information gives selection a chance
[Outro]
From raw to refined, the craftsman's sacred art
Every feature matters, every choice is smart
5. 1 Foundations
[Verse 1]
Start with layers stacked in rows
Forward flow is all it knows
Input travels left to right
Hidden layers shine their light
Universal theorem states
Any function it creates
Given width and given time
Neural networks climb and climb
[Chorus]
Feed it forward, push it back
Gradients upon the track
ReLU flows and GELU glows
Swish it smooth where learning goes
Xavier starts, He takes the lead
LSUV plants the perfect seed
Batch and layer, drop and stay
Neural foundations light the way
[Verse 2]
Backprop starts from final cost
Chain rule saves what might be lost
Partial derivatives cascade
Down each layer, debts are paid
Delta flows from right to left
No computation is bereft
Weight updates with learning rate
Gradient descent seals the fate
[Chorus]
Feed it forward, push it back
Gradients upon the track
ReLU flows and GELU glows
Swish it smooth where learning goes
Xavier starts, He takes the lead
LSUV plants the perfect seed
Batch and layer, drop and stay
Neural foundations light the way
[Bridge]
When ReLU dies, gradients cease
GELU brings dying neurons peace
Swish combines the best of both
Smooth and sharp, they keep their oath
Initialize with proper care
Xavier uniform and fair
He normal for ReLU's might
LSUV makes the variance right
[Verse 3]
Batch norm shifts the mean to zero
Makes your gradients a hero
Layer norm works sample-wise
When your batch size multiplies
Dropout kills some neurons dead
Regularization in your head
But be careful when to use
Each technique has its own clues
[Chorus]
Feed it forward, push it back
Gradients upon the track
ReLU flows and GELU glows
Swish it smooth where learning goes
Xavier starts, He takes the lead
LSUV plants the perfect seed
Batch and layer, drop and stay
Neural foundations light the way
[Outro]
From inputs through to final score
These foundations are your core
Master these and you'll see clear
How deep learning persevere
6. 2 Convolutional Neural Networks
[Verse 1]
Started back in eighty-nine with LeCun's dream
LeNet Five showed us what convolution means
Filters slide across the image, stride by stride
Receptive fields are windows where the features hide
Padding keeps the edges safe from being lost
Dilation spreads the kernel out, but at what cost
[Chorus]
Conv Net blues, from pixels to predictions
Kernel slides with mathematical precision
Pooling down, feature maps grow deeper
Transfer learning makes the training cheaper
From LeNet to EfficientNet we climb
Convolutional rhythms keeping time
[Verse 2]
AlexNet brought the power with its GPU might
Eight layers deep and ReLU brought the light
Then VGG went deeper with those tiny threes
Sixteen layers stacked up like autumn leaves
But gradients would vanish in the deeper nets
Until ResNet came along with skip connections set
[Chorus]
Conv Net blues, from pixels to predictions
Kernel slides with mathematical precision
Pooling down, feature maps grow deeper
Transfer learning makes the training cheaper
From LeNet to EfficientNet we climb
Convolutional rhythms keeping time
[Bridge]
ImageNet pretrained weights are gold
Fine-tune the top, keep the bottom bold
Freeze the features, train the classifier
YOLO sees objects, Faster R-CNN's wiser
U-Net segments with that hourglass flow
Encoder-decoder, watch the pixels grow
[Verse 3]
EfficientNet scaled up with compound wisdom
Width and depth and resolution's kingdom
Mobile architectures for the edge device
Separable convolutions, efficient and precise
Object detection with those anchor boxes
Semantic segmentation, pixel-level foxes
[Chorus]
Conv Net blues, from pixels to predictions
Kernel slides with mathematical precision
Pooling down, feature maps grow deeper
Transfer learning makes the training cheaper
From LeNet to EfficientNet we climb
Convolutional rhythms keeping time
[Outro]
Thirty years of vision, layer by layer
Convolution revolution, we're the players
In this neural network, blues-filled game
Computer vision will never be the same
7. 3 Sequence Models
[Verse 1]
RNN starts simple with a hidden state
Memory from yesterday, but gradients don't wait
They vanish as they travel through the chain
Long sequences forgotten, driving us insane
The basic cell just can't hold on too long
When backprop hits the layers, signals grow weak and wrong
[Chorus]
Remember the gates, forget and update
LSTM controls what flows and what waits
GRU simplified with reset and update gates
Attention mechanism says "look here, don't wait"
Convolution sliding through the temporal space
Three sequence models, each finding their place
[Verse 2]
LSTM came along with gates to save the day
Forget gate zeros out what should fade away
Input gate decides what new info to store
Output gate controls what flows to the fore
Cell state highway keeps the gradient strong
Now we can remember sequences long
[Chorus]
Remember the gates, forget and update
LSTM controls what flows and what waits
GRU simplified with reset and update gates
Attention mechanism says "look here, don't wait"
Convolution sliding through the temporal space
Three sequence models, each finding their place
[Verse 3]
GRU streamlined the gates from three down to two
Reset gate clears memory when starting anew
Update gate blends the past with present state
Fewer parameters but performance stays great
Bahdanau attention breaks the bottleneck curse
Looks at all encoder states, for better or worse
[Bridge]
But wait, there's more than recurrence alone
One-D convolution makes patterns known
Sliding filters capture local temporal features
Parallel processing, one of its best teachers
TCN stacks dilated convs in layers deep
Receptive fields growing, long patterns to keep
[Verse 4]
Luong attention came with different alignment
Global and local focus, perfect refinement
Dot product scoring or a learned function call
Weighted context vectors, attending to all
While recurrence struggles with parallel compute
Convolution processes sequences in one route
[Outro]
From vanishing gradients to attention's bright gleam
From LSTM gates to the convolutional dream
Each model has its strength in the sequence game
Understanding their purpose, that's how we claim
Mastery over time series, text, and speech
These three sequence models put learning within reach
8. 4 Practical Deep Learning
[Verse 1]
Started with a learning rate too high, my loss was jumping to the sky
Gradient descent was going wild, like a Texas storm untamed and riled
Then I learned about the schedule, cosine annealing smooth and gentle
Starts up high then slowly falls, like the sun behind cathedral walls
[Chorus]
Schedule it down, warm restart around
Mix precision to save, debug what you've found
When deep learning's right, when simple's the sight
Practical wisdom in the neural night
Schedule, mix, augment, debug
These four pillars won't let you shrug
[Verse 2]
OneCycleLR takes you on a ride, learning rate goes up then slides
Warm restarts give a second chance, like a phoenix in a learning dance
Mixed precision cuts the memory load, sixteen bits on the neural road
Keep the gradients in thirty-two, accuracy stays clean and true
[Chorus]
Schedule it down, warm restart around
Mix precision to save, debug what you've found
When deep learning's right, when simple's the sight
Practical wisdom in the neural night
Schedule, mix, augment, debug
These four pillars won't let you shrug
[Verse 3]
Data augmentation domain-aware, images flip and rotate with care
Text needs different tricks to grow, back-translation makes the dataset flow
Audio stretches time and pitch, every domain has its own rich niche
More data means your model learns, synthetic samples help it turn
[Bridge]
When your loss curve looks strange, plateauing in a narrow range
Check your gradients, plot the flow, dead neurons barely even glow
Histogram tells the hidden tale, are your weights about to fail
Learning rate too high or low, the debug charts will let you know
[Verse 4]
Sometimes neural nets are overkill, linear models climb the hill
When you've got structure clear and bright, classical methods see the light
But when the patterns run too deep, hidden layers earn their keep
Vision, language, complex sound, that's where neural nets are found
[Outro]
From the textbook to the code, Goodfellow lit the road
Practical deep learning art, schedule smart and debug heart
Schedule, mix, augment, debug
Four pillars strong, don't you shrug
9. 1 Ranking & Recommendations
[Verse 1]
When users browse and need suggestions true
Collaborative filtering's what we do
Matrix factorization breaks it down
User-item pairs in rows and columns found
ALS alternating least squares dance
Minimizing error, give predictions chance
Learning from behavior, patterns emerge
Past interactions help new tastes converge
[Chorus]
Rank and recommend, make the right choice
Pointwise pairwise listwise, give algorithms voice
Two towers rising, candidates flow
Retrieve then rerank, watch the magic grow
Cold start exploring, bandits in play
Recommendation systems guide the way
[Verse 2]
Content-based filtering reads the features clean
Item attributes paint the user scene
Hybrid approaches blend the best of both
Collaborative content, binding like an oath
Learning-to-rank takes three different roads
Pointwise regression, single scores it loads
Pairwise comparisons, RankNet's neural art
Which item wins? That's where we start
[Chorus]
Rank and recommend, make the right choice
Pointwise pairwise listwise, give algorithms voice
Two towers rising, candidates flow
Retrieve then rerank, watch the magic grow
Cold start exploring, bandits in play
Recommendation systems guide the way
[Bridge]
Listwise LambdaMART optimizes the whole list
NDCG gradients, precision can't be missed
Two-tower models encode user and item
Query and candidate, perfect alignment
First stage retrieval casts the widest net
Second stage reranking, fine-tune what you get
[Verse 3]
Cold start problem when the data's sparse
New users items, fill the empty space
Exploration-exploitation, bandit's choice
Thompson sampling, epsilon's voice
Multi-armed bandits balance what we know
With discovering paths that help us grow
Pipeline architecture, candidate to rank
From millions down to dozens, fill the tank
[Chorus]
Rank and recommend, make the right choice
Pointwise pairwise listwise, give algorithms voice
Two towers rising, candidates flow
Retrieve then rerank, watch the magic grow
Cold start exploring, bandits in play
Recommendation systems guide the way
[Outro]
From collaborative to content-based design
Hybrid systems make the stars align
Learning-to-rank with neural networks deep
Recommendation promises that we keep
10. 2 Fraud Detection & Anomaly Detection
[Verse 1]
In the world of transactions flowing fast and free
Ninety-nine percent are clean as they can be
But that single percent that's hiding in the shade
Those are the fraudsters that we need to catch today
Dataset's so imbalanced, needles in the hay
Normal classification just won't find the way
[Chorus]
SMOTE it up, balance the classes out
Cost-sensitive learning when you're full of doubt
Tune that threshold, find the perfect line
Catch the fraud in real-time, every single time
Feature engineering from the transaction flow
Graph connections tell you what you need to know
[Verse 2]
Synthetic samples from the minority class
SMOTE creates new points so the training will last
But when the cost of missing fraud is way too high
Weight those positive cases, let the model fly
Precision-recall curve shows the trade-off clear
Move that threshold left when false negatives you fear
[Chorus]
SMOTE it up, balance the classes out
Cost-sensitive learning when you're full of doubt
Tune that threshold, find the perfect line
Catch the fraud in real-time, every single time
Feature engineering from the transaction flow
Graph connections tell you what you need to know
[Bridge]
But here's the twist in this detection game
The fraudsters learn and adapt, nothing stays the same
Adversarial drift means your model fades
As criminals find new paths through your data maze
Time windows, velocity, amount and place
Network patterns showing connections they can't erase
[Verse 3]
Real-time scoring with latency so tight
Milliseconds matter in this fraud-fighting fight
Pre-compute features, cache what you can
Simple models sometimes beat the complex plan
When patterns shift and drift comes knocking at your door
Retrain that model, then deploy once more
[Chorus]
SMOTE it up, balance the classes out
Cost-sensitive learning when you're full of doubt
Tune that threshold, find the perfect line
Catch the fraud in real-time, every single time
Feature engineering from the transaction flow
Graph connections tell you what you need to know
[Outro]
In this endless dance of cat and mouse we play
Anomaly detection saves another day
Balance your data, engineer with care
Real-time responses floating in the air
The fraudsters adapt but we adapt too
That's the fraud detection blues for me and you
11. 3 Time Series & Forecasting
[Verse 1]
Time series data flows like a river through the years
ARIMA breaks it down, makes the patterns crystal clear
AutoRegressive looks behind, Integrated makes it smooth
Moving Average finds the trend, that's how forecasters groove
Exponential smoothing weighs the recent more than past
Alpha beta gamma tune the forecast built to last
[Chorus]
A-R-I-M-A, Prophet shows the way
Exponential smoothing keeps the noise at bay
Lags and rolling windows, Fourier in the night
Tree-based strategies make the future bright
Time series forecasting, probability in sight
[Verse 2]
Prophet handles seasonality with additive and multiplicative flow
Holiday effects and changepoints help the model grow
Direct strategy trains each horizon on its own
Recursive takes one step ahead then builds upon what's shown
Multi-step or single-step, the choice will shape your game
Feature engineering holds the key to forecasting fame
[Chorus]
A-R-I-M-A, Prophet shows the way
Exponential smoothing keeps the noise at bay
Lags and rolling windows, Fourier in the night
Tree-based strategies make the future bright
Time series forecasting, probability in sight
[Bridge]
Temporal features are the gold we seek to find
Lagged variables tell us what we left behind
Rolling mean and standard deviation smooth the ride
Fourier transforms capture cycles deep inside
Random Forest, XGBoost trees can learn the temporal dance
Prediction intervals give uncertainty a chance
[Verse 3]
Probabilistic forecasting doesn't give just one result
Confidence bands and quantiles help us navigate tumult
Upper bounds and lower bounds embrace the unknown space
Mean absolute error tells us if we're keeping pace
Walk-forward validation keeps our models honest true
Backtesting on the past prepares us for what's new
[Chorus]
A-R-I-M-A, Prophet shows the way
Exponential smoothing keeps the noise at bay
Lags and rolling windows, Fourier in the night
Tree-based strategies make the future bright
Time series forecasting, probability in sight
[Outro]
From stationary data to the trends that rise and fall
Time series and forecasting, we can handle it all
Direct or recursive, the future's in our hands
With probability and intervals, we'll understand
12. 4 Search & Information Retrieval
[Verse 1]
Started with a simple dream to find what users need
Built an index flipping words to documents they feed
Term frequency times inverse document frequency
TF-IDF weighs the words with mathematical harmony
BM25 takes it further with saturation in mind
Length normalization keeps the scoring refined
Inverted indices map each term to where it lives
Fast retrieval when the query string arrives
[Chorus]
Search and retrieve, find what you believe
TF-IDF and BM25 achieve
Embeddings in space, vectors find their place
Query understanding shows the user's face
Test and iterate, A-B compensate
Information retrieval seals our fate
[Verse 2]
Embeddings learned from context paint semantic scenes
Dense vectors clustering similar meanings
Cosine similarity measures the angle tight
Nearest neighbors dancing in high-dimensional light
Before we called it RAG, retrieval led the way
Vector databases storing knowledge every day
Transformer encoders mapping text to floating points
Similar concepts gathering at vector joints
[Chorus]
Search and retrieve, find what you believe
TF-IDF and BM25 achieve
Embeddings in space, vectors find their place
Query understanding shows the user's face
Test and iterate, A-B compensate
Information retrieval seals our fate
[Verse 3]
Query comes in messy, spelling all askew
Edit distance algorithms make corrections true
Intent classification reads between the lines
Entity recognition in the query finds
Autocomplete suggestions guide the user's hand
Synonym expansion helps them understand
Query reformulation when results fall short
Natural language processing of every sort
[Bridge]
A-B testing in the wild, interleaving results side by side
Click-through rates and dwell time, metrics we can't hide
Online evaluation while the users browse and search
Quality improvements that we constantly research
[Chorus]
Search and retrieve, find what you believe
TF-IDF and BM25 achieve
Embeddings in space, vectors find their place
Query understanding shows the user's face
Test and iterate, A-B compensate
Information retrieval seals our fate
[Outro]
From sparse to dense and back again
The search continues without end
Information waiting to be found
In every query, wisdom's sound
13. 5 Other High-Value Niches
[Verse 1]
In the market where the prices dance and sway
Elasticity tells us how customers will pay
When you raise it up, do they walk away?
Or stick around for another day?
Gradient descent finds the sweet spot right
Maximizing revenue through the night
A-B testing shows us what works best
Put your pricing model to the test
[Chorus]
Five niches where the money flows
P-C-S-C-R, that's how it goes
Pricing, Churn, Supply, Credit too
Risk assessment pulling us through
Train your models, watch them learn
Every dataset helps us earn
Classical methods tried and true
These five niches work for you
[Verse 2]
Customer churn is the silent thief
Steals your revenue beyond belief
Logistic regression spots the signs
Random forests draw the lines
Lifetime value tells the tale
Which customers will never fail
Cohort analysis shows the trend
Who will stay until the end
[Chorus]
Five niches where the money flows
P-C-S-C-R, that's how it goes
Pricing, Churn, Supply, Credit too
Risk assessment pulling us through
Train your models, watch them learn
Every dataset helps us earn
Classical methods tried and true
These five niches work for you
[Bridge]
Supply chain optimization keeps the flow
Inventory forecasting helps you know
Time series models predict demand
Neural networks lend a helping hand
ARIMA captures seasonal waves
While your warehouse money saves
[Verse 3]
Credit scoring guards the lending gate
Risk modeling seals your financial fate
Regulatory constraints keep you in line
Feature engineering makes models shine
Logistic regression for pass or fail
Decision trees tell a clearer tale
Default probability guides the way
Who gets approved another day
[Outro]
From Texas blues to K-pop dreams
Machine learning powers all your schemes
Five high-value niches pave the road
Classical methods bear the load
Price and churn and supply so bright
Credit risk done just right
These domains will make you shine
Advanced practitioner by design
14. 1 Data Pipeline Work
[Verse 1]
Raw data flows from every source tonight
Extract transform and load it right
Or maybe extract load then transform the way
ELT patterns help us scale today
Messy records broken schemas everywhere
Real world data needs some tender care
Pipeline orchestration keeps it all in line
From chaos to features we refine
[Chorus]
Extract Transform Load, that's the ETL way
Extract Load Transform when the data's here to stay
Feature stores keep our ML fed
Feast and Tecton, engineered and spread
Pipeline flow, pipeline flow
Clean the data, make it go
Pipeline flow, pipeline flow
From raw to gold, watch it grow
[Verse 2]
Great Expectations validate what comes through
Schema enforcement keeps it true
Check for nulls and ranges out of bounds
Data quality can't break down
Drift detection watches through the night
When distributions shift from what's right
Concept drift means the world has changed its mind
New patterns that we need to find
[Chorus]
Extract Transform Load, that's the ETL way
Extract Load Transform when the data's here to stay
Feature stores keep our ML fed
Feast and Tecton, engineered and spread
Pipeline flow, pipeline flow
Clean the data, make it go
Pipeline flow, pipeline flow
From raw to gold, watch it grow
[Bridge]
Scale it up when terabytes arrive
Batch or streaming, keep the flow alive
Monitor the pipeline health each day
Catch the failures before they break the way
Version features, lineage traced
Every transformation can be placed
[Verse 3]
Messy data tells a human story
Inconsistent timestamps, categories
Handle duplicates and missing fields with grace
Transform the chaos into structured space
Feature engineering makes the models shine
Time windows, aggregates by design
Store computed features, serve them fast and clean
The smoothest pipeline you've ever seen
[Chorus]
Extract Transform Load, that's the ETL way
Extract Load Transform when the data's here to stay
Feature stores keep our ML fed
Feast and Tecton, engineered and spread
Pipeline flow, pipeline flow
Clean the data, make it go
Pipeline flow, pipeline flow
From raw to gold, watch it grow
[Outro]
Data flows like rivers to the sea
Pipeline architecture sets us free
From collection through to model deployment
Every stage needs our employment
Pipeline flow, let it go
Clean data makes the models glow
15. 2 Model Serving & Deployment
[Verse 1]
When your model's trained and ready to go live
Two paths diverge in the serving divide
Batch inference waits, processes in chunks
Real-time responds when the API call comes
Trade latency for throughput, choose your design
Milliseconds matter when users are online
[Chorus]
Serialize, containerize, optimize the flow
ONNX, TorchScript, formats that you know
Docker wraps it tight, Kubernetes scales high
Triton serves it fast when the traffic flies by
Model serving magic, deployment done right
From training to production, bringing models to light
[Verse 2]
Save your weights in formats that persist
ONNX cross-platform, can't be missed
TorchScript compiles your PyTorch brain
SavedModel format keeps TensorFlow's chain
Pickle files and checkpoints, choose your way
Serialization matters for deployment day
[Chorus]
Serialize, containerize, optimize the flow
ONNX, TorchScript, formats that you know
Docker wraps it tight, Kubernetes scales high
Triton serves it fast when the traffic flies by
Model serving magic, deployment done right
From training to production, bringing models to light
[Bridge]
A-B testing splits the traffic clean
Shadow deployments lurk unseen
Canary rollouts, small percent
Gradual changes, risks prevent
Test your models safely first
Before full deployment burst
[Verse 3]
Latency budgets keep you on track
Quantize those weights, eight bits back
Pruning cuts the neurons spare
Distillation makes models share
Compress and optimize, speed up the call
Performance matters most of all
[Final Chorus]
Serialize, containerize, optimize the flow
ONNX, TorchScript, formats that you know
Docker wraps it tight, Kubernetes scales high
Triton serves it fast when the traffic flies by
A-B test and canary, shadow deploy with care
Model serving mastery, intelligence everywhere
[Outro]
From batch to real-time, the choice is yours
Serving models right opens up new doors
16. 3 MLOps & Monitoring
[Verse 1]
Started with a model trained on morning coffee
Hyperparameters scattered, results looking sloppy
MLflow comes to save us, tracks each experiment run
Logs the loss and accuracy until the training's done
Weights and Biases watching every epoch that we make
Visualizing gradients for our learning's sake
[Chorus]
Track it, version it, monitor the flow
MLOps lifecycle, watch your models grow
Registry keeps them safe, reproducible and clean
Data drift detection in the production machine
Track it, version it, monitor the flow
Feedback loops and retraining, that's the way to go
[Verse 2]
Model registry stores each version that we build
Staging, production, archived - structure that we've filled
Artifacts and metadata, lineage crystal clear
Reproduce that winning run from six months ago this year
Semantic versioning guides us, major minor patch
Every model deployment has a version we can catch
[Chorus]
Track it, version it, monitor the flow
MLOps lifecycle, watch your models grow
Registry keeps them safe, reproducible and clean
Data drift detection in the production machine
Track it, version it, monitor the flow
Feedback loops and retraining, that's the way to go
[Bridge]
Production's where the rubber meets the road
Input distributions start to shift their mode
Kolmogorov-Smirnov tests are ringing alarm bells
Prediction drift is rising, performance indicator tells
Population stability index warns us of decay
Time to trigger retraining, can't delay another day
[Verse 3]
Monitoring dashboards light up red and green
Statistical tests revealing what the data really mean
Ground truth labels flowing back through feedback streams
Automated pipelines fulfilling MLOps dreams
Threshold-based retraining when the metrics start to fall
Continuous deployment answers the model's call
[Chorus]
Track it, version it, monitor the flow
MLOps lifecycle, watch your models grow
Registry keeps them safe, reproducible and clean
Data drift detection in the production machine
Track it, version it, monitor the flow
Feedback loops and retraining, that's the way to go
[Outro]
From experiment to production, MLOps shows the way
Monitoring and versioning, models here to stay
Track it, version it, monitor the flow
That's how professional machine learning systems grow
17. 4 Tools & Ecosystem
[Verse 1]
Started with NumPy arrays, vectors in my hand
Pandas DataFrames dancing, cleaning data like I planned
Scikit-learn's the teacher, algorithms line by line
Python's got the power, making predictions shine
[Chorus]
Four tools and ecosystem, build your ML dream
Python, PyTorch, SQL streams
Git and CI will make it flow
Spark and Dask when data grows
Four tools, four tools, that's how knowledge gleams
[Verse 2]
PyTorch tensors flowing through the neural network gates
Autograd's computing gradients that calculate my weights
TensorFlow's an option, either path will serve you right
Dynamic graphs or static, both will help you see the light
[Chorus]
Four tools and ecosystem, build your ML dream
Python, PyTorch, SQL streams
Git and CI will make it flow
Spark and Dask when data grows
Four tools, four tools, that's how knowledge gleams
[Verse 3]
SQL's not just simple selects, window functions call my name
Analytical queries, complex joins are not the same
Common table expressions, subqueries that nest so deep
Real production data, not the toy stuff that you keep
[Bridge]
When your data's getting bigger
Spark will be your friend
Distributed computation
Scale that doesn't end
Dask for Python lovers
Parallel and clean
Git commits tell the story
CI makes the pipeline lean
[Chorus]
Four tools and ecosystem, build your ML dream
Python, PyTorch, SQL streams
Git and CI will make it flow
Spark and Dask when data grows
Four tools, four tools, that's how knowledge gleams
[Verse 4]
Version control's essential, branches merge and conflicts heal
Continuous integration, automated tests make models real
MLOps is the future, pipelines running through the night
Infrastructure as code now, deployment done just right
[Outro]
NumPy, pandas, scikit-learn
PyTorch helps your models burn
SQL queries, Spark at scale
Git and CI, they never fail
Four tools mastered, you have learned
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