Project Best Practices

MLOps Project Best Practices: Your Recipe Book for Success 📖

Imagine you’re a chef. Not just any chef—you run a restaurant where thousands of people eat every day. If you forget a recipe, use different ingredients each time, or cook things differently than how you taste-tested them, disaster happens. Customers get sick. Food tastes weird. Your restaurant closes.

Machine Learning projects work the same way. Your ML model is like a recipe. The data is your ingredients. The code is your cooking method. And “production” is when real people actually eat your food!

Let’s learn the 5 golden rules that keep your ML kitchen running smoothly.

1. Reproducibility Best Practices 🔁

The Story: The Lost Cake Recipe

Little Maya made the most amazing chocolate cake for her birthday. Everyone loved it! But when her friend asked for the recipe, Maya realized… she didn’t write anything down. “Um, I used some flour… maybe 2 cups? And chocolate… some amount? I baked it for… a while?”

She could never make that exact cake again. 😢

This is what happens in ML without reproducibility. You train an amazing model, but you can’t recreate it because you didn’t track:

• What data you used
• What random seed you picked
• What hyperparameters you chose
• What version of your code ran

The Fix: Write Everything Down!

graph TD
    A["🎯 Start Experiment"] --> B["📝 Log Data Version"]
    B --> C["📝 Log Random Seed"]
    C --> D["📝 Log Hyperparameters"]
    D --> E["📝 Log Code Version"]
    E --> F["✅ Reproducible!"]

Real Example

Bad (Maya’s approach):

model.fit(data)
# Which data? What settings?

Good (Professional approach):

# Version: v2.3.1
# Data: customers_2024_jan.csv
# Seed: 42
# Learning rate: 0.001
random.seed(42)
model.fit(data, lr=0.001)

Key Rule

“If you can’t reproduce it, you can’t trust it.”

2. Training-Serving Skew ⚖️

The Story: The Practice vs Game Problem

Tommy practiced basketball in his backyard every day. He was amazing! He could shoot from anywhere and never miss. But at his first real game… he missed every shot. Why?

• Practice: Soft grass, no crowd, perfect silence
• Game: Hard court, screaming fans, pressure

His practice environment was different from the real game. This is training-serving skew.

What Is It?

Your model trains on training data but serves real-world data. If these are different, your model acts like Tommy—great in practice, terrible in the real game.

graph TD
    A["Training Data"] -->|Different?| B["Serving Data"]
    B --> C{Match?}
    C -->|Yes| D["✅ Model Works!"]
    C -->|No| E["❌ Model Fails!"]

Three Types of Skew

Real Example

The Bug:

# Training: age calculated precisely
age = (today - birth_date).days / 365.25

# Serving: age from user input (rounded)
age = user_input["age"]  # "25" vs 25.7

The Fix:

# Use SAME function everywhere!
def calculate_age(birth_date):
    return int((today - birth_date).days / 365.25)

Key Rule

“Train like you serve. Serve like you train.”

3. Configuration Management 🎛️

The Story: The Remote Control

Imagine if every TV remote had buttons in different places. Your living room TV has volume on the left. Bedroom TV has it on the right. Kitchen TV has no volume button at all!

You’d go crazy trying to remember which remote works which way.

Configuration management means putting all your “buttons” (settings) in one predictable place.

Why It Matters

ML projects have LOTS of settings:

• Learning rate
• Batch size
• Model type
• Data paths
• Feature names
• Server addresses

Without organization, you end up with settings scattered everywhere—in code, in scripts, in random files, in your head!

The Solution: One Config File

# config.yaml - ONE place for everything!
model:
  type: "random_forest"
  n_estimators: 100
  max_depth: 10

data:
  train_path: "data/train.csv"
  test_path: "data/test.csv"

training:
  learning_rate: 0.001
  batch_size: 32
  epochs: 50

Environment-Specific Configs

graph TD
    A["Base Config"] --> B["Dev Config"]
    A --> C["Staging Config"]
    A --> D["Production Config"]

Never put secrets in config files! Use environment variables:

# Good: Secret from environment
api_key = os.environ["API_KEY"]

# Bad: Secret in code
api_key = "sk-12345abcde"  # NEVER!

Key Rule

“One source of truth. No magic numbers in code.”

4. ML Project Structure 📁

The Story: The Messy Room

Two kids have toy collections.

Kid A: Toys everywhere! LEGOs on the bed, cars under the table, puzzles in the closet, dolls in the kitchen. When mom asks for the red LEGO, it takes 30 minutes to find.

Kid B: LEGOs in the LEGO box. Cars in the car drawer. Puzzles on the puzzle shelf. Red LEGO? Found in 10 seconds!

Your ML project is your toy collection. Structure saves time.

The Standard ML Project Layout

my_ml_project/
├── data/
│   ├── raw/           # Original, untouched data
│   ├── processed/     # Cleaned, ready-to-use data
│   └── external/      # Data from outside sources
│
├── notebooks/         # Experiments (Jupyter)
│
├── src/
│   ├── data/          # Data loading scripts
│   ├── features/      # Feature engineering
│   ├── models/        # Model definitions
│   └── evaluation/    # Metrics & evaluation
│
├── models/            # Saved/trained models
│
├── configs/           # Configuration files
│
├── tests/             # Unit & integration tests
│
├── requirements.txt   # Dependencies
└── README.md          # Project documentation

Why Each Folder Matters

Key Rule

“A place for everything. Everything in its place.”

5. Code Review for ML 👀

The Story: The Spelling Bee Partner

Before the spelling bee, smart kids practice with a partner. The partner listens, catches mistakes, and gives tips. “Hey, you spelled ‘necessary’ wrong—it has two S’s!”

Code review is your spelling bee partner for code. Someone else reads your work and catches mistakes before they become problems.

ML Code Review Is Special

Regular code review checks:

• Does the code run?
• Is it readable?
• Are there bugs?

ML code review adds:

• Is the math right?
• Is there data leakage?
• Are features computed correctly?
• Will this work in production?

The ML Code Review Checklist

graph LR
    A["Code Review"] --> B["Logic Check"]
    A --> C["Data Leakage Check"]
    A --> D["Feature Engineering Check"]
    A --> E["Reproducibility Check"]
    A --> F["Production Readiness"]

What Reviewers Look For

Real Example: Catching Data Leakage

The Bug (Reviewer Catches):

# WRONG: Scaling before split = data leakage!
scaler.fit(all_data)  # Sees test data!
train, test = split(all_data)

The Fix:

# RIGHT: Scale only on training data
train, test = split(all_data)
scaler.fit(train)  # Only sees train!
train = scaler.transform(train)
test = scaler.transform(test)

Key Rule

“Four eyes see more than two.”

Putting It All Together 🎯

Remember our chef metaphor? Here’s how everything connects:

graph LR
    A["🎯 Great ML Project"] --> B["📝 Reproducible"]
    A --> C["⚖️ No Skew"]
    A --> D["🎛️ Organized Configs"]
    A --> E["📁 Clean Structure"]
    A --> F["👀 Code Reviewed"]

Quick Reference Card 🃏

The 5 Best Practices

1. Reproducibility → Log everything (data, seeds, params, code)
2. Training-Serving Skew → Same code path for training & serving
3. Configuration Management → One config file, no magic numbers
4. Project Structure → Standard folders, clear organization
5. Code Review → ML-specific checklist, catch data leakage

Warning Signs 🚨

• “It worked on my machine” → Reproducibility problem
• “Model was great in testing” → Training-serving skew
• “Where’s that setting?” → Config management problem
• “Which file has the features?” → Structure problem
• “Nobody looked at this” → Missing code review

You’ve Got This! 🚀

These five practices aren’t just rules—they’re superpowers. They help you:

• Sleep well (your model won’t mysteriously break)
• Work faster (find things quickly)
• Collaborate better (others understand your work)
• Debug easily (reproduce any issue)
• Ship confidently (catch bugs before production)

Start with one practice today. Master it. Then add the next. Before you know it, you’ll be running the most reliable ML kitchen in town!

Now go build something amazing—the right way. 🎉