# Component Design Standards for Optimization

This document outlines the component design standards for Optimization projects. It aims to guide developers in creating reusable, maintainable, and performant components, improving overall application quality and reducing development costs.

## 1. Architecture Overview

### 1.1. Component Definition

**Standard:** A component should be a self-contained, reusable module that performs a specific function within the Optimization system.

**Do This:** Encapsulate related functionality into a single component. For example, a "DataIngestionComponent" handles all aspects of data loading and preprocessing.

**Don't Do This:** Create monolithic components that perform multiple unrelated tasks. This leads to code bloat and reduced reusability.

**Why:** Modularity improves code organization, reduces dependencies between functions, and promotes easier testing & maintenance.

**Example:**

"""python

# Correct: well-defined component

class DataIngestionComponent:

def __init__(self, source, config):

self.source = source

self.config = config

def load_data(self):

# Logic to load data from source using config

pass

def preprocess_data(self):

# Logic to preprocess loaded data

pass

# Incorrect: monolithic function

def process_data(source, config):

# Loads, preprocesses, calculates stats all in one place

pass

"""

### 1.2. Component Interaction

**Standard:** Components should communicate through well-defined interfaces to reduce tight coupling.

**Do This:** Use Dependency Injection (DI) and publish-subscribe patterns to decouple components.

**Don't Do This:** Directly access internal state or methods of other components.

**Why:** Loose coupling makes components more independent and easier to replace, test, and reuse.

**Example:**

"""python

# Publish-Subscribe using a Mediator (example for illustration)

class EventMediator:

def __init__(self):

self._listeners = {}

def subscribe(self, event_type, callback):

if event_type not in self._listeners:

self._listeners[event_type] = []

self._listeners[event_type].append(callback)

def publish(self, event_type, data):

if event_type in self._listeners:

for callback in self._listeners[event_type]:

callback(data)

# Components using Mediator

class DataAnalyzer:

def __init__(self, mediator: EventMediator):

self.mediator = mediator

mediator.subscribe("data_ingested", self.analyze)

def analyze(self, data):

# Logic to analyze ingested data

print("Analysis complete.")

class DataExporter:

def __init__(self, mediator: EventMediator):

self.mediator = mediator

mediator.subscribe("data_analyzed", self.export)

def export(self, data):

# Logic to export Analyzed data

print("Data Exported.")

# Usage

mediator = EventMediator()

analyzer = DataAnalyzer(mediator)

exporter = DataExporter(mediator)

# Simulate data ingestion

mediator.publish("data_ingested", {"data": [1, 2, 3]})

#This Pattern allows loose coupling.

"""

### 1.3. Layered Architecture

**Standard:** Implement a layered architecture to separate concerns.

**Do This:** Structure your application into presentation, business logic, and data access layers.

**Don't Do This:** Mix UI code with data access code in the same component.

**Why:** Separation of concerns enhances maintainability, testability, and scalability.

**Example:**

"""

# Example Layered Architecture

- presentation/ # UI components

- business_logic/ # Business rules and workflows

- data_access/ # Data persistence and retrieval

- models/ # Data models

"""

## 2. Component Design Principles

### 2.1. Single Responsibility Principle (SRP)

**Standard:** Each component should have only one reason to change.

**Do This:** Focus each component on a specific task or function.

**Don't Do This:** Create components that handle multiple unrelated responsibilities.

**Why:** SRP promotes code reuse, makes components easier to understand and test, and reduces the risk of introducing bugs when modifying the component.

**Example:**

"""python

# Correct: Separate classes for different responsibilities

class DataFetcher:

def fetch_data(self, url):

# Fetches data from URL

pass

class DataParser:

def parse_data(self, data):

# Parses data into a specific format

pass

# Incorrect: Single class handling both fetching and parsing

class DataProcessor:

def process_data(self, url):

# Fetches data from URL and parses it

pass

"""

### 2.2. Open/Closed Principle (OCP)

**Standard:** Components should be open for extension but closed for modification.

**Do This:** Use inheritance and polymorphism to add new functionality without altering existing code.

**Don't Do This:** Modify existing component code directly when adding new features.

**Why:** OCP reduces the risk of introducing bugs when extending components, ensuring stability and maintainability.

**Example:**

"""python

# Correct: Extending functionality using inheritance

class BaseOptimizer:

def optimize(self, data):

raise NotImplementedError

class GradientDescentOptimizer(BaseOptimizer):

def optimize(self, data):

# Implements gradient descent optimization

pass

class AdamOptimizer(BaseOptimizer):

def optimize(self, data):

# Implements Adam optimization

pass

# Incorrect: Modifying the original class to add new optimizers

class Optimizer:

def optimize(self, data, method="gradient_descent"):

if method == "gradient_descent":

# Implements gradient descent

pass

elif method == "adam":

# Implements Adam

pass

"""

### 2.3. Liskov Substitution Principle (LSP)

**Standard:** Subtypes should be substitutable for their base types without altering the correctness of the program.

**Do This:** Ensure derived classes correctly implement the behavior expected of the base class.

**Don't Do This:** Create subclasses that exhibit unexpected behaviour compared to the base class.

**Why:** LSP ensures that derived classes can be used interchangeably with their base classes, making code more flexible and robust.

**Example:**

"""python

# Correct: Subclass adheres to the interface contract

class BaseModel:

def predict(self, input_data):

raise NotImplementedError

class LinearRegressionModel(BaseModel):

def predict(self, input_data):

# Implements linear regression prediction

return linear_regression_calculation(input_data)

class NeuralNetworkModel(BaseModel):

def predict(self, input_data):

# Implements neural network prediction

return neural_network_calculation(input_data)

def use_model(model: BaseModel, data):

return model.predict(data)

# usage

model = LinearRegressionModel()

result = use_model(model, [1,2,3])

"""

### 2.4. Interface Segregation Principle (ISP)

**Standard:** Clients should not be forced to depend on methods they do not use.

**Do This:** Create small, specific interfaces that provide only the methods a client needs.

**Don't Do This:** Create large, general-purpose interfaces that force clients to implement unnecessary methods.

**Why:** ISP reduces dependencies and makes code more modular and maintainable. It promotes loose coupling.

**Example:**

"""python

# Correct: Specific interfaces for specific clients

from typing import Protocol

class DataReader(Protocol): #Requires Python 3.8+

def read_data(self):

...

class DataWriter(Protocol):

def write_data(self, data):

...

# Clients requesting data from a source

class DataFetcher:

def __init__(self, reader: DataReader):

self.reader = reader

def fetch(self):

return self.reader.read_data()

# Clients which persist the validated data

class DataPersistor:

def __init__(self, writer: DataWriter):

self.writer = writer

def persist(self, data):

return self.writer.write_data(data)

# Incorrect: A bloated interface

class DataInterface:

def read_data(self):

pass

def write_data(self, data):

pass

def process_data(self):

pass

"""

### 2.5. Dependency Inversion Principle (DIP)

**Standard:** High-level modules should not depend on low-level modules. Both should depend on abstractions. Abstractions should not depend on details. Details should depend on abstractions.

**Do This:** Decouple the components by having them depend on abstractions (interfaces or abstract classes) rather than concrete implementations.

**Don't Do This:** High-level modules directly depends on low-level modules.

**Why:** Reduces coupling between components, allowing easier testing, reuse, and modification.

**Example:**

"""python

# Abstraction

class DatabaseInterface:

def save(self, data):

raise NotImplementedError

# Concrete implementations

class MySQLDatabase(DatabaseInterface):

def save(self, data):

# Save data to MySQL database

pass

class PostgreSQLDatabase(DatabaseInterface):

def save(self, data):

# Save data to PostgreSQL database

pass

# High-level module depending on abstraction

class DataProcessor:

def __init__(self, database: DatabaseInterface): # Dependency injection

self.database = database

def process_and_save(self, data):

# Process the data

self.database.save(data) # depends on abstract DatabaseInterface

# Usage

mysql_db = MySQLDatabase()

data_processor = DataProcessor(mysql_db)

data_processor.process_and_save({"key": "value"})

# This decoupling allows easy change to a database without changing data_processor

postgres_db = PostgreSQLDatabase()

data_processor = DataProcessor(postgres_db) # DataProcessor doesnt even need to know the underlying DB changed.

data_processor.process_and_save({"key": "value"})

"""

## 3. Coding Standards for Optimization-Specific Components

### 3.1. Optimizers

**Standard:** Optimizers should be designed as independent components with customizable parameters.

**Do This:** Use class-based structures for optimizers to encapsulate optimization logic and parameters. Leverage inheritance and polymorphism to support different optimization algorithms.

**Don't Do This:** Hardcode optimization logic directly into training loops or other components.

**Why:** This makes it easier switch between different optimization approaches or algorithms.

**Example:**

"""python

# Optimizer Interface

class Optimizer:

def __init__(self, learning_rate):

self.learning_rate = learning_rate

def update(self, model, gradients):

raise NotImplementedError

# Concrete Implementation of Gradient Descent

class GradientDescent(Optimizer):

def __init__(self, learning_rate):

super().__init__(learning_rate)

def update(self, model, gradients):

for layer, gradient in zip(model.layers, gradients):

layer.weights -= self.learning_rate * gradient

# Usage

optimizer = GradientDescent(learning_rate=0.01)

optimizer.update(model, gradients )

# Alternate Optimizer

class Adam(Optimizer):

def __init__(self, learning_rate):

super().__init__(learning_rate)

def update(self, model, gradients):

# Adam optimization implementation

pass

optimizer = Adam(learning_rate=0.001) # Using a different algorithm is very simple

optimizer.update(model, gradients)

"""

### 3.2. Loss Functions

**Standard:** Loss functions should be implemented as independent, modular components.

**Do This:** Create separate classes or functions for each loss function, allowing different models and training loops to reuse or adapt the loss function.

**Don't Do This:** Hard-code the loss function inside the model definition or have multiple unrelated loss functions within the same class.

**Why:** Promotes reuse, allows different loss functions to be used interchangeable, and allows easy testing/validation of different loss approaches.

**Example:**

"""python

# Loss function interface or base class

class LossFunction():

def calculate(self, y_true, y_pred):

raise NotImplementedError("Calculate method must be implemented.")

# Concrete implementation of mean squared error loss

class MeanSquaredError(LossFunction):

def calculate(self, y_true, y_pred):

return np.mean((y_true - y_pred)**2)

# Concrete implementation of cross-entropy loss

class CrossEntropyLoss(LossFunction): # Uses numpy for demonstration purposes

def calculate(self, y_true, y_pred):

return -np.sum(y_true * np.log(y_pred))

# Usage inside a training loop. The specific loss is injected.

def train(model, data, labels, loss_function: LossFunction, optimizer):

# Predict

predictions = model.predict(data)

# Calculate loss

loss = loss_function.calculate(labels, predictions)

# Calculate gradients

gradients = model.calculate_gradients(data, labels, predictions)

# Update the model

optimizer.update(model, gradients)

#Usage

mse = MeanSquaredError()

cross_entropy = CrossEntropyLoss()

train(model, data, labels, cross_entropy, optimizer) # Using different loss functions by changing the injected dependency

train(model, data, labels, mse, optimizer)

"""

3.3. Data Loaders and Preprocessors

**Standard:** Create reusable data loaders and preprocessors as independent components.

**Do This:** Implement data loading and preprocessing steps in separate components, allowing data transformations to be applied independently of model architecture.

**Don't Do This:** Embed data loading and preprocessing logic directly inside model definitions.

**Why:** Makes it easy to change the data source, data transformations or experiment with alternate pre-processing approaches.

**Example:**

"""python

# Data Loader Component

class DataLoader:

def __init__(self, filepath, batch_size, transform=None):

self.filepath = filepath

self.batch_size = batch_size

self.transform = transform

def load_data(self):

# Load data from file

data = load_from_csv(self.filepath)

# Apply transformations if specified

if self.transform:

data = self.transform(data)

# Batch data for training purposes (demonstration)

batched_data = [data[i:i + self.batch_size ] for i in range(0, len(data), self.batch_size)]

return batched_data

# Data Preprocessor Component

class DataPreprocessor:

def __init__(self, scaling_method="standardize"):

self.scaling_method = scaling_method

def __call__(self, data):

# Apply scaling transformations (example is just a simple representation)

if self.scaling_method == "standardize":

mean = np.mean(data)

std = np.std(data)

return (data - mean) / std

elif self.scaling_method == "normalize":

min_val = np.min(data)

max_val = np.max(data)

return (data - min_val) / (max_val - min_val)

return data

# Usage

preprocessor = DataPreprocessor(scaling_method="normalize")

dataloader = DataLoader(filepath="data.csv", batch_size=32, transform=preprocessor) # Data Processing is now decoupled

data = dataloader.load_data()

"""

3.4. Model Building and Configuration

**Standard:** Models should be configured via external configuration files or parameters.

**Do This:** Define model architectures using configuration files or parameter objects, reducing the need to modify code when tweaking the model configuration.

**Don't Do This:** Hardcode model architecture details directly within code.

**Why:** Allows different models or model architectures to be configured without having to change code.

"""python

# Using a configuration dictionary

def create_model(config):

model_type = config.get("model_type", 'linear') # Defaults to linear regression if undefined

if model_type == "neural_network":

layers = config.get("layers", [64, 32, 1]) # Defults to [64, 32, 1] if undefined

# Build layers based on layer configuration

model = build_neural_network(layers)

return model

elif model_type == 'linear':

return LinearRegressionModel() # Instantiate using the default

# build_neural_network function to construct the model

def build_neural_network(layers):

model = Sequential() # Assuming Tensorflow Keras Style sequential API

# Input Layer

model.add(Dense(layers[0], activation = 'relu', input_shape=(10,))) # Dummy input_shape

# Hidden Layers

for i in range(1, len(layers) - 1):

model.add(Dense(layers[i], activation = 'relu'))

# Output Layer

model.add(Dense(layers[-1], activation = 'linear'))

return model

# Load configuration from JSON file

import json

with open("model_config.json", "r") as f:

config = json.load(f)

model = create_model(config) # Model can be easily configured from JSON by modifying the file.

"""

3.5. Evaluation Metrics

**Standard:** Evaluation metrics should be designed as independent components that can be applied to any model or result. Calculation of metrics can happen on a different server/node or in an isolated process and still provide reusable results.

**Do This:** Implement metrics as classes or functions, that take the true values and predicted valuesas input and and return the metric value.

**Don't Do This:** Hard-code metric calculation logic directly within model or training loops.

"""python

# Metric interface or base class

class Metric:

def calculate(self, y_true, y_pred):

raise NotImplementedError("Calculate method must be implemented.")

# Implementation of Accuracy metric

class Accuracy(Metric):

def calculate(self, y_true, y_pred):

correct_predictions = np.sum(y_true == y_pred) #Assumes y_true and y_pred are numpy arrays

total_samples = len(y_true)

return correct_predictions / total_samples

# Implementation of Precision Metrics

class Precision(Metric):

def calculate(self, y_true, y_pred):

#Calculates the total number of true positives

true_positives = np.sum((y_true == 1) & (y_pred == 1))

#Calculates the total number of predicted positives

predicted_positives = np.sum(y_pred == 1)

#Avoids division by zero in scenarios where there are no predicted positives at all

if predicted_positives == 0:

return 0

precision = true_positives / predicted_positives

return precision

"""

3.6. Logging and Monitoring

**Standard:** Logging and monitoring logic should be decoupled from the core component functionality.

**Do This:** Use a logging framework to record component behavior, error messages, and performance metrics. Implement monitoring tools to track component health and resource utilization.

**Don't Do This:** Mix logging and monitoring code directly with component logic, this introduces tangling of concerns.

"""python

import logging

# Configure logger

logging.basicConfig(level=logging.INFO,

format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

# Get logger instance

logger = logging.getLogger(__name__)

class DataProcessor:

def __init__(self, database):

self.database = database

def process_and_save(self, data):

logger.info("Starting to process data...") # Log message

try:

# Process the data

processed_data = self._process(data) # Assuming _process does the processing

# Save the data to database

self.database.save(processed_data)

logger.info("Data processed and saved successfully.") # Logging output

except Exception as e:

logger.error(f"Error processing data: {e}", exc_info=True) # Error logging

raise # Re-raise exception

def _process(self, data):

#Dummy processing steps for example purposes

if data is None:

logger.warning("Data input is None")

return [] #Return empty list

#Log number of records processed

logger.debug("Processing data length: %d", len(data))

return data[::-1]

"""

## 4. Common Anti-Patterns

### 4.1. God Class

**Anti-Pattern:** A class that knows too much or does too much. It violates the Single Responsibility Principle.

**Example:** A single "ModelTrainer" class that handles data loading, preprocessing, model building, training, evaluation, and deployment.

### 4.2. Code Duplication

**Anti-Pattern:** Repeating the same code logic in multiple components.

**Example:** Implementing similar data validation logic in multiple input components.

### 4.3. Tight Coupling

**Anti-Pattern:** Components directly depend on the internal implementation details of other components.

**Example:** A UI component directly accessing the database connection details of a data access component.

### 4.4. Feature Envy

**Anti-Pattern:** A component that frequently accesses data or methods of another component more than its own data and methods.

**Example:** A "ReportGenerator" component heavily relying on the internal methods and data of the "OrderProcessingService" component.

## 5. Technology-Specific Implementation Details

### 5.1. TensorFlow / Keras

**Best Practice:** Use Keras layers and models for defining the components. Implement custom layers to enable specialized behavior if needed.

"""python

import tensorflow as tf

from tensorflow.keras.layers import Layer, Dense

# Custom layer example

class CustomDenseLayer(Layer):

def __init__(self, units, activation=None):

super(CustomDenseLayer, self).__init__()

self.units = units

self.activation = tf.keras.activations.get(activation) #Use Keras functions when possible

def build(self, input_shape):

self.w = self.add_weight(shape=(input_shape[-1], self.units),

initializer='random_normal',

trainable=True)

self.b = self.add_weight(shape=(self.units,),

initializer='zeros',

trainable=True)

def call(self, inputs):

return self.activation(tf.matmul(inputs, self.w) + self.b)

# Usage

model = tf.keras.models.Sequential([

Dense(64, activation='relu', input_shape=(784,)),

CustomDenseLayer(10, activation='softmax') #Using the custom layer

])

"""

### 5.2. PyTorch

**Best Practice:** Utilize "torch.nn.Module" for creating components. Design your components to maximize GPU acceleration.

"""python

import torch

import torch.nn as nn

class CustomModule(nn.Module):

def __init__(self, input_size, output_size):

super().__init__()

self.linear = nn.Linear(input_size, output_size) # Torch Linear layer

def forward(self, x):

return self.linear(x)

# Create an instance of the CustomModule to be incorporated in a model

customNeuralModule = CustomModule(input_size = 5, output_size = 2)

#Push the NN to the GPU if available for acceleration

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

customNeuralModule.to(device)

"""

### 5.3. Scikit-learn

**Best Practice:** Follow the "fit"/"transform"/"predict" pattern for scikit-learn transformers and models.

"""python

from sklearn.base import BaseEstimator, TransformerMixin

from sklearn.preprocessing import StandardScaler

import numpy as np

# Custom Transformer

class FeatureSelector(BaseEstimator, TransformerMixin):

def __init__(self, feature_names):

self.feature_names = feature_names

def fit(self, X, y=None):

return self

def transform(self, X):

return X[self.feature_names]

# Create a pipeline with a feature selector and a scaler

from sklearn.pipeline import Pipeline

#Assuming your data is a pandas df

feature_names = ['feature1', 'feature2', 'feature3']

scaler = StandardScaler() # Scaling implementation

selector = FeatureSelector(feature_names = feature_names)

pipeline_steps = [('selector', selector), ('scaler', scaler)]

pipeline = Pipeline(steps = pipeline_steps)

#Transform the data

data = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])

datatransformed = pipeline.fit_transform(data)

"""

## 6. Security Considerations

### 6.1. Input Validation

**Standard:** All component inputs should be validated to prevent injections and other vulnerabilities.

**Do This:** Implement input validation logic at the component level using appropriate validation techniques for the data type and context.

**Don't Do This:** Rely on global or centralized validation mechanisms only.

### 6.2. Data Sanitization

**Standard:** Sanitize all data before processing and displaying it to prevent security vulnerabilities.

**Do This:** Use escaping and sanitization libraries appropriately, and consistently apply sanitization to all outputs.

**Don't Do This:** Assume that data is always safe and forgo sanitization.

### 6.3. Access Control

**Standard:** Implement appropriate access controls to restrict component usage to authorized users.

**Do This:** Use authentication and authorization mechanisms to ensure only permitted users can access components.

**Don't Do This:** Provide unrestricted access without authentication.

Cline

This guide explains how to effectively use .clinerules with Cline, the AI-powered coding assistant.

Overview

The .clinerules file is a powerful configuration file that helps Cline understand your project's requirements, coding standards, and constraints. When placed in your project's root directory, it automatically guides Cline's behavior and ensures consistency across your codebase.

Key Concepts

Purpose of .clinerules

Defines project-specific guidelines and requirements
Enforces consistent coding standards
Establishes documentation practices
Sets testing and quality requirements
Configures error handling preferences

File Location

Place the .clinerules file in your project's root directory. Cline automatically detects and follows these rules for all files within the project.

Rule Structure

1. Project Overview

# Project Overview
project:
  name: 'Your Project Name'
  description: 'Brief project description'
  stack:
    - technology: 'Framework/Language'
      version: 'X.Y.Z'
    - technology: 'Database'
      version: 'X.Y.Z'

2. Code Standards

# Code Standards
standards:
  style:
    - 'Use consistent indentation (2 spaces)'
    - 'Follow language-specific naming conventions'
  documentation:
    - 'Include JSDoc comments for all functions'
    - 'Maintain up-to-date README files'
  testing:
    - 'Write unit tests for all new features'
    - 'Maintain minimum 80% code coverage'

3. Security Rules

# Security Guidelines
security:
  authentication:
    - 'Implement proper token validation'
    - 'Use environment variables for secrets'
  dataProtection:
    - 'Sanitize all user inputs'
    - 'Implement proper error handling'

Best Practices

Writing Effective Rules

Be Specific
- Use clear, actionable language
- Provide examples where helpful
- Define measurable criteria
Maintain Organization
- Group related rules together
- Use consistent formatting
- Keep critical rules at the top
Regular Updates
- Review rules periodically
- Update based on team feedback
- Document changes in version control

Common Patterns

# Common Patterns Example
patterns:
  components:
    - pattern: 'Use functional components by default'
    - pattern: 'Implement error boundaries for component trees'
  stateManagement:
    - pattern: 'Use React Query for server state'
    - pattern: 'Implement proper loading states'

Integration with Development Workflow

Using with Version Control

Commit the Rules
- Include .clinerules in version control
- Document rule changes in commit messages
- Review rule changes as part of PR process
Team Collaboration
- Discuss rule changes with team
- Maintain changelog for rule updates
- Ensure all team members understand rules

Troubleshooting

Common Issues

Rules Not Being Applied
- Verify file location (must be in root directory)
- Check file formatting
- Ensure Cline has access to the file
Conflicting Rules
- Review rule hierarchy
- Resolve conflicts explicitly
- Document rule precedence
Performance Considerations
- Keep rules concise and focused
- Avoid overly complex rule structures
- Regular cleanup of obsolete rules

Examples

Basic Project Setup

# Basic .clinerules Example
project:
  name: 'Web Application'
  type: 'Next.js Frontend'
  standards:
    - 'Use TypeScript for all new code'
    - 'Follow React best practices'
    - 'Implement proper error handling'

testing:
  unit:
    - 'Jest for unit tests'
    - 'React Testing Library for components'
  e2e:
    - 'Cypress for end-to-end testing'

documentation:
  required:
    - 'README.md in each major directory'
    - 'JSDoc comments for public APIs'
    - 'Changelog updates for all changes'

Advanced Configuration

# Advanced .clinerules Example
project:
  name: 'Enterprise Application'
  compliance:
    - 'GDPR requirements'
    - 'WCAG 2.1 AA accessibility'

architecture:
  patterns:
    - 'Clean Architecture principles'
    - 'Domain-Driven Design concepts'

security:
  requirements:
    - 'OAuth 2.0 authentication'
    - 'Rate limiting on all APIs'
    - 'Input validation with Zod'

Component Design Standards for Optimization

Cline

Overview

Key Concepts

Purpose of .clinerules

File Location

Rule Structure

1. Project Overview

2. Code Standards

3. Security Rules

Best Practices

Writing Effective Rules

Common Patterns

Integration with Development Workflow

Using with Version Control

Troubleshooting

Common Issues

Examples

Basic Project Setup

Advanced Configuration

Related Rules

Testing Methodologies Standards for Optimization

State Management Standards for Optimization

Code Style and Conventions Standards for Optimization

Core Architecture Standards for Optimization

Deployment and DevOps Standards for Optimization