Part 1 - How to solve Any ML System Design Problem

Exact Framework that you should follow....

Dec 26, 2023

Hi All,

Previously we covered 200+ system design case studies. In the new segment; we are moving forward with - How to solve Any ML System Design Problem and the exact format that you should follow -

Pre-requisite to starting ML System Design -

MUST Complete these data science and ML courses ( since ML system design case studies will involve lots of concepts covered in these courses)

Start here -

Day 1 of ML System Design Case Studies Series : ML System Design Basics

In this post we will cover -

ML System Design Framework

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1. Clarify the Requirements

Scope (features needed), scale, and personalization
Performance: prediction latency, scale of prediction
Constraints
Impact and Cost if we choose to solve this as ML problem
Data: sources and availability

2.Defining the ML objective

Goal
Scaling Requirements
ML category

3.Architectural Components

Non -ML Components
ML Components

4.Data

Take a Dataset
Explore the Dataset
Features and Target Relationship
- Data Balanced
- Missing Values
- Garbage Values
Data augmentation
Data Generation Pipeline
- Data collection/ingestion (offline, online)
- Feature generation
- Feature transform
- Label generation
Feature Importance
ML Pipeline: Data Ingestion
ML Pipeline: Data Preparation
Feature Engineering
ML Pipeline - Data Segregation

5.Model

Build ML Pipeline - Model Train and Evaluation
- Model Selection
(ML Pipeline - Model Train and Evaluation: Hyper Parameter Selection
(ML Pipeline - Model Train and Evaluation: Bias Variance Trade off, Underfitting or overfitting
Draw the ML pipeline
Model Debug
Model Deployment
A/B Experiments
- How to A/B test?
  - what portion of users?
  - control and test groups
  - null hypothesis
ML Pipeline: Performance Monitoring: Metrics Evaluation
- AUC, F1, MSE, Accuracy, NDCG for ranking problems etc.
- When to use which metrics?
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Lets get started and understand what each of above points mean ( and why they are important ) -

1. Clearly Defining the Objective of the ML System:

What it is: This involves articulating the specific goal or purpose that the machine learning system aims to achieve. It encapsulates defining the problem, setting success metrics, and outlining what success looks like.
Why to Use It: Defining the objective helps focus the efforts of the ML system. It provides a clear direction for data collection, model selection, and evaluation. It ensures that the entire system architecture aligns with the intended outcome, avoiding wasted resources and efforts.

2. Future Scaling Considerations for ML Systems:

What it is: Scaling requirements refer to the system's ability to handle increased workloads, larger datasets, and higher demand for computation or storage. This includes horizontal scaling (adding more machines) or vertical scaling (increasing resources on existing machines).
Why to Use It: Anticipating future scaling needs ensures the ML system can adapt to growth without compromising performance. Proper design for scalability accommodates increased data volume, user traffic, or computational requirements efficiently, minimizing downtime and ensuring consistent service quality.

3. ML Categories: Supervised, Unsupervised, Reinforcement Learning, etc.:

What it is: ML categories represent different approaches to learning from data. Supervised learning uses labeled data, unsupervised learning explores unlabeled data, while reinforcement learning involves learning through interaction and feedback.
Why to Use It: Understanding the ML categories helps in choosing the appropriate algorithmic approach based on available data and the problem at hand. For instance, supervised learning is ideal for tasks with labeled data, while unsupervised learning can discover hidden patterns in data without predefined labels. Reinforcement learning suits scenarios where an agent learns to make sequential decisions through trial and error.

1. Architectural Components:

What it is: Architectural components refer to the different building blocks or elements that constitute the system's structure. This includes hardware components, software components, networking elements, and their interactions.
Why to Use It: Designing a system with clear architectural components facilitates understanding, maintenance, and scalability. It enables a systematic approach to building and evolving the system while ensuring its reliability, security, and performance. Clear architectural components also aid in troubleshooting and optimizing the system.

2. Non-ML Components: Infrastructure, Databases, APIs, etc.:

Infrastructure: This encompasses the hardware and software setup needed to support the ML system, including servers, cloud resources, networking equipment, and more.
Databases: Storing and managing data is critical. Databases like relational (SQL), NoSQL, or specialized databases cater to different data storage and retrieval needs.
APIs (Application Programming Interfaces): These facilitate communication and interaction between different components of the system. They allow different software applications to communicate and share data or functionality.
Why to Use It: These components provide the foundational framework for the ML system. Infrastructure ensures computational resources are available, databases store and retrieve data efficiently, and APIs enable seamless interaction between various system elements. Properly chosen and integrated non-ML components streamline the entire system's functionality.

3. ML Components: Models, Algorithms, Training, and Inference Processes:

Models: These are representations of patterns or relationships learned from data. They form the core of the ML system, making predictions or decisions based on input.
Algorithms: ML algorithms are the mathematical procedures used to train models and make predictions. They include regression, decision trees, neural networks, etc.
Training: It involves feeding data to the model to learn patterns or relationships, adjusting parameters to minimize errors, and optimizing model performance.
Inference Processes: Once trained, models make predictions or decisions when presented with new, unseen data.
Why to Use It: ML components are the essence of the system. Models and algorithms drive decision-making or predictions. Proper selection and optimization of models and algorithms, efficient training processes, and seamless inference are crucial for the system's effectiveness.

1. Data:

What it is: Data encompasses all information used by the machine learning system for training, validation, and inference. It includes raw input, labeled or unlabeled datasets, features, and target variables.
Why to Use It: Data forms the foundation of machine learning models. Utilizing relevant, high-quality data is crucial for building accurate and robust models that can effectively generalize to new, unseen data.

2. Dataset Selection: Criteria for Choosing the Dataset:

What it is: Criteria for dataset selection involve aspects like data quality, relevance to the problem domain, diversity, size, and representativeness of the target population.
Why to Use It: Choosing the right dataset is pivotal as it directly influences the model's performance. A well-selected dataset ensures the model learns from meaningful patterns, avoiding biases and overfitting while accurately representing the problem domain.

3. Exploratory Data Analysis (EDA):

What it is: EDA involves examining and visualizing the dataset to understand its characteristics, such as data distribution, statistical properties, missing values, outliers, and correlations.
Why to Use It: EDA helps in comprehending the nature of the data, identifying anomalies or inconsistencies that might affect model performance. It guides data preprocessing and feature engineering strategies.

4. Data Augmentation & Generation:

What it is: These methods involve techniques to increase the volume or diversity of the dataset. Augmentation modifies existing data (e.g., image rotation, text paraphrasing), while generation creates new synthetic data (e.g., generative adversarial networks).
Why to Use It: Augmentation and generation help in improving model generalization by exposing it to more diverse scenarios, reducing overfitting, and enhancing performance in cases where original data might be limited.

5. Data Processing Pipeline:

What it is: The data processing pipeline encompasses the steps involved in data collection, cleaning, feature extraction, transformation, and labeling.
Why to Use It: A robust and well-designed data processing pipeline ensures data is preprocessed consistently, making it suitable for model training. It streamlines the flow of data, ensuring it's in a format conducive to model learning.

6. Feature Importance & Engineering:

What it is: Feature importance involves identifying which features have the most significant impact on the model's predictions. Feature engineering involves creating new features or transforming existing ones to improve model performance.
Why to Use It: Understanding feature importance guides in focusing on the most relevant features, optimizing model performance, and reducing dimensionality or noise in the data.

7. ML Pipeline Segregation:

What it is: This involves dividing the dataset into separate portions for training, validation, and testing to assess the model's performance.
Why to Use It: Segregating the data ensures unbiased evaluation of the model's performance. Training data is used for model learning, validation data for hyperparameter tuning, and testing data for assessing the model's generalization to new data.

1. Model:

What it is: The model is the learned representation of patterns or relationships within the data, enabling predictions, classifications, or decision-making.
Why to Use It: Models form the core of the ML system, translating data into actionable insights or predictions. Well-designed models are essential for accurate and reliable outcomes.

2. ML Pipeline: Training, Evaluation, Hyperparameter Tuning, Bias-Variance Trade-off:

ML Pipeline: It's the sequence of steps involved in training models, evaluating their performance, tuning hyperparameters, and managing the bias-variance trade-off.
Why to Use It: The pipeline ensures a systematic approach to model development, evaluation, and optimization. Training data helps the model learn, evaluation ensures its performance, hyperparameter tuning fine-tunes the model's settings, and understanding bias-variance trade-offs aids in finding the right model complexity for generalization.

3. Model Deployment & Debugging:

Model Deployment: It involves making the trained model accessible for making predictions or inferences in production systems.
Debugging: Identifying and resolving issues or errors that arise during the deployment or operational phase of the model.
Why to Use It: Effective deployment ensures that the model functions in a real-world environment. Debugging strategies are crucial to identifying and rectifying any discrepancies or unexpected behavior in the deployed model, ensuring its reliability and accuracy.

4. A/B Experiments: Methodology, Control Groups, Hypothesis Testing, and Analysis:

Methodology: A/B experiments involve comparing two versions (A and B) of a model or system to determine which performs better.
Control Groups: One version (control group) remains unchanged for comparison against the modified version (experimental group).
Hypothesis Testing & Analysis: Statistical methods are used to assess if differences between versions are statistically significant.
Why to Use It: A/B experiments help in making informed decisions about model changes or updates by providing empirical evidence of the impact on performance. Proper methodology, control groups, hypothesis testing, and analysis ensure reliable conclusions.

Part 2 - How to solve Any ML System Design Problem : Coming soon!

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