Machine Learning System Design: Template

A quick blueprint for effective ML System Design

The ML system design template provides a structured framework for designing and building machine learning systems. It outlines the key phases, considerations, and best practices throughout the ML system lifecycle. The template typically includes sections for problem navigation, data collection, feature engineering, modeling, offline and online evaluation, model deployment, monitoring, and retraining. It serves as a guide, ensuring that important aspects such as data integrity, model performance, scalability, security, and fairness are appropriately addressed. The template can be customized and adapted to specific project requirements, making it a valuable tool for efficient and systematic ML system design.

Problem Navigation:

The Problem Navigation phase plays a crucial role in setting the foundation for a successful project. It involves visualizing and organizing the entire problem and solution space, allowing stakeholders to gain a comprehensive understanding of the task at hand. This phase requires converting the business problem into a well-defined machine learning problem, articulating the objectives and constraints in a way that can be addressed by ML algorithms. Furthermore, it is essential to establish a strong connection between the business context and needs and the decisions made in the ML system. This alignment ensures that the ML models and algorithms implemented align with the specific requirements and goals of the organization. To measure the performance and effectiveness of the ML system, both online and offline metrics are used. Online metrics assess the system’s performance in real-time, while offline metrics evaluate the model’s performance on historical or pre-collected data. These metrics provide valuable insights into the system’s accuracy, efficiency, and ability to meet business objectives.

Visualize and organize the entire problem and solution space

Convert the Business problem to a Machine learning problem

Connect the business context and needs to the ML decisions.

Metrics (Online and Offline)

Training Data:

The Training Data Collection phase is a critical step in machine learning system design as it lays the foundation for model training and performance. Various methods are employed to collect training data, including manual data collection, web scraping, data augmentation, and leveraging existing datasets. Each method has its own constraints and risks. For instance, manual data collection can be time-consuming and expensive, while web scraping may introduce data quality issues or legal concerns.

To ensure the effectiveness of the training process, it is crucial to balance the positive and negative training samples. This balance is essential to avoid biased models and ensure accurate predictions across different classes or categories.

The Train and Test data split methods are employed to assess the model’s performance. Common techniques include random sampling, k-fold cross-validation, or time-based splitting. These methods help evaluate the model’s generalization capability and ensure that it performs well on unseen data.

In some cases, human labelers are employed to provide ground truth labels for training data. Human labelers play a crucial role in providing accurate annotations, especially when dealing with complex tasks or subjective data. However, relying on human labelers introduces additional challenges, such as inter-labeler variability or the need for quality control measures to maintain consistency.

Overall, the Training Data Collection phase requires careful consideration of the methods used, addressing constraints and risks, balancing training samples, selecting appropriate data split methods, and ensuring the accuracy and reliability of ground truth labels.

Training data collection methods

Constraints/risks with a proposed method

Balancing positive and negative training sample

Train, test data split methods

Human labelers for ground truth?

Feature Engineering:

The Feature Engineering phase involves transforming raw data into meaningful features that effectively represent the underlying problem. Several important steps are undertaken during this phase.

Converting intuitive ideas into concrete features often requires normalization, smoothing, or bucketing techniques. Normalization ensures that features are on a similar scale, smoothing reduces noise or variability, and bucketing groups similar values into distinct categories for easier analysis. Feature selection or importance analysis is conducted to identify the most relevant features for the machine learning task. This helps to reduce dimensionality, improve model efficiency, and focus on the most informative attributes. Dealing with categorical features requires appropriate encoding techniques, such as one-hot encoding, label encoding, or entity embedding generation. These methods enable the incorporation of categorical information into numerical representations suitable for machine learning algorithms.

Understanding the relationship between different features and the target variable is essential for effective modeling. Analyzing feature correlations, performing statistical tests, or visualizations can provide insights into their impact on the target variable.

Handling missing values or outliers is crucial to prevent bias or inaccuracies in the model. Strategies such as imputation, removing outliers, or treating missing values as a separate category can be employed based on the characteristics of the dataset.

Data imbalance is another consideration in feature engineering. If the data is imbalanced, techniques like oversampling or undersampling can be used to address the class distribution and improve model performance.

Addressing bias in features is an important ethical consideration. It involves identifying and mitigating biases that may exist within the data, such as gender or racial bias, to ensure fairness and equity in the model’s predictions.

The Feature Engineering phase is a crucial step in machine learning system design as it transforms raw data into meaningful and representative features. Through careful selection, transformation, handling of missing values or outliers, and addressing bias, this phase plays a vital role in improving the quality and performance of the machine learning model.

Convert intuitive ideas to concrete features — normalization, smoothing, and bucketing.

Encoding categorical features, embedding generation etc.

Identify the most important features for the specific task. Feature Selection/Importance, Relevant ML features

Handle missing values or outliers

Understand different features and their relationship with the target
— Is the data balanced? If not do you need oversampling/undersampling?
— Is there a missing value (not an issue for tree-based models)
— Is there an unexpected value for one/more data columns? How do you know if it's a typo etc. and decide to ignore it?

Handle bias

Modeling:

The Modeling phase involves selecting and implementing a suitable model to address the problem at hand. During this phase, several important considerations and steps are taken.

Modeling choices and tradeoffs need to be evaluated, considering the pros and cons of different models. Each model has its strengths and weaknesses, and the decision to use a specific model depends on factors such as the nature of the problem, the size and quality of the available data, interpretability requirements, computational resources, and time constraints. It is important to weigh these tradeoffs and select a model that best aligns with the project goals and constraints.

Justifying the decision to use a specific model involves assessing its suitability for the task. Factors like the model’s performance on similar problems, its interpretability, and its ability to handle specific data characteristics or requirements play a role in this decision-making process. Additionally, considerations related to overfitting and regularization techniques need to be addressed to ensure the model’s generalization capability and robustness.

The training process involves feeding the model with the prepared training data and iteratively updating its parameters to minimize the chosen objective function. Techniques such as gradient descent, backpropagation, and optimization algorithms are commonly used during training. Regular monitoring and evaluation of the model’s performance on validation data are essential to make necessary adjustments and prevent issues like overfitting.

Risks associated with modeling include overfitting, underfitting, or model performance degradation due to data quality or distribution shifts. Mitigating these risks involves employing techniques such as regularization (e.g., L1 or L2 regularization), early stopping, cross-validation, or ensemble methods to improve model generalization and reduce the risk of overfitting. It is also important to maintain data quality, perform regular validation, and monitor model performance in production to identify and address potential issues.

The selection of neural network or deep learning models is driven by factors such as the complexity and non-linearity of the problem, the availability of large-scale labeled data, and the need for capturing intricate patterns or hierarchical relationships. Neural networks excel in tasks such as image and speech recognition, natural language processing, and recommendation systems due to their ability to learn complex representations from raw data. However, their use requires significant computational resources and extensive data for training. Careful consideration of these factors is necessary when choosing neural network models.

In summary, the Modeling phase involves making informed decisions about model selection, addressing overfitting and regularization, conducting the training process, mitigating risks, and selecting appropriate neural network or deep learning models when warranted by the problem complexity and data availability.

Modeling choices/tradeoffs? — pros and cons of each model over other.

Justify the decision to use a specific model. Overfitting and regularization.

Training process

Risks and how do you mitigate those risks?

Selection of Neural Network/Deep Learning Models and why?

Model Evaluation:

The Offline Model Evaluation phase focuses on assessing the performance and effectiveness of the trained models using consistent evaluation techniques. Consistency is crucial to ensure fair comparisons across different models and iterations.

Hyperparameter Optimization (HPO) is an important aspect during model evaluation. It involves tuning the hyperparameters of the chosen model to find the optimal configuration. Common HPO methods include grid search, random search, and Bayesian optimization. The choice of hyperparameters depends on factors such as model complexity, dataset characteristics, and computational resources. The goal is to find the best combination that maximizes model performance.

When selecting metrics to track, it is essential to justify and articulate the choices. The metrics should align with the specific problem and capture the desired performance aspects. Common metrics include accuracy, precision, recall, F1 score, and area under the ROC curve (AUC-ROC). The selection depends on the problem domain, class imbalances, and business objectives. It is important to choose metrics that provide meaningful insights into model performance and impact on the problem at hand.

Model debugging techniques are employed during the offline evaluation phase to identify and address issues. This involves analyzing model predictions, inspecting feature importance, conducting error analysis, and visualizing model behavior. Debugging helps to uncover potential flaws, biases, or limitations of the model and guide improvements or adjustments.

During offline evaluation, different models can be trained and validated using various algorithms or architectures. This allows for comparison and selection of the best-performing model based on predefined evaluation metrics. Multiple models can be trained with different hyperparameter configurations to explore their impact on performance.

While offline model evaluation provides valuable insights, it is often necessary to complement it with online experimentation, such as A/B testing. Online experimentation allows for real-time evaluation and comparison of models or interventions in a live environment, gathering user feedback and assessing the model’s performance in a production setting.

In summary, the Offline Model Evaluation phase focuses on designing consistent evaluation techniques, performing hyperparameter optimization, justifying metric choices, utilizing model debugging techniques, training and validating different models offline, and considering online experimentation like A/B testing to assess model performance in real-world scenarios.

Design consistent evaluation techniques?

Different hyperparameters optimization (HPO) in the chosen model and why?

Justify and articulate your choice of metrics to track.

Model debugging techniques

Training and validating different models offline

Online experimentation — A/B testing

Deployment:

The Model Deployment phase in machine learning system design involves designing and implementing techniques for deploying the trained model in a production environment. Various considerations come into play during this phase.

The deployment technique can vary based on the specific requirements and constraints. Cloud-based deployment allows for flexible and scalable infrastructure, with platforms like AWS, Azure, or GCP offering convenient services for hosting and managing models. On-premises deployment, on the other hand, provides more control over the infrastructure and data, which may be preferred in certain cases. Edge devices, such as IoT devices or mobile devices, enable deploying models directly on the device itself, enabling real-time predictions without relying on a centralized server.

Model serving mechanisms need to be established to handle prediction requests efficiently. This involves setting up APIs, microservices, or serverless functions that can accept input data and provide predictions or insights in real-time. Model serving frameworks like TensorFlow Serving or FastAPI can be utilized to streamline this process.

Distributed training techniques can be employed during deployment to train models on large datasets or to accelerate the training process. Distributed training frameworks like TensorFlow’s Distributed Strategy or PyTorch’s DataParallel enable training models across multiple machines or GPUs, improving training efficiency.

Model monitoring pipelines are crucial for tracking the performance of the deployed model. These pipelines collect real-time data and measure various metrics, such as prediction accuracy, latency, or resource utilization. Monitoring tools like Prometheus or Grafana can be used to create dashboards and trigger alerts when anomalies or performance degradation are detected.

Model governance and versioning are important considerations in the deployment phase. Establishing proper documentation, version control, and tracking of model changes ensure reproducibility, traceability, and compliance with regulatory requirements. It also facilitates collaboration among team members and enables effective management of model versions.

Continuous deployment and model retraining practices should be established to keep the deployed model up to date. This involves automating the deployment process, integrating with version control systems, and implementing pipelines for continuous integration and deployment. Regular retraining of the model with new data ensures that it remains accurate and relevant over time.

Scalability, reliability, security, and privacy are paramount during deployment. The infrastructure should be scalable to handle varying workloads, ensuring the model can handle increasing demand. Reliability measures, such as redundancy, fault tolerance, and load balancing, should be in place to ensure the availability of the deployed model. Robust security practices, including access controls, encryption, and data anonymization, should be implemented to protect sensitive data and prevent unauthorized access. Privacy concerns should be addressed by complying with relevant regulations and implementing privacy-enhancing techniques, such as differential privacy.

In summary, the Model Deployment phase involves designing deployment techniques (cloud-based, on-premises, or edge devices), implementing model serving mechanisms, exploring distributed training options, establishing model monitoring pipelines, ensuring model governance and versioning, enabling continuous deployment and retraining, and considering scalability, reliability, security, and privacy aspects throughout the deployment process.

Design deployment techniques? cloud-based or on-premises or edge devices

Model serving

Distributed training

Model monitoring pipelines for performances

Model governance and versioning

Continuous deployment and model retraining

Scalability, reliability, security, and privacy

Monitoring and Observability:

The Model Monitoring and observability phase is crucial for ensuring the ongoing performance, reliability, and fairness of the deployed model. This phase involves various activities to monitor and address potential issues that may arise during the model’s operation.

Offline and online performance monitoring techniques are employed to assess the model’s performance. Offline monitoring involves analyzing historical data to evaluate the model’s accuracy, precision, recall, or other relevant metrics. Online monitoring, on the other hand, tracks the model’s performance in real-time using live data. Both approaches help identify deviations or anomalies in the model’s behavior and trigger alerts or interventions when necessary.

Handling data issues is an essential aspect of model monitoring. Data drift, where the statistical properties of the input data change over time, need to be detected and addressed to maintain the model’s accuracy. Data quality and integrity issues, such as missing or inconsistent data, should be monitored and resolved to prevent biases or inaccuracies in the model’s predictions. Outliers in the data need to be identified and handled appropriately to avoid undue influence on the model’s behavior.

Training-serving skew, which refers to differences between the training and serving environments, can impact the model’s performance. Monitoring for such skew and mitigating it through techniques like consistent data preprocessing, version control, or retraining is crucial to ensure the model’s reliability and consistency in production.

Model issues, such as performance degradation, bias, and fairness, need to be monitored and addressed. Tracking the model’s performance over time helps identify degradation in accuracy or other metrics, enabling proactive measures like retraining or model updates. Model bias and fairness monitoring involve assessing the model’s predictions for different demographic groups and ensuring equitable outcomes.

Troubleshooting techniques are employed to address issues and maintain model performance. Model lineage, which traces the origin and transformations of the data and models, helps in understanding and addressing issues that may arise during the deployment. Model explainability techniques, such as feature importance analysis or interpretability methods, provide insights into the model’s decision-making process, aiding in troubleshooting and resolving potential issues.

Model retraining is an important consideration during the monitoring phase. Regular retraining of the model with new data ensures that it remains accurate and up to date, incorporating changes in the underlying patterns or distribution of the data.

Real-time observability is essential for monitoring the model’s behavior in real-world scenarios. Observability techniques, such as logging, monitoring dashboards, or distributed tracing, enable tracking the model’s inputs, outputs, and performance metrics in real-time, facilitating rapid detection and resolution of issues.

In summary, the Model Monitoring phase involves offline and online performance monitoring, handling data issues like drifts and outliers, addressing training-serving skew, managing model issues such as performance, bias, and fairness, troubleshooting using model lineage and explainability, retraining the model, and ensuring real-time observability to maintain the model’s performance and reliability in production.

Offline/Online performance monitoring

Handle data issues:

— -Data Drifts, Data Quality/Integrity, and Data Outliers:

— Training-Serving Skew:

Handle model issues:

— -Model Performance, Model Bias and Fairness

Troubleshooting:

— -Model Lineage, Model Explainability:

Model Retraining:

Realtime Observability:

— — — -

References:

1. Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications by Chip Huyen
2. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan

Further in this series:

Machine Learning System Design Stage: Problem Navigation

Machine Learning System Design Stage: Data Preparation

Machine Learning System Design Stage: Feature Engineering

Machine Learning System Design Stage: Modelling

Machine Learning System Design Stage: Model Evaluation

Machine Learning System Design Stage: Deployment

Machine Learning System Design Stage: Monitoring and Observability