How to Design MLOps Architecture That Drives Efficiency

Organizations shifting to data-driven decision-making often struggle to operationalize machine learning. Even if teams successfully build machine learning models, getting them into production can be a long and tedious. MLOps architecture provides the structure to streamline this process. In this article, we will explore AI Inference vs Training, MLOps architecture, its components, and how to implement an effective architecture to help your organization reduce operational complexity, maximize team productivity, and support scalable deployments of machine learning models.

One of the best solutions for achieving your MLOps architecture goals is AI inference APIs. These powerful tools can help your team deploy machine learning models faster and simplify the operational processes for managing models in production.

What is MLOps Architecture and Its Pivotal Role

MLOps architecture provides a structured approach to managing ML models throughout their lifecycle. An effective architecture allows organizations to achieve their unique ML goals and supports team collaboration. It also enables organizations to:

• Automate processes
• Scale operations
• Implement best practices for model governance and compliance

MLOps architecture is critical for enhancing the reliability and performance of ML models in production. It helps address common challenges such as:

• Model version control
• Reproducibility
• Scalability
• Monitoring

MLOps architecture enables organizations to deploy models confidently, knowing they have the frameworks and processes to support continuous improvement.

Key Components of MLOps Architecture

MLOps architecture is a comprehensive framework encompassing various components and processes in the ML pipeline.

The architecture includes the following key elements:

Data Management

This component focuses on:

• Data collection
• Preprocessing
• Versioning

It ensures that high-quality data is available for model training and testing.

Model Development

The model development component covers:

• Model training
• Evaluation
• Versioning tasks

It involves:

• Selecting appropriate algorithms
• Tuning hyperparameters
• Assessing model performance

Model Deployment

Model deployment involves:

• Packaging ML models into containers or deployment artifacts
• Orchestrating their deployment
• Setting up continuous integration and deployment (CI/CD) pipelines

Monitoring and Logging

This component ensures real-time monitoring of deployed models, capturing performance metrics and logging relevant events and predictions. It facilitates:

• Issue detection
• Debugging
• Performance optimization

Model Governance and Compliance

Model governance ensures that models adhere to regulatory and ethical guidelines. It involves:

• Version control
• Documentation
• Maintaining data privacy and security standards

Popular MLOps Architecture Patterns

Several popular architecture patterns exist in MLOps. Two of the most common ones include:

Lambda Architecture

The Lambda architecture combines batch and real-time processing to:

• Handle large-scale data ingestion
• Processing
• Analytics

It allows for historical and real-time data analysis, making it suitable for time-sensitive ML applications.

Kappa Architecture

Kappa architecture is a simplified version of the Lambda architecture. Here, real-time streaming data is directly fed into the processing pipeline, eliminating the need for separate batch and real-time layers.

It offers lower latency and simpler processing but sacrifices some of the Lambda architecture’s capabilities.

Related Reading

• Model Inference
• AI Learning Models
• MLOps Best Practices
• Machine Learning Best Practices
• AI Infrastructure Ecosystem

How to Select the “Best” MLOps Architecture for the Project

Finding the Right MLOps Architecture for Your Project

Every machine learning project is different. To develop a successful MLOps architecture for your project, you must keep its specific requirements in mind, including your organization’s:

• Goals
• Team size
• Regulatory requirements

Architectural Patterns in MLOps

MLOps architectural patterns involve designing model training and serving. The data pipeline architectures are often tightly coupled with the training and serving architectures.

Machine Learning Dev/Training Architectural Pattern

In your training and experimentation phase, architectural decisions are often based on the type of input data you’re receiving and the problem you’re solving. For example, if the input data changes frequently in production, you might want to consider a dynamic training architecture. You might feel a static training architecture if the input data changes rarely.

Dynamic Training Architecture

In this case, you constantly refresh your model by retraining it on the always-changing data distribution in production. Three different architectures exist based on the input received and the overall problem scope.

1. Event-Based Training Architecture (Push-Based)

Training architecture for event-based scenarios where an action (such as streaming data into a data warehouse) causes a trigger component to turn on either:

• A workflow orchestration tool (helps orchestrate the workflow and interaction between the data warehouse, data pipeline, and features written out to a storage or processing pipeline),
• A message broker is the middleman who helps coordinate processes between the data and the training job. You may need this if you want your system to continuously train on real-time data ingestion from an IoT device for stream analytics or online serving. 

2. Orchestrated Pull-Based Training Architecture

Training architecture for scenarios where you must retrain your model at scheduled intervals. Your data is waiting in the warehouse, and a workflow orchestration tool is used to plan the extraction and processing, as well as retrain the model on fresh data.

This architecture is beneficial for problems where users don’t need real-time scoring, like a content recommendation engine (for songs or articles) that serves pre-computed model recommendations when users log into their accounts.

3. Message-Based Training Architecture

This sort of training architecture is helpful when you need continuous model training.

For example:

• New data arrives from different sources (like mobile app, web interaction, and/or other data stores),
• The data service subscribes to the message broker so that when data enters the data warehouse, it pushes a message to the message broker
• The message broker sends a message to the data pipeline to extract data from the warehouse.

Once the transformation is over and data is loaded to storage, a message is pushed to the broker again to send a message to the training pipeline to load data from the data storage and kick off a training job.

Designing Continuous and On-Demand Training Pipelines for Real-Time Machine Learning

It joins the data service (data pipeline) and the training service (training pipeline) into a single system, so that training is continuous across each job. For example, you may need this training architecture to refresh your model on real-time transactions (fraud detection applications).

You can also have a user-triggered training architecture, where a user requests the training pipeline service to begin training on available data and write out the model data, perhaps the training report.

Static Training Architecture

Consider this architecture for problems where your data distribution doesn’t change much from what was trained on offline. An example of a situation like this could be a loan approval system, where the attributes needed to decide whether to approve or deny a loan undergo gradual distribution change, and a sudden change only in rare cases, like a pandemic.

Serving Architecture

Your serving architecture is very varied. To successfully operationalize the model in production, it goes beyond just serving. You must also monitor, govern, and manage it in the production environment.

Your serving architecture may vary, but it should always consider these aspects. The serving architecture you choose will depend on the business context and the requirements you develop.

Common Operations Architecture Patterns

Batch Architectural Patterns

This is the simplest architecture for serving your validated model in production. Your model makes predictions offline and stores them in a data storage that can be served on demand. You might want to use this serving pattern if the requirement doesn’t involve serving predictions to clients in seconds or minutes.

A typical use case is a content recommendation system (which pre-computes recommendations for users before they sign into their accounts or open applications).

Online/Real-Time Architectural Patterns

There are scenarios when you want to serve model predictions to users with minimal delay (within a few seconds or minutes). You may want to consider an online serving architecture that’s meant to serve predictions to users in real time as they request them.

Detecting fraud during a transaction before it is processed completely is an example of a use case that fits this profile.

Selecting the Best MLOps Architecture for Your Project

MLOps architecture is not one-size-fits-all. Like any other product or solution you want to architect, coming up with the right design is very much problem-specific. You will often find that similar problems may have only slight architectural variations. So, best can be subjective, and I want to clarify it in this article.

What defined as the best architecture is one that;

• It is designed around the needs of the end-user.
• Takes into account the necessary project requirements for the project’s business success.
• It follows template best practices, principles, methodologies, and techniques; for best practices and design principles, I referenced the Machine Learning Lens, AWS Well-Architected Framework practices, which are the most generalizable templates.
• Is implemented with robust tooling and technologies.

Choosing the Right MLOps Architecture: Balancing Maturity, Cost, and the Four Pillars of Operational AI

You will also find that some of these may or may not apply to you based on your MLOps maturity level, driving even more subjectivity in the choice of architecture. Regardless, I gave full details on the project, including the MLOps maturity level, taking the cost of running the system into account. To keep things consistent, our project use case takes into account the four pillars of MLOps:

• Production model deployment
• Production model monitoring
• Model governance in production
• Model lifecycle management
  • Retraining
  • Remodeling
  • Automated pipelines

A Structured Approach to ML Architecture Design: From Problem Framing to AWS-Backed Implementation

To show you how to think about these architectures, I follow the same template:

1. Problem analysis: What’s the objective? What’s the business about? Current situation? Proposed ML solution? Is data available for the project?
2. Requirements consideration: What requirements and specifications are needed for a successful project run? The requirement is what we want the entire application to do, and the specifications, in this case, are how we want the application to do it, in terms of data, experiment, and production model management.
3. Defining system structure: Defining the architecture backbone/structure through methodologies.
4. Deciding implementation: Filling up the structure with recommended robust tools and technologies.
5. Deliberating on why such architecture is best using the AWS Well-Architected Framework (Machine Learning Lens) practices.

Adapting Good Design Principles from AWS Well-Architected Framework (Machine Learning Lens)

1. Well-Architected Pillar: Operational Excellence

• Establish cross-functional teams.
• Identify the end-to-end architecture and operational model early in the ML workflow.
• Continuously monitor and measure ML workloads.
• Establish a model retraining strategy: Automation? Human intervention?
• Version machine learning inputs and artifacts.
• Automate machine learning deployment pipelines.

2. Well-Architected Pillar: Security

• Restrict access to ML systems.
• Ensure data governance.
• Enforce data lineage.
• Enforce regulatory compliance.

3. Well-Architected Pillar: Reliability

• Manage changes to model inputs through automation.
• Train once and deploy across environments.

4. Well-Architected Pillar: Performance Efficiency

• Optimize compute for your ML workload.
• Define latency and network bandwidth performance requirements for your models.
• Continuously monitor and measure system performance.

5. Well-Architected Pillar: Cost Optimization

• Use managed services to reduce the cost of ownership.
• Experiment with small datasets.
• Right size training and model hosting instances.

Related Reading

• AI Infrastructure
• MLOps Tools
• AI as a Service
• Machine Learning Inference
• Artificial Intelligence Cost Estimation
• AutoML Companies
• Edge Inference
• LLM Inference Optimization

4 Major Trends and Innovations in MLOps Architecture

1. Automated MLOps Workflows: Streamlining Machine Learning Operations

The relentless pursuit of efficiency has led to automating various MLOps processes. From hyperparameter tuning to model deployment, automation tools are streamlining tasks that were once manual and time-consuming.

Automated MLOps workflows accelerate model deployment and reduce the risk of human error, making the entire process smoother and more reliable.

2. Explainable AI and Model Interpretability: Peering Inside the Black Box

The ‘black box’ nature of complex machine learning models has long been a concern. In response, the trend of explainable AI (XAI) is gaining momentum.

MLOps architectures are now integrating techniques that illuminate the inner workings of models. This enables stakeholders to understand how decisions are made and ensures regulatory compliance.

3. Federated Learning at Scale: Collaborative Learning without the Data

Privacy concerns and data security have led to federated learning, where models are trained collaboratively on decentralized data sources. This approach maintains data on local devices or servers, addressing privacy concerns while enabling large-scale model training.

MLOps is embracing federated learning to allow organizations to harness insights from diverse data sources without compromising data privacy.

4. Continuous Integration and Continuous Deployment (CI/CD) for ML: Automating Machine Learning Model Operations

CI/CD practices are borrowed from software development and applied to ML model development. MLOps is embracing CI/CD pipelines that automate the:

• Testing
• Integration
• Deployment of ML models

This leads to faster iteration cycles and more robust models.

Related Reading

• LLM Serving
• LLM Platforms
• Inference Cost
• Machine Learning at Scale
• TensorRT
• SageMaker Inference
• SageMaker Inference Pricing

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