Ultimate MLOps Architecture Blueprint for Effortless AI Scaling

Building and deploying AI models can feel overwhelming. The constant pressure to improve accuracy as your data changes and new information comes in can make it challenging to keep up. It's no wonder that businesses are increasingly looking to MLOps architecture to automate those processes, ensure smooth deployment, and maintain performance over time. AI Inference vs Training plays a crucial role in this process, impacting how models are optimized and deployed efficiently. In this article, we'll explore MLOps architecture and how it can help your organization achieve its goals and ease the burden of AI model deployment.

AI inference APIs can help you achieve your objectives, like building an MLOps architecture that effortlessly scales AI models, reduces operational complexity, and accelerates deployment without constant manual intervention.

What are the Common MLOps Architecture Patterns?

A successful machine learning project isn’t just about deploying a working app. It’s about delivering positive business value and ensuring you keep offering it.

When you work on many machine learning projects, some work well during development but never reach production. Other projects make it to production but can’t scale to meet user demand. Yet another project will be too expensive to generate profit after it’s scaled up.

Optimizing MLOps Architecture for Success

Conventional software has DevOps; we have MLOps, which is even more critical. For your development and production workloads to succeed, you need an optimal MLOps architecture.

The diagram tells us there’s more to production-grade machine learning systems than designing algorithms and writing code. Being able to select and create the most optimal architecture for your project is often what bridges the gap between machine learning and operations and, ultimately, what pays for the hidden technical debt in your ML system.

The Reality of Production-Grade Machine Learning Systems

When you think of working on a machine learning project, a very detailed workflow for you may be:

From Development to Deployment: Choosing the Right MLOps Architecture

You might have already developed your model with a workflow like this and want to deploy it and prepare it for production challenges, such as:

• Deterioration
• Scalability
• Speed
• Maintenance and so on

Suppose you’re thinking of life beyond experimentation and development. In that case, you might have to do a thorough rethink, starting with choosing the right architecture to operationalize your solution in the wild.

Key Considerations

Operationalizing a machine learning system at a general level requires a complex architecture or so the famous "Hidden Technical Debt in Machine Learning Systems" paper states.

What should the architecture look like for a machine learning project that serves users in real time? What will you consider? What should you account for?

Common Architectural Patterns for MLOps

As you saw in the (relatively) complex representation of a machine learning system above, MLOps is simply machine learning and operations mixed and running on top of infrastructure and resources.

The architectural patterns in MLOps concern training and serving design. The data pipeline architectures are often tightly coupled with these 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, consider a dynamic training architecture if the input data changes often in production. 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. Based on the input received and the overall problem scope, three different architectures exist.

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), or
• A message broker (serves as the middleman to help coordinate processes between the data job 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 and retraining of 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

Useful when you need continuous model training. For example:

• New data arrives from different sources (like mobile apps, 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.

Static Training Architecture

Consider this architecture for problems where your data distribution doesn’t change much from what was trained offline.

An example could be a loan approval system in which the attributes needed to decide whether to approve or deny a loan undergo gradual distribution change, with 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 arguably the most straightforward 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 will be a content recommendation system (pre-computing recommendations for users before they sign into their account or open an application).

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.

An example of a use case that fits this profile is detecting fraud during a transaction before it is processed completely. Other architectures worth your time are:

• Near real-time serving architecture: functional for personalization use cases.
• Embedded serving architecture: for use cases where data and/or computing must stay on-premise or on an edge device (like a mobile phone or microcontroller).

Now that you’ve seen common MLOps architectural patterns, let’s go ahead and implement one!

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

How to Design an MLOps Architecture

Selecting the optimal MLOps architecture involves more than just technology. The best architecture is designed around the end user's needs, not the preferences of data scientists, MLOps engineers, or IT staff. While this might sound counterintuitive, remember that the end-user is the person or application that will ultimately consume the predictions made by your ML model.

The architecture must consider the necessary project requirements for the project's business success. It should follow template best practices, principles, methodologies, and techniques. I referenced the Machine Learning Lens—AWS Well-Architected Framework practices for best practices and design principles, seemingly the most generalizable template.

Problem Analysis: Understanding the Objective

What’s the objective? What’s the business about? Current situation? Proposed ML solution? Is Data available for the project?

Requirements Consideration: Identifying Success Criteria

What are the requirements and specifications needed for a successful project run? The requirement is what we want the entire application to do and the specifications, in this case, is how we want the application to do it regarding data, experiment, and production model management.

Defining System Structure: Creating the MLOps Architecture Backbone

Defining the architecture backbone/structure through methodologies.

Deciding Implementation: Filling the Structure with Robust Tools and Technologies

Fill up the structure with recommended robust tools and technologies.

Why This Architecture is “Best”: Using the AWS Well-Architected Framework (Machine Learning Lens) Practices

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)

We adopt the five pillars of a well-architected solution developed by AWS. They help build solutions with optimal business value using a standard framework of sound design principles and best practices:

• Operational Excellence: This focus is on operationalizing models in production, monitoring performance, and gaining insights into ML systems to deliver business value while continually improving supporting processes and procedures.
• Security: Emphasizes protecting information, systems, and assets (data) while delivering business value through risk assessments and mitigation strategies.
• Reliability: Ensures the system can recover from infrastructure or service disruptions, dynamically acquire computing resources to meet demand, and mitigate misconfigurations or transient network issues.
• Performance Efficiency: Focuses on efficiently using computing resources to meet requirements and maintain that efficiency as demand changes and technologies evolve.
• Cost Optimization: Enables building and operating cost-aware ML systems that achieve business outcomes while minimizing costs and maximizing return on investment.

I have created a summary table based on these five design principles, which you should consider when planning your architecture.

Well-Architected Pillar

Design Principles for ML Systems

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

Security

• Restrict Access to ML systems
• Ensure Data Governance
• Enforce Data Lineage
• Enforce Regulatory Compliance

Reliability

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

Performance Efficiency

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

Cost Optimization

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

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• AI Infrastructure
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• Machine Learning Inference
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MLOps Architecture in Practice

For solution architects, the design process starts with a specification of problems that the new architecture needs to solve for example:

• Manual data collection is slow and error-prone and requires a lot of effort.
• Real-time data processing is not part of the current data loading approach.
• There is no data versioning, so reproducibility is not supported over time.
• The model's code is triggered manually on local machines and constantly updated without versioning.
• Data and code sharing via a common platform is completely missing.
• The forecasting process is not represented as a business process. All the steps are distributed and unsynchronized, and most require manual effort.
• Experiments with the data and models are not reproducible and not auditable.
• Scalability is not supported in case of increased memory consumption or CPU-heavy operations.
• Monitoring and auditing of the whole process are currently not supported.

Platform Design Decisions

The two main strategies to consider when designing an MLOps platform are:

• Developing from scratch vs. selecting a platform
• Choosing between a:
  • Cloud-based
  • On-premises
  • Hybrid model

Developing from Scratch vs. Choosing a Fully Packaged MLOps Platform

Building an MLOps platform from scratch is the most flexible solution. It would allow the company to solve future needs without depending on other companies and service providers. It would be a good choice if the company already has the required specialists and trained teams to design and build an ML platform.

Prepackaged vs. Custom MLOps Solutions

A prepackaged solution would be a good option to model a standard ML process that does not need many customizations. If available on the market, one option would be to buy a pre-trained model (e.g., model as a service) and build only the data loading, monitoring, and tracking modules around it. The disadvantage of this type of solution is that if new features need to be added, achieving those additions on time might be hard.

Buying a platform as a black box often requires building additional components around it. An important criterion to consider when choosing a platform is the possibility of extending or customizing it.

Cloud-Based, On-Premises, or Hybrid Deployment Model

Cloud-based solutions are already on the market, with popular AWS, Google, and Azure options. With no strict data privacy requirements and regulations, cloud-based solutions are a good choice due to the unlimited infrastructural resources for model training and model serving.

On-Premises vs. Hybrid MLOps Solutions

An on-premises solution would be acceptable for stringent security requirements or if the infrastructure is already available within the company.

The hybrid solution is an option for companies that already have part of the systems built but want to extend them with additional services—e.g., buy a pre-trained model and integrate it with the locally stored data or incorporate it into an existing business process model.

MLOps Architecture Use Case: Financial Institutions’ Macroeconomic Forecasting

Our example use case for this demonstration is a financial institution that has been conducting macroeconomic forecasting and investment risk management for years. Currently, the forecasting process is based on partially manual loading and postprocessing external macroeconomic data, followed by statistical modeling using various tools and scripts based on personal preferences. According to the institution's management, this process is unacceptable due to recently announced banking regulations and security requirements. In addition, the delivery of calculated results is too slow and financially unacceptable compared to competitors in the market. Investment in a new digital solution requires a good understanding of the complexity and the expected cost.

Open-Source MLOps with Minimalist, Composable Architecture

The financial institution from our use case does not have enough specialists to build a professional MLOps platform from scratch. Still, it does not want to invest in an end-to-end managed MLOps platform due to regulations and additional financial restrictions. The institution's architectural board has decided to adopt an open-source approach and buy tools only when needed.

The architectural concept is built around developing minimalistic components and a composable system. The general idea is built around microservices covering nonfunctional requirements like scalability and availability. Striving for maximal simplicity of the system, the following decisions for the system components were made.

Data Management Platform

The data collection process will be fully automated. Due to the heterogeneity of external data providers, each data source will have a separate data loading component. The choice of database is crucial when writing real-time data and reading a large amount of data. Due to the time-based nature of the macroeconomic data and the institution's already available relational database specialists, they chose to use the open-source database TimescaleDB. Providing a standard SQL-based API, performing data analytics, and conducting data transformations using standard relational database GUI clients will decrease the time to deliver the prototype of the platform. Data versions and transformations can be tracked and saved into separate data versions or tables.

Model Development Platform

The model development process consists of four steps:

• Data reading and transformation
• Model training
• Model serialization
• Model packaging

Git-Based Model Training in MLOps

Once the model is trained, the parameterized and trained instance is usually stored as a packaged artifact. Git is the most common solution for code storage and versioning. Furthermore, the financial institution is already equipped with a solution like GitHub, which provides functionality to define pipelines for building, packaging, and publishing the code.

The architecture of Git-based systems usually relies on distributed worker machines executing the pipelines. That option will train the model as part of the minimalistic MLOps architectural prototype.

Model Storage and API Deployment

After training a model, the next step is to store it in a model repository as a released and versioned artifact. Storing the model in a database as a binary file, a shared file system, or even an artifacts repository is an acceptable option at that stage. Later, a model registry or a blob storage service could be incorporated into the pipeline.

A model's API microservice will expose the model's functionality for macroeconomic projections.

Model Deployment Platform

The decision to keep the MLOps prototype as simple as possible applies to the deployment phase. The deployment model is based on a microservices architecture. Each model can be deployed using a Docker container as a stateless service and scaled on demand. That principle applies to the data loading components, too.

Once that first deployment step is achieved and the dependencies of all the microservices are clarified, a workflow engine might be needed to orchestrate the established business processes.

Model Monitoring and Auditing Platform

Traditional microservices architectures already include tools for gathering, storing, and monitoring log data. Tools like Prometheus, Kibana, and ElasticSearch are flexible enough to produce specific auditing and performance reports.

Open-Source MLOps Platforms

A minimalistic MLOps architecture is a good start for a company's initial digital transformation. Keeping track of available MLOps tools in parallel is crucial for the next design phase. The following table summarizes some of the most popular open-source tools.

Kubeflow

• Description: Makes deployments of ML workflows on Kubernetes simple, portable, and scalable.
• Functional Areas:
  • Tracking and versioning
  • Pipeline orchestration
  • Model deployment

MLflow

• Description: Is an open-source platform for managing the end-to-end ML lifecycle.
• Functional Areas:
  • Tracking and versioning

BentoML

• Description: Is an open standard and SDK for AI apps and inference pipelines; provides features like auto-generation of API servers, REST APIs, gRPC, and long-running inference jobs; and offers auto-generation of Docker container images.
• Functional Areas:
  • Tracking and versioning
  • Pipeline orchestration
  • Model development
  • Model deployment

TensorFlow Extended (TFX)

• Description: Is a production-ready platform; is designed for deploying and managing ML pipelines; and includes components for data validation, transformation, model analysis, and serving.
• Functional Areas:
  • Model development
  • Pipeline orchestration
  • Model deployment

Apache Airflow, Apache Beam

• Description: Is a flexible framework for defining and scheduling complex workflows — data workflows in particular, including ML.
• Functional Areas:
  • Pipeline orchestration

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