MLOps: Mitigating the Hidden High-Interest Technical Debt in Production AI Systems

In the rapidly evolving landscape of technology, data science and machine learning have emerged as cornerstone components, driving businesses to solve complex, real-world problems across various domains. This transformation has not only revolutionized industry practices but has also generated significant business value. The current hype surrounding machine learning is fueled by the availability of relatively inexpensive, accelerated compute resources such as GPUs, TPUs, and DPUs, coupled with the rapid advancements in fields like large language models and natural language understanding. These developments have made it possible to leverage some of the largest datasets available, prompting businesses to heavily invest in their data science teams and machine learning capabilities. The goal is clear: develop predictive models that deliver unparalleled business value to their clients.

The Rise of Machine Learning Operations (MLOps)

As machine learning continues to gain traction, the complexity of integrating these technologies into existing systems grows. This is where Machine Learning Operations, or MLOps, comes into play. MLOps can be understood as the application of DevOps principles to machine learning systems, fostering a culture and practice that unifies model development and system operations. It embodies the spirit of automation and monitoring throughout the machine learning system construction process, including integration, testing, releasing, deployment, and infrastructure management.

Why MLOps?

The significance of Machine Learning Operations (MLOps) extends far beyond mere integration of machine learning models into existing systems. It directly addresses the often overlooked and underestimated aspect of machine learning projects: the hidden, high-interest technical debt. This debt is not just a minor inconvenience; it represents a substantial barrier to the efficient, effective, and scalable deployment of machine learning systems in production environments.

The Realities of Machine Learning Development

Contrary to popular belief, the development of a machine learning model represents just a fraction—roughly 10%—of the entire workload in a machine learning project. The remaining 90% involves a myriad of critical tasks such as configuration, automation, data collection, data verification, testing, debugging, resource management, model analysis, metadata management, serving infrastructure, and monitoring, among others. These components are vital for the continuous operation of an integrated ML system in a production setting. Neglecting these aspects can lead to substantial inefficiencies and challenges down the line.

The High Cost of Ignoring MLOps

In the absence of a robust MLOps framework, machine learning projects can accumulate significant technical debt, characterized by compromised code quality and operational excellence. This often results from the pressure to prioritize rapid release over quality. While such a strategy may yield short-term gains, it necessitates costly and time-consuming corrections later. This "high-interest" technical debt is particularly perilous in the realm of machine learning due to the experimental nature and the complex operational characteristics of ML projects. As a result, the cost and complexity of maintaining and scaling ML systems escalate dramatically, turning what was once a manageable project into an unwieldy and expensive endeavor.

The Consequences of High-Interest Technical Debt

This high-interest technical debt manifests most alarmingly when the pressure to prioritize release over quality leads to operational compromises. The real challenge in machine learning is not merely building a model but constructing an integrated system that can operate continuously and efficiently in a production environment. When the intricate balance between rapid delivery and high quality is skewed, developers are forced to revisit and rectify issues to achieve the operational excellence initially overlooked.

Ignoring the operational complexities and the extensive requirements for maintaining a production-level ML system can result in a scenario where the cost of rectification far exceeds the initial development expenditure. This scenario is not just hypothetical but a practical reality for many organizations that have ventured into machine learning without a comprehensive understanding of the importance of MLOps.

DevOps vs. MLOps: Understanding the Key Differences

The evolution of DevOps has significantly impacted the development and operation of large-scale software systems, introducing practices that shorten the development cycle, increase development velocity, and ensure dependable releases. Central to achieving these benefits are the principles of Continuous Integration (CI) and Continuous Delivery (CD). However, when these practices are applied to machine learning (ML) systems, several distinct differences emerge due to the unique challenges and requirements of ML projects. Let’s delve into these differences to understand why MLOps is not merely an extension of DevOps but a specialized discipline in its own right.

1. Team Skills

In ML projects, the team composition often includes data scientists and ML researchers who specialize in exploratory data analysis (EDA), model development, and experimentation. These individuals might not possess extensive experience in software engineering or the development of production-level systems. This contrast sharply with DevOps, where the focus is predominantly on the software development lifecycle and operational efficiency, requiring a different skill set.

2. Development

ML development is inherently experimental, requiring iterations over different features, algorithms, model architectures, techniques, and parameter configurations to discover the most effective solution. This experimental nature presents challenges in tracking experiments, ensuring reproducibility, and maximizing code reusability—issues that are less prevalent in traditional software development.

3. Testing

Testing ML systems involves more than the standard unit and integration tests common in software systems. It requires additional layers of validation, including data validation, model quality evaluation, and model validation. These steps ensure that the model performs as expected on real-world data, addressing challenges that do not typically arise in conventional software testing.

4. Deployment

Deployment in ML systems extends beyond merely deploying an offline-trained model as a prediction service. It often involves deploying a multi-step pipeline that automates the retraining and deployment of models. This complexity necessitates automating tasks traditionally performed manually by data scientists, such as training and validating new models, adding another layer of complexity to ML deployments.

5. Production and Monitoring

ML systems face performance degradation not only from suboptimal coding or bugs (as with traditional software) but also from evolving data profiles. This phenomenon, known as model decay, requires continuous monitoring of data profiles and model performance, with mechanisms in place for notification and rollback if performance deviates from expectations. This aspect of ML operations is unique and critical for maintaining system effectiveness over time.

Continuous Integration and Continuous Delivery in MLOps

While MLOps shares similarities with DevOps in areas such as source control, unit testing, integration testing, and continuous delivery, there are notable distinctions:

• Continuous Integration (CI) in MLOps involves not only the testing and validation of code components but also the testing and validation of data, schemas, and models. This expanded scope reflects the broader challenges of ensuring consistency and reliability in ML systems.
• Continuous Delivery (CD) in MLOps transcends the deployment of a single software package or service. It encompasses a system that automatically deploys another service, the model prediction service, and introduces the concept of continuous training. Continuous training, a property unique to ML systems, is concerned with the automated retraining and serving of models to ensure they remain accurate and effective over time.
• Continuous Training (CT) in MLOps represents a unique and essential property of MLOps, distinguishing it from traditional DevOps practices. CT is concerned with the automated retraining and serving of models, ensuring that they adapt over time to changes in data patterns, preferences, or environmental conditions. Unlike traditional software, where updates may focus on feature enhancements or bug fixes, CT in ML systems is crucial for maintaining the accuracy and relevance of predictions. This process involves periodically re-evaluating data, adjusting models based on new information, and seamlessly deploying updated models without interrupting the service. By incorporating CT, MLOps ensures that machine learning models remain effective and efficient, providing ongoing value in dynamic and evolving operational environments.

Exploring the ML System Lifecycle

The machine learning lifecycle is a structured approach to developing, deploying, and maintaining ML models efficiently and effectively. Within the framework of MLOps, this lifecycle is divided into three main phases: the discovery phase, the development phase, and the deployment phase. Each phase encompasses specific tasks tailored to optimize the ML model's performance and applicability. These tasks can be completed manually or can be completed by an automatic ML pipeline.

Phase 1: Use Case Discovery

The discovery phase constitutes 30-35% of the total workload in the ML lifecycle and lays the groundwork for a successful ML project:

• Business Use Case Definition: Define the business problem and the expected outcome to guide the ML model's development.
• Contextual Understanding: Gain insights into the intended users and those impacted by the solution, providing context for the problem.
• Data Extraction: Extract relevant data from various sources for use in the ML task.
• Exploratory Data Analysis: Conduct an initial investigation of the data to discover patterns, identify anomalies, understand the data schema and characteristics, and test hypotheses.
• Data Pre-processing: Clean, normalize, and encode the data to prepare it for use in machine learning models.
• ML Algorithm Selection: Choose the most suitable machine learning algorithms based on the problem's nature and the data's characteristics.

Phase 2: ML Development

The development phase involves hands-on development of the ML model. Contrary to popular belief, this phase constitutes only 15-20% of the total workload in the ML lifecycle.

• Feature Engineering: Develop features from raw data to enhance the ML algorithms' learning effectiveness.
• Model Training and Tuning: Train the ML model with processed data, and adjust the hyperparameters to enhance performance.
• Model Evaluation: Evaluate the model's performance with a separate validation dataset and refine as necessary.

Phase 3: Production Deployment

The deployment phase focuses on integrating the ML model into a production environment, accounting for more than 50% of the workload of the entire ML lifecycle.

• Model Validation: Validate the model's performance against new, unseen data to ensure it generalizes effectively.
• Inference Data Pipeline Creation: Automate the data flow from the source to the model, ensuring that all required pre-processing and feature engineering steps are automated within the inference pipeline consistently with the model development phase.
• Model Inference Service Developing: Serve the model's predictions via an API endpoint using technologies like TensorFlow Serving, Flask, FastAPI, and Docker. Cloud providers also offer managed services like GCP's Vertex AI, AWS SageMaker, and Azure AI.
• Inference Service Deployment: Deploy the model onto endpoint hardware using available cloud provider-managed services or custom hardware solutions.
• Model API Integrations: To serve model predictions to end-users, the model API is typically integrated with mobile or web applications.
• Model Performance Monitoring: Continuously monitor the model's performance to ensure its ongoing accuracy.
• Data Skew and Drift Detection: Monitor the input data profile for changes that could affect the model's performance and initiate updates or retraining as needed.
• Model Retraining: Periodically manually or trigger automated retraining of the model with new data to maintain its accuracy and to adapt to changes in the underlying data distribution.

The level of automation within the machine learning (ML) lifecycle defines the maturity of an businesses's ML process, which is directly proportional to the speed of training new models given new data or training new models with new implementations.

Exploring the Roles within the MLOps Ecosystem

In the rapidly evolving landscape of Machine Learning (ML) and operations (MLOps), the orchestration of an effective lifecycle is pivotal for turning innovative ideas into value-driven solutions. This transformation demands a symphony of skilled ML practitioners, each playing a vital role in the seamless execution of ML projects. Understanding the diversity of these roles and their contributions is essential for anyone looking to navigate or optimize the MLOps ecosystem. Let's delve into these roles, their responsibilities, and how they interconnect to drive success in ML projects.

Product Managers stand at the forefront of the ML lifecycle. They are the visionaries who identify and define the core business challenges that ML solutions can address. By thoroughly understanding market needs, customer pain points, and the competitive landscape, they craft the strategic direction of ML projects. Their role encompasses defining the user case, setting clear objectives, and ensuring that the project aligns with broader business goals. Their insights initiate the MLOps lifecycle, starting with the user discovery phase, where the foundation for impactful ML solutions is laid.

Once the vision is set, Data Analysts and Data Scientists take the baton. Data Analysts are the gatekeepers of data, responsible for sourcing, cleaning, and preprocessing data from a plethora of sources. Their expertise in exploratory data analysis and preliminary feature engineering lays the groundwork for model development. They are the detectives in the data realm, uncovering trends, patterns, and insights that inform the subsequent stages of model development.

Data Scientists then step in with their analytical prowess to select the most suitable ML technologies and algorithms. Their role is critical in training, tuning, and validating ML models to meet the defined objectives. They are the architects of the model, building and refining it until it can accurately predict outcomes or generate insights that address the business challenge.

Data Engineers play a crucial role in transitioning from model development to deployment. They construct robust data pipelines that automate the preprocessing and feature engineering steps, ensuring a smooth flow of data from source to model. Their work facilitates the model's ability to infer from new data, making them indispensable in operationalizing ML solutions.

Machine Learning Developers are responsible for bringing the model to life in production environments. They develop the model inference services, often through APIs, to make predictions accessible to end-users or other systems. Their expertise in API development and integration bridges the gap between ML models and user-facing applications, whether they're mobile apps, web platforms, or internal tools.

Lastly, Machine Learning Engineers and MLOps Engineers are the custodians of the ML system's reliability and efficiency in the real world. They establish continuous training pipelines, integration, and delivery mechanisms to ensure the model remains relevant and accurate over time. Their work epitomizes the ethos of MLOps, focusing on automation, monitoring, and maintenance to facilitate seamless deployment and scalability of ML solutions.

In conclusion, the MLOps lifecycle is a collaborative journey that requires a diverse set of skills and perspectives. From the strategic insight of Product Managers to the technical acumen of ML Developers and Engineers, each role is integral to the lifecycle's success. Understanding these roles and how they contribute to the ML lifecycle is essential for businesses aiming to leverage AI and ML technologies effectively. As we continue to push the boundaries of what's possible with ML, fostering collaboration among these roles will be paramount in transforming innovative ideas into tangible, value-driven solutions.

MLOps Maturity

Let's consider the following three levels of maturity in MLOps, starting from the most common level, which involves no automation, up to the automation of both Continuous Training (CT) and Continuous Integration/Continuous Deployment (CI/CD) pipelines.

MLOps Level 0: Manual Operations

MLOps Level 0 is common among many businesses beginning to apply ML to their use cases. At this foundational level, ML teams, often comprising data scientists, data analysts and researchers, build and deploy state-of-the-art models entirely manually. This stage is characterized by:

• Manual, script-driven execution and transition from one task to another to serve the model as a prediction service, where the experimental code is written and executed in notebooks by data scientists interactively within the discovery and ML development phases.
• The manual script-driven nature of this process makes it very difficult to keep track of all experimentation steps tried, what worked and what didn't, and to maintain reproducibility while maximizing code reusability.
• Data scientists hand over a trained model as an artifact to the ML Engineer to deploy on API infrastructure by placing the trained model in a storage location, checking the model object into a code repository, or uploading it to a model registry.
• A disconnection between ML teams (Data Scientists, Data Analysts) and operational teams (ML Engineers, ML Developers, and Data Engineers) can cause training-serving skew (inconsistency between data preprocessing scripts written by data scientists and the inference pipeline written by Data Engineers) in the production phase.
• Infrequent release cycles within the process assume that there is no requirement for Continuous Integration (CI), Continuous Delivery (CD), Continuous Deployment (CD), and Continuous Training (CT) as the data science team manages a few models and models don't change frequently, with either changing model implementation or retraining the model with new data done only a couple of times per year.
• The ML engineers and ML developers who responsible for the production phase might have their own complex setup for API configuration, testing, and deployment, including security, regression, and load and canary testing. In addition, the production deployment of a new version of an ML model usually goes through A/B testing or online experiments before the model is promoted to serve all prediction request traffic.

This manual, script-driven process might be sufficient when data rarely change or models are rarely retrained. But in most practical production steps, models often break when deployed in the real world. This is because they fail to adapt to changes in the dynamics of the environment or changes in the data that describes the environment. Also, this process makes it challenging, difficult, and adds extra overhead and effort on the frequent retraining of production models and experimentation with new implementations of models and new technologies as and when they become available in the rapidly changing data science environment.

MLOps Level 1: Automating CT with ML Pipeline

The goal of Level 1 is to perform continuous training (CT) of the model through an automated ML pipeline on fresh data, based on live pipeline triggers; this achieves the continuous delivery (CD) of the model prediction service. The characteristics of this MLOps maturity level consist of:

• Rapid, orchestrated experimentation with modularized code and CT pipeline components that are reusable, composable, and potentially shareable across ML pipelines. The transition between ML development tasks is automated, leading to rapid iteration of experiments and better readiness to move the entire pipeline to production. While data analysis and exploratory data analysis (EDA) are done in notebooks, ML development is done using existing templates for ML pipeline components in a reusable manner, allowing for efficient experimentation by simply adjusting model algorithms, architectures, and hyperparameters. ML CT pipeline components should ideally be containerized to achieve the following: Decouple the execution environment from the custom code runtime. Make code reproducible between development and production environments. Isolate each component in the pipeline, allowing components to have their own version of the runtime environment, and use different languages and libraries.
• Experimental-operational symmetry: The pipeline implementation used in the development or experiment environment is the same as that used in the preproduction and production environments, which is a key aspect of MLOps practice for unifying DevOps.
• Pipeline deployment: Unlike Level 0, where you deploy a trained model as a prediction service to production, in Level 1, you deploy an entire training pipeline, which automatically and recurrently runs to serve the trained model as the prediction service.

In this Continuous Training (CT) ML pipeline, it's necessary to implement strategies for data and model validation, pipeline triggers, feature store, and metadata management to achieve a streamlined process that facilitates the continuous delivery of model prediction services.

Data Validation: Before model training, it's essential to verify the quality of incoming data to decide whether to proceed with retraining or halt the pipeline. This involves checking for:

• Data Schema Skews: Anomalies in input data such as unexpected features, missing expected features, or features with unexpected values, which may necessitate halting the pipeline for investigation and possible adjustments.
• Data Value Skews: Significant changes in the statistical properties of the data, indicating evolving data patterns that require model retraining to remain accurate and relevant.

Model Validation: Post-training, the model undergoes rigorous evaluation to ensure it meets production standards before deployment. This includes:

• Evaluation of Predictive Quality: Using test datasets to produce and assess evaluation metrics, ensuring the model's predictive performance is up to standard.
• Comparison Against Existing Models: Ensuring the new model outperforms current models in production, considering overall predictive accuracy and consistency across various data segments.
• Deployment Testing: Verifying infrastructure compatibility and API consistency to ensure smooth integration into the prediction service.
• Online Model Validation: Beyond offline validations, newly deployed models are further tested through canary deployments or A/B testing setups to ensure they perform well under real-world conditions.

Feature Store: An advanced component for Level 1 ML pipeline automation, the feature store centralizes feature management, supporting both batch and real-time serving. It aids in:

• Discovering and reusing feature sets, ensuring consistent definitions across projects.
• Serving up-to-date feature values, reducing training-serving skew by providing a common data source for experimentation, training, and online serving.

Metadata Management: Capturing detailed metadata for each pipeline execution is crucial for tracking data lineage, ensuring reproducibility, and facilitating comparisons and debugging. This metadata includes:

• Versions of pipeline components, execution timestamps, and duration.
• Execution parameters and pointers to produced artifacts, supporting pipeline resumption and rollback as needed.
• Model evaluation metrics, aiding in performance comparison during validation steps.

ML Pipeline Triggers: Automation of ML production pipelines is tailored to specific needs, including:

• On-demand: Manual initiation based on immediate requirements.
• Scheduled: Regular retraining in response to systematic data availability.
• New Data Availability: Ad-hoc retraining when new data becomes available.
• Performance Degradation: Retraining in response to observed drops in model performance.
• Concept Drift: Addressing significant changes in data distributions that affect model accuracy.

MLOps Level 2: CI/CD for New ML CT Pipeline Implementations

For a rapid and reliable update of the CT pipelines in production, there is a requurment of a robust automated CI/CD system. This automated CI/CD system lets your data scientists rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment.