Navigating MLOps for Platform Teams: Key Challenges and Emerging Best Practices

July 25, 2024

What is MLOps?

MLOps applies proven DevOps methodologies to machine learning workflows. While DevOps focuses on enabling closer collaboration between developers and operations teams, MLOps applies to data scientists, model developers, governance experts, and AI infrastructure managers. Bringing these groups closer together simplifies ML development by enabling changes to be more efficiently applied, in a similar way to how traditional software is built. The MLOps lifecycle consists of five main stages:

1. Data Preparation: The data to be used is ingested, sanitized, and transformed into the format that the model will require.
2. Model Development: The model is created by ML engineers and data scientists.
3. Model Training and Validation: The model is trained using the prepared dataset, then tested to ensure it performs as expected.
4. Deployment and Monitoring: The model is deployed into a production environment, ready to serve users. Its performance is monitored by a dedicated team.
5. Iterative Updates: Analysis of the model's operation is used to inform future changes, such as improvements to the training data and the model's code.

At a high level, this workflow is similar to regular software development. However, ML models pose several unique challenges that can upset the process. For example, the prospect of non-deterministic outputs—where the model gives a different answer to subsequent queries, even when the same input is used—and complex dependencies on different datasets can make it hard to accurately test models, while robust oversight is essential to ensure models operate compliantly and remain compatible with prevailing ethical standards. These issues need to be addressed within any MLOps strategy.

Key MLOps Challenges

Implementing MLOps is superficially similar to pursuing DevOps: you need to adopt a new mindset that revolves around close collaboration and automated workflows. Nonetheless, the unique complexity of ML development means it's common to experience struggles along the way—here are some of the main ones you might face.

1. Data Management Challenges

Data quality is critical to the success of ML models. Not only must data be accurate, relevant, and well-formatted, but consistency must also be preserved as new records are added. This is vital to the successful training of reliable models that perform as expected, without producing incorrect or biased output. To maintain quality, your MLOps team needs to develop a robust data processing pipeline to ingest new data, flag potential problems, and process it into the format that the model requires. It's also crucial to ensure the pipeline is scalable as the volume of training data grows, enabling you to continue refining the model without experiencing bottlenecks. Addressing these problems involves data scientists, ML engineers, and IT infrastructure teams.

2. Model Training and Validation

Training is vital to AI model development but this nuanced data science topic can be misunderstood. If you've got high-quality data available, then it should be possible to produce an accurate model that responds well to user queries. However, it can be hard to gauge whether a model has been effectively trained while balancing accuracy with the need for generalization. Deeper training leads to more accurate results, but also causes "overfitting" that limits the model's ability to generalize. The challenge for ML engineers is to develop training processes that ensure a healthy compromise between accuracy and generalization. Training progress also needs to be easily observable so that teams can validate the model's performance and assess when it's ready to deploy.

3. Deployment Complexity

Deploying ML models has distinct challenges compared to regular software systems. To operate at scale, you need significant resources to ensure performance, availability, and low-latency querying. But merely having access to scalable infrastructure isn't enough to operate, as you also need processes that are capable of efficiently moving trained models from test environments into production.

4. Continuous Integration and Deployment (CI/CD) Pipelines

CI/CD pipelines have transformed how software is built and deployed. They're equally applicable to ML workflows, but require special adaptation to ensure they remain scalable and performant. Although ML pipelines can be used to prepare data sources, execute training workloads, and deploy models, the extra complexity of ML systems means it can be harder to configure and maintain these processes.

5. Model Monitoring and Governance

Real-time ML model monitoring and observability is essential so that performance degradation, bias, and drift away from training parameters can be detected. Failure to establish proper monitoring constraints can expose you to governance and compliance issues, including data privacy and transparency concerns if the model delivers inappropriate responses to queries.

Emerging MLOps Best Practices

These challenges can make MLOps seem daunting, but emerging best practices provide a pathway towards effective ML model development and deployment. Here are four key techniques to include in MLOps implementations.

1. Effectively Version and Manage Models

Version control is one of the most crucial components in the software development toolchain, but it can be overlooked in MLOps. Whether you're building a traditional app or an ML model, it's important to have precise versioning that allows you to track changes over time, prevent conflicts, and easily rollback to an earlier revision.

2. Utilize Advanced Deployment Strategies

ML models need to be deployed carefully to ensure changes don't cause unintentional accuracy issues in production. Choosing an advanced strategy such as blue-green deployments helps by allowing engineers to validate the model in its production environment, before it receives real traffic.

3. Continually Monitor and Tune Performance

Dedicated tools can be used to continually monitor model performance in production, then develop changes based on any problems that are found. These could include performance degradation, drift away from expected outcomes, and inaccurate output due to gaps in the training data.

4. Regularly Retrain and Update Models

Retraining and updating of ML models is frequently required to address gaps in the training data, introduce new features, and improve the accuracy of outputs provided to users. You should set clear criteria that trigger a retraining cycle, such as after accuracy reductions are observed or user adoption falls, and develop mechanisms that make retraining a more efficient process.

Advanced MLOps Topics

There's more to MLOps than just building and operating models. It should provide a holistic take on the entire ML lifecycle, from design and data collection through to continual improvement based on performance insights.

Automated Model Retraining

Automated model retraining enables your models to continually advance, even when they're not being actively maintained by developers.

A/B Testing with Machine Learning Models

New model variations need to be evaluated to assess which is the most effective. A/B tests allow you to conduct comparisons in production environments to identify the model that performs the most successfully for users.

Multi-Cloud and Hybrid Deployments

ML models are some of the most demanding IT workloads you'll deploy. Their unique infrastructure requirements can quickly become costly and complex, so it's important to look for strategies that make models easier to utilize without overburdening operations teams.

The Takeaway: Utilize MLOps to Gain a Machine Learning Competitive Advantage

MLOps combines tools and methodologies to achieve a robust ML model development and deployment process. Applying DevOps principles to ML workflows facilitates smoother communication between developers, data scientists, business stakeholders and infrastructure operators, allowing you to successfully train and improve your models.