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

MLOps is a new discipline that defines processes and best practices for effectively managing machine learning (ML) development and deployment workflows. With demand for ML and generative AI apps representing the latest push in the software industry, it’s increasingly crucial you can utilize these technologies effectively within your products. MLOps is a pragmatic way to address common pain points encountered when building models, releasing them to users, and governing their operation.

Nonetheless, MLOps isn’t always a perfect solution. In this article, we’ll explore some of the challenges you can encounter, then look at the best practices and advanced capabilities that help MLOps implementations achieve their potential. This will guide you towards creating an ML development process that delivers consistent value to your developers, users, and business stakeholders.

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. It can be tackled by introducing data warehousing solutions that facilitate the efficient storage, querying, and validation of data at scale, reducing your dependence on manual procedures.

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 model becomes so closely aligned with its training data that it’s unable to respond appropriately to new inputs. This makes the model appear less accurate in real-world use.

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. This is best automated using dedicated pipeline tools that test the model using a separate validation dataset. Exposing the model to a combination of previously seen and unseen inputs allows accuracy and generalization scores to be calculated.

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.

Problems in this area typically derive from tooling issues. The tools used by data scientists and model developers to build new models aren’t necessarily the same as those needed to deploy to the cloud. Similarly, it can be hard to reconcile model formats and dependencies between platforms—settling on open standards such as Open Neural Network Exchange (ONNX) is the best way to ensure interoperability.

Maintaining consistency between environments is another challenge: it’s crucial that models can be depended upon to deliver the same performance in production as that observed in training and validation environments. Use of technologies such as containerization to package models with their dependencies and training data helps to solve this problem.

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.

One of the potential problems is the need to continually tune pipelines as the model evolves. Whereas software pipelines tend to be fairly static, ML development is inherently experimental. Having to go back to the platform or operations team to make CI/CD changes prior to model deployment can cause frustrating productivity bottlenecks.

Attaining reliable pipeline reproducibility can also be challenging. Different combinations of model and parameter can deliver different outputs, while non-deterministic models make it challenging to reproduce CI/CD pipeline results locally, and vice versa.

Hence, CI/CD for MLOps is best implemented in two stages: first, the pipeline configuration to build and test the model’s source code—similar to a regular DevOps workflow—then a pipeline that trains, validates, and deploys the actual model. This second stage demands the input of data scientists and model engineers, along with integrations with ML testing and monitoring suites so the model’s accuracy can be analyzed. The pipeline configuration must then be continually updated as model changes are made.

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.

Maintaining regulatory compliance and adhering to applicable ethical and moral standards is another component of ML model governance. This requires your MLOps workflow to be independently auditable, with documentation about how models and processes function readily available to prove compliance. MLOps tools and processes should be designed to produce this information, such as by automatically archiving the results of training runs and retaining pipeline job logs.

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.

Use of dedicated model management tools such as Chalk, Neptune AI, and Rafay AI Suite allow you to robustly catalog and version your model releases. This makes it easier to iterate on your models and ensure the correct update is running in production.

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. The new “green” deployment is not exposed to users until it’s manually promoted to become the blue release.

Incremental rollouts using a canary strategy are another way to safely deploy changes, while only exposing them to a subset of users. The proportion of traffic directed to the new version of the model is gradually increased as the rollout progresses, providing an opportunity for more real-world issues to be promptly found and corrected.

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. Analysis of model learning curves, prediction distributions, and hardware utilization metrics reveals insights that can inform future optimizations.

Use of a dedicated ML model management platform provides vital assistance in the ongoing tuning of deployed models. Solutions that index your models, facilitate unified maintenance across environments, and provide single-pane-of-glass monitoring reveal key insights and enable updated models to be efficiently released. They ensure your models maintain optimum effectiveness.

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.

This is best handled using an automated pipeline-driven workflow. Newly ingested training data should be processed, stored, and then automatically initiate a retraining run. You then need to validate the updated model, publish it as a new version, and manage its deployment into production as a canary or green release, with the ability to rollback if a regression occurs. The retraining cycle can then begin anew.

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. The following topics represent some further ways to succeed at MLOps and gain an advantage over less experienced AI and ML competitors.

Automated Model Retraining

Automated model retraining enables your models to continually advance, even when they’re not being actively maintained by developers. A dedicated pipeline can be used to monitor the model, spot data drift, performance, and accuracy issues, then respond by producing a new release that’s prepared with the latest training data you’ve collected.

This process can be integrated into an existing MLOps framework by using the features available in model management platforms or by developing your own custom workflow implementation. At its simplest, a retraining strategy could be built by using a tool like Apache Airflow or Argo Workflows to launch jobs automatically in response to trigger events.

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.

A/B testing for MLOps is implemented similarly to regular software deployments. You can use a canary strategy to split traffic between model versions, then analyze how user behavior varies. However, it’s important to ensure your test is structured in a way that lets you make an informed analysis of model performance. You need to start with a clear hypothesis, decide how you’ll determine whether changes are statistically significant, and then tie those back to characteristics of the model or training data that you can alter to modify the test’s outcome.

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.

Utilizing multi-cloud and hybrid cloud deployments provides advantages by allowing you to mix and match infrastructure components each time you deploy a new model. This can help keep costs low and ensure individual models run on the provider that’s most optimal for their requirements. Any extra complexity incurred by maintaining multiple cloud accounts can be mitigated by using multi-cloud management tools that let you centrally standardize security policies, access controls, and environment configurations.

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.

Compared to less structured approaches, MLOps provides clear strategic value as your organization embraces gen AI, big data, and ML. The ability to reliably deploy models, tune their performance, and carry out retraining and A/B testing represents a competitive advantage that keeps you ahead of your peers. This makes it more likely you’ll realize a good ROI on your ML solutions.

Ready to transform your AI, ML, and LLM development with MLOps? Start now using Rafay’s AI Suite, a development of our leading PaaS offering that lets your platform teams, devs, and data scientists efficiently build integrated AI workflows.

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