MLOps Strategy Implementation for Business Growth

AI has been the buzzword across numerous industries over the past decade. As tech companies develop intelligent systems that leverage AI and machine learning technologies to streamline processes, many businesses are seeing the value in incorporating AI tools into their existing systems and processes.

Unfortunately, AI deployment can be quite challenging, especially for organizations with limited experience. The lack of industry standards for machine learning frameworks, coupled with ineffective collaboration between production and deployment teams, creates numerous bottlenecks that could hinder effective deployment.

However, with a proper MLOps strategy in place, organizations can effectively eliminate most bottlenecks, ultimately streamlining their model development and deployment practices.

Read on for some of the most effective MLOps strategies for implementing ML models into your business.

What is MLOps?

MLOps, short for Machine Learning Operations, is the culmination of people, practices, processes, and underlying technologies that facilitate the deployment, monitoring, and management of machine learning models in a scalable and fully governed way to provide measurable business value.

Essentially, it lays the foundation for data scientists and development teams to collaborate and leverage automation to deploy and monitor machine learning processes within an organization.

This systematic way of moving models into production allows organizations to eliminate bottlenecks and bring models into production faster and more effectively.

The scale and nature of an MLOps infrastructure ultimately come down to the nature of the organization. MLOps infrastructures can range anywhere from simple, well-vetted, and maintained processes to complex, automated systems designed to streamline the lifecycle of ML models.

Why is having an MLOps strategy so crucial for organizations?

As the world becomes more digitized, organizations are tapping into AI and machine learning technologies in a bid to deliver sleek, personalized experiences. When properly utilized, ML models can also facilitate automation and real-time analytics, thus boosting productivity and revenue.

Unfortunately, most organizations looking to deploy ML models have hit a snag, leaving only 15% of leading enterprises with functional deployments. [1] What’s even more concerning is the staggering amount of money these organizations have poured into their efforts – with little to show for it.

This begs the question, why is ML model deployment so challenging? The biggest reason behind this recurring predicament is the huge skill, collaboration, and motivation gap between development teams like data scientists and model operators like DevOps and software development teams.

MLOps provides a technical backbone for managing the life cycle of machine learning models through automation and scalability. It also facilitates seamless collaboration between the data science teams responsible for creating the models and the model operators responsible for managing and maintaining the models in production environments.

This way, organizations can effectively alleviate some of the issues associated with model deployment and chart a path to reaching the strategic goals they want to achieve with AI.

Essential elements of an MLOps framework

An ideal MLOps framework should be able to deliver machine learning applications at scale and maintain a high level of sophistication for maximum impact. To this effect, organizations must focus on the following critical areas:

• Model deployment

Data scientists utilize various programming languages and machine-learning platforms during model development. In some cases, the creators are oblivious to the intended deployment environment and other critical considerations.

When this happens, organizations are unable to integrate the ML models into environments suited for normal software applications. Continuing on such a trajectory could risk jeopardizing the stability of the production environment, thus limiting the models’ usability.

MLOps provides a framework for streamlining the processes between modeling and production. This way, ML models can integrate seamlessly into the production environment, regardless of the machine learning platform or programming language they were built on.

Some of the best enterprise-grade MLOps systems allow organizations to integrate ML models into their systems and generate reliable API access for production teams on the other end, allowing effective utilization of models in various deployment environments and cloud services.

• Model monitoring

Machine learning models degrade and develop other performance-related issues over time. One of the biggest contributing factors to model degradation is outdated data, which may cause the model to provide irrelevant predictions.

Take an analytics ML model designed to predict customer behavior, for instance. Despite its reliability when first deployed, it may not perform as well after some time. That’s because customer behavioral patterns change over time due to numerous factors, including market volatility, economic crisis, and personal preferences.

As such, a model trained on older data doesn’t represent the customers’ current behavior and cannot make accurate predictions. What’s even more concerning is businesses may not be able to recognize when this happens, increasing the possibility of making decisions that could harm the business.

• Model lifecycle management

Most machine learning models, despite their robust capabilities, are only suited to performing specific tasks. This means that organizations planning to leverage machine learning capabilities across numerous use cases may have to develop several models.

While organizations may gain more benefits from utilizing multiple models, managing the models throughout their lifecycle can be quite challenging. For starters, organizations must ensure that every phase the various models go through is streamlined and approved via a flexible workflow. There are also various challenges that come with automating the models’ implementation process, which is vital for cost-effectiveness and effective model management. [2]

In a bid to curb these challenges, organizations are utilizing various approaches, including:

• Champion/challenger model gating
  The Champion/ challenger model gating approach involves introducing a new ML model by first running it in a production environment and measuring its performance against its predecessor. This helps determine whether the new model is worthy of replacing the previous model, thereby facilitating the continuous improvement of ML model predictions and stability. [3]
• Troubleshooting and Triage
  In addition to selecting the best models and monitoring them, organizations also take the monitoring process a step further through triage, troubleshooting, and fixing inaccurate and poorly performing ML models.
• Model approval
  Deploying an ML model into an organization’s production environment can have a dramatic impact on the organization’s core processes, including production and service delivery. Therefore, it helps to formalize the deployment process by ensuring that all relevant business leaders and technical departments sign off on the model.
• Model updates in production
  ML models need to be constantly maintained and updated. Sometimes, this may require taking the model offline and temporarily swapping it with another one. As such, the process must be conducted in such a way that doesn’t negatively impact the production workflow, thus ensuring business continuity. With an effective ML operations strategy, organizations can automate model lifecycle management with the techniques mentioned above, thereby securing their workflows and ensuring efficient model lifecycle management as they scale.

Production model governance

Model deployment is barred by numerous regulatory and compliance requirements. [4] For instance, compliance regulations set forth by the GDPR and CCPA may prove challenging when it comes to maintaining data privacy. [5]

Regulatory compliance is even more challenging for global organizations, which have to navigate a complex maze of regulations across numerous jurisdictions. To curb these issues, organizations need to create and maintain effective model tracking, which can involve everything from tracking model approvals, interactions, updates, and deployed versions.

With an ML operations strategy in place, organizations can streamline model governance through enterprise-grade solutions that deliver automated documentation, model version control, and complete, searchable lineage tracking for all deployed models.

This way, organizations can better manage corporate and legal risks and minimize model bias in the production model management pipeline.

Key components of an MLOps strategy

Developing an effective ML operations strategy doesn’t just involve focusing on the technical aspects of model deployment, implementation, and management – it should also outline the organization’s goals, with a clear representation of how it will get there.

To this effect, the strategy should be well-distributed across the organizations so that all staff and stakeholders can see it. It should also cover the following key areas:

Current friction points

This applies to organizations that are already utilizing machine learning models. Before deploying a new model, the organization should first determine the current pain points they have with deployed models. This way, they can develop a strategy that eliminates these issues with future deployments.

Ideal workflow

Organizations have different needs when it comes to machine learning solutions. Therefore, when developing a strategy, organizations should first outline what a perfect MLOps solution would look like for their business.

Budget

Deploying, monitoring, and managing machine learning models can be a costly endeavor. Cloud infrastructure costs alone could cost $100 to $300 a month, depending on the model’s complexity. [6]

While cost constraints may not be a big challenge for larger organizations with vast financial resources, smaller businesses may need to evaluate how much their ideal workflow may cost and whether it aligns with the business’s goals.

Short-term solution

Evaluating the organization’s current pain points, budget, and ideal workflow can help identify potential issues with the strategy. In this case, the organization should first identify the most readily available solutions and formulate immediate and medium-term solutions for more complex issues.

Long-term solution

Despite an organization’s best efforts, some problems don’t have an immediate solution. There’s also the possibility of more problems developing in the future. Therefore, the ideal strategy should outline any problems that need to be solved later (including how to solve them). It should also outline any potential problems, followed by a clear plan on how to avoid them.

Ownership and team structure

Like everything in business, effective machine learning operations strategies need a defined ownership and team structure. This way, organizations can assign responsibilities and delegate responsibilities accordingly.

Timeline

Everything in an ML operations strategy should have a defined timeline. This includes all goals, tools, and processes pertinent to the project.

Key principles of an MLOps strategy

To efficiently manage machine learning models, organizations must apply the following key principles.

Automation

The maturity of any ML process is determined by the level of automation in the model, data, and code pipelines. As organizations improve the maturity of their ML processes, they dramatically increase the velocity of training for new models.

Managing multiple models can be quite challenging, so data scientists strive to automate all steps in the ML workflow, such that everything functions optimally without manual intervention. The triggers utilized for automated training and deployment can range anywhere from Callander and monitoring events to changes in the data, application code, and training code.

There are three common levels of automation in MLOps. They include:

• Manual process
  ML implementation starts with a typical, manual data science process with an experimental and iterative nature. As such, each step in the implementation process, starting from data preparation and validation, model training and testing is executed manually. To this effect, organizations leverage Rapid Application Development (RAD) tools like Jupyter Notebooks.
• ML pipeline automation
  In the second stage of ML implementation, MLOps teams focus on executing model training automatically. They also introduce continuous model training, which triggers model retraining whenever new data is available. This level may also include other steps such as data and model variation.
• CI/CD pipeline automation
  The final stage of the process involves the introduction of a CI/CD system to perform fast and reliable machine learning model deployments in a production environment. Contrary to the ML pipeline automation stage, which focuses solely on model training, this stage incorporates all automation steps, including the building, testing, and deployment of data, ML model, and all model training pipelines.

Continuous training. monitoring, evaluation

Effective model deployment involves assessing and identifying the identity, versioning, components, and dependencies of the model’s ‘artifacts’, including the model itself, its parameters, hyperparameters, training and testing data, as well as training scripts.

Due to the varying destinations of these artifacts, organizations need a deployment service that provides model orchestration, monitoring, logging, and notifications to ensure the stability of the model’s code and data artifacts.

The process involves several practices, including:

• Continuous Integration (CI)
  Continuous integration provides additional testing and validation data to improve the model’s testing and code validation components.
• Continuous Delivery (CD)
  Continuous delivery involves creating an automated ML training pipeline that facilitates continuous training and deployment of ML model prediction services. [7]
• Continuous Training (CT)
  Continuous training involves the automated retraining of ML models for re-deployments in production environments.
• Continuous Monitoring (CM)
  Continuous monitoring involves automated monitoring of the model’s production data and performance metrics, which are vital in assessing a model’s alignment with business goals.

Versioning

Versioning can be described as the process of tracking any changes to the data, code, and models used in the ML pipeline. When done right, versioning can ensure that the pipeline is repeatable and reproducible.

The process is typically achieved through version control systems like Git, which allows multiple data science teams to work on the same codebase simultaneously and provides a detailed history of all changes made to the code.

Besides code changes by data science teams, other common reasons for changes in the model and data include:

• Model retraining based on newly available data
• Model degradation
• Model retraining based on new approaches
• Model self-learning
• Model deployment in new applications
• Model rollback to a previous serving version
• Data breaches necessitating revision
• Non-immutable data storage
• Issues with data ownership

Experiments Tracking

The development of machine learning models is a highly iterative and research-centric process. Organizations may execute multiple experiments on model training before deciding on which model to take into production.

One of the most common approaches utilized when experimenting with model development involves using different (Git) branches to track multiple experiments, with each Git dedicated to a particular experiment. This way, each branch’s output represents a trained model.

Organizations can then select an appropriate model by comparing different models based on specific metrics.

Testing

A typical ML development pipeline has three essential components: a data pipeline, an application pipeline, and a model pipeline. As such, the scope of testing ML systems should focus on testing features and data, ML infrastructure, and model development.

Features and data tests: This test starts with data validation, an automatic check for features schema (domain values), and data. To build a schema, MLOps teams typically calculate statistics from the training data. Once calculated, they can use the schema in a semantic role for input data during training and serving or as a definition of expectations. MLOps teams also need to test the relevance of each feature to understand whether new features improve the system’s predictive power.

To this effect, MLOps teams need to:

• Compute the correlation coefficient on different feature columns
• Train the model with one or two features
• Use a subset of features to train a new set of modules
• Measure each feature’s inference latency, data dependencies, and RAM usage and compare it with the predictive power of newly added features
• Eliminate unused or depreciated features from the ML infrastructure and document all removals. In addition to testing performance, MLOps teams should also test the features and data pipelines for compliance. Additionally, all new feature creation code should be tested in units to improve the probability of capturing bugs.

Tests for reliable model development

Model development tests are intended to detect ML-specific errors throughout the model’s lifecycle – from training to deployment and governance.

MLOps teams should include routines when testing ML training. Routines can help verify whether the algorithms utilized make decisions aligned with business objectives. Essentially, the ML algorithm loss metrics like log-loss and MSE should correlate with business impact metrics like user engagement and revenue.

ML models can also go stale, and, therefore, need to undergo stringent staleness tests. A model can be defined as stale if it does not satisfy business requirements or doesn’t include up-to-date information.

Model staleness tests can be conducted using A/B experiments with older models. These experiments typically involve producing an Age vs. Prediction Quality curve to help developers understand how often the model needs to be retrained.

ML infrasctucture tests

ML model training should be reproducible. This basically means that using the same training data on a different model should produce similar models.

To this effect, MLOps teams rely on deterministic training to Diff-test ML models. Unfortunately, deterministic training is hard to achieve due to random seed generation, non-convexity of ML algorithms, and distributed model training. To overcome these challenges, it is advisable to determine the non-deterministic parts in the training data code base and reduce non-determinism in the code.

Reproducibility

Reproducibility refers to the ability to recreate the same results from a machine learning model. With regards to ML workflows, reproducibility means that every phase, including data processing, model training, and deployment, should produce the same results when presented with the same input.

Collabration

Collaboration is vital to the MLOps process and lifecycle. Failure to collaborate effectively in the initial stages of model development might yield significant challenges down the line. For instance, when creating models, data scientists might use programming languages that production teams aren’t familiar with. In this case, the organization may face difficulties in utilizing the model effectively since there isn’t a unified use case.

Therefore, collaboration must begin right from the start. To this effect, organizations should promote organization-wide visibility to ensure that all relevant teams are aware of every single detail.

Final Thoughts

AI and machine learning have permeated nearly every industry. They offer a wide array of use cases that could significantly benefit organizations of all sizes. Unfortunately, model deployment can prove quite challenging without an effective strategy.

Implementing a proper ML operations strategy can help overcome some of the challenges that come with deploying, monitoring, and maintaining machine learning models. Considering the lack of industry standards for machine learning frameworks, utilizing the best practices outlined in this guide can act as a stepping stone toward developing a fully operational ML lifecycle.

References

[1]Forbes. com, AI Stats News: Only 14.6% Of Firms Have Deployed AI Capabilities In Production
https://www.forbes.com/sites/gilpress/2020/01/13/ai-stats-news-only-146-of-firms-have-deployed-ai-capabilities-in-production/?sh=697e612c2650,Accessed on April 29, 2024
[2] Research.aimultiple, ML Model Management: Challenges & Best Practices in 2024
https://research.aimultiple.com/ml-model-management/, Accessed on April 29, 2024
[3]Researchgate. net, Champion-challenger based predictive model selection
https://www.researchgate.net/publication/261459083_Champion-challenger_based_predictive_model_selection, Accessed on April 29, 2024
[4] Iapp.org, Machine learning compliance considerations
https://iapp.org/news/a/machine-learning-compliance-considerations/, Accessed on April 29, 2024
[5] Secureprivacy.ai, Artificial Intelligence, and Personal Data Protection: Complying with the GDPR and CCPA While Using AI
https://secureprivacy.ai/blog/ai-personal-data-protection-gdpr-ccpa-compliance, Accessed on April 29, 2024
[6]Hackernoon. com, Machine Learning Costs: Price Factors and Real-World Estimates
https://hackernoon.com/machine-learning-costs-price-factors-and-real-world-estimates, Accessed on April 29, 2024
[7] Datacamp.com, A Beginner’s Guide to CI/CD for Machine Learning
https://www.datacamp.com/tutorial/ci-cd-for-machine-learning, Accessed on April 29, 2024