There is no denying the fact that AI is a game changer for every industry. In fact, every department plans to integrate some kind of AI solution to derive business value. Still there is a stark difference in the enthusiasm reflected about AI and the actual adoption of AI products into organizational processes. A survey by Insider Intelligence found 42% of North American companies are yet behind adopting AI or ML (machine learning), a sub-branch of AI. This pops up the question – in spite of strong buy-in what makes productizing AI and ML solutions difficult for enterprises?
The Challenges of AI/ML Product Adoption by Businesses
The ML development process consists of several stages and each stage has its unique set of challenges. We have broadly categorized the typical challenges you will encounter while developing a ML model:
Data Constraints
Training ML models demand labeled, normalized, bias-free, and contextual data. Enterprises often lack an underlying data architecture and appropriate data governance making it hard to procure good quality data. Then there are compliance regulations making data unauthorized for use especially if the enterprise intends to train a third-party ML model.
Lack of technical experience
The standard ML model lifecycle includes more steps compared to the software development lifecycle; data collection and processing, model development, model versioning and integration, plus ongoing model monitoring for performance, security, and effective governance. An often ad hoc approach to ML model development creates misalignment between data scientists and operational professionals often using different tools. Models are typically created using data science-friendly languages and platforms unfamiliar to operations teams and their services. Lack of a proper machine learning governance structure regarding models, with system, lifecycle, user logs, stifles troubleshooting, and legal and regulatory reporting. Organizations that don’t properly monitor their models end up creating models that are difficult to integrate into the existing system and processes.
Failing to see business value
Companies just setting out on AI/ML research may find a disconnect between creating models and delivering business value. AI Strategy, and the process to deliver AI, should prioritize alignment to an organization’s business strategy over perfecting models. Organizations that rely on data science teams to plan the next move for building AI feature often fail to derive business value from their research efforts. This arises because Ops teams are geared towards optimizing run time environments upon their cloud, resource managers, role-based services, etc. Data Science teams are not only unaware of any considerations these dependencies require but are typically oblivious to them altogether and hence models they create do not take these into consideration at all. Leading to a “brains in a jar” situation of sitting on a shelf models.
MLOps establishes a strong platform for seamless collaboration between the operations and data team. It serves as a technological backbone for managing the machine learning lifecycle through automation and scalability.
What is MLOps?
MLOps is a set of practices and standardized workflows for creating a repeatable, continuous, and automated process for building and deploying machine learning models. Born out of agile and DevOps principles, MLOps intends to deliver the benefits of AI to the organization in a faster way.
Fundamental Principles Governing MLOps
Automation
MLOps provides automation through three levels. At the basic level manual processes of data cleansing is automated to prevent redundant data processing and making data accessible to all consumers. At an advanced stage, automation pipelines are built for model training and retraining to avoid model concept drifts or loss of relevance in its outputs. The highest maturity level of MLOps witnesses automatic build, test, and deploy data, models, and ML training pipeline components through CI/CD.
Reproducibility and Versioning
The goal of reproducibility is to ensure that ML models and their associated data can be easily replicated across different stages of the model lifecycle (data processing, model training) and across various production environments. Versioning is a key enabler of reproducibility. Unlike standard software products, machine learning models can generate multiple code branches during the development and training stages, meaning there’s more code (and data) to log, store, and version control. Moreover, models generate multiple artifacts (reports, vocabularies, etc.) during the training stage that also need to be documented and versioned to ensure reproducibility. By tracking ML models and data sets with version control systems, the versioning process ensures that all model elements are properly documented and thus can be replicated if needed.
Monitoring
ML models are prone to concept drift over time. As predictions become less accurate due to changes in market data, your algorithm needs to be retrained. However, noticing the exact moment of model degradation isn’t easy. MLOps Community states that most ML teams (72%) require a week or more to detect and fix a model performance issue. Automated model training monitor several factors like data version changes, dependency upgrades, mismatches in the data schema, GPU memory use, network traffic, etc. Model performance drift can happen due to changes in data or monitored conditions. Early detection means faster remediation.
Implementing MLOps Across the AI Product Lifecycle
Envision
This is the planning phase when data scientists, business experts, and operational teams sit together to envision the values of an AI product. Identify what are redundant tasks and could be automated. Identify risks and ethical considerations to guide future build, deploy, and monitoring activities. Your team should be vigilant enough to spot customer issues earlier than they get exposed before the customer.
Build
This stage involves implementing model governance guidelines, production guardrails, and a robust testing approach. Model governance includes model version controlling, automated documentation, and model lineage tracking. Test your model against the metrics that reflect the value the organization wants. Utilize traditional statistical methods to evaluate and select models as they are being built. Identify and eliminate business risks related to bias, ethics, compliance, vulnerabilities.
Deploy
The engineering aspects of MLOps help ensure that models are deployed in a way that aligns with the organization’s standard software release methods. Deployment with an objective to derive business value promotes models to be integrated in an organization’s products and business processes seamlessly. Train employees on how to use the model’s reporting and analytics in their jobs and create awareness about the changes in workflow. Establish guardrails and take appropriate measures for data protection for customers and employees.
Monitor
Establish monitoring processes to provide governance oversight over the models to help ensure that they continue to provide the expected business value. It’s important to catch when models are going astray and losing their predictive power (concept drift) and/or when the patterns in the data feeding the model have changed (data drift). The guiding principles defined in the envision phase continue to safeguard the investment in monitor phase.
Benefits of MLOps
With MLOps capabilities in place, organizations can start focusing on things that really matter, scaling AI capabilities throughout the organization, while simultaneously tracking KPIs that matter to each team and department. Business leaders, in turn, get more information about the benefits, risks, and ROI of different AI use cases and can make more informed decisions.
MLOps allows data scientists to focus on what’s important — discovering new use cases to tackle, working on feature discovery, and building more in-depth business expertise. On one hand, the solution will automate many parts of their day-to-day life, and on the other, it will help Data Scientists effectively collaborate with their Ops counterparts, offloading much of the burden of day-to-day model management.
A robust MLOps system simplifies deployment of ML models on an application for developers by supplying a straightforward deployment and versioning system, backed by a clear and easy-to-manage API.
Conclusion
As it is evident from the above article, MLOps accelerates AI product delivery and maturity. It establishes clear roles and boundaries of every employees involved in an AI/ML project and sets tangible metrics to evaluate if organizations are meeting their expected goals from AI investments. At Gleecus, we are helping businesses to demonstrate ROI from ML and AI models through expert technology guidance, use case evaluation, and hands-on support with productizing ML models.
