How Can ModelOps Scale and Govern AI-led Initiatives?

Artificial Intelligence, or AI, has slowly but steadily made inroads into our lives in the form of personal assistants to self-driving cars. Today, a plethora of software applications incorporate AI, machine learning, and deep learning to undertake various activities in disparate domains. These may include bioinformatics, document classification and processing, and understanding natural language, among others. AI-led digital assurance may include speech processing and image classification being applied to computer vision and robotics. AI has allowed the automation of repetitive tasks with minimal or zero human intervention.

Although the potential of artificial intelligence is not lost on many, businesses often balk at implementing the same given the slim return on investment. Technology, even if thought of as enabling for businesses, does not yield suitable outcomes. For most organizations, leveraging AI ML solutions is largely focused on the delivery of analytical models. In fact, the integration of AI with business delivery processes or its operationalization has yet to pick up speed. This is because it takes considerable time for a business to integrate AI into its workflow.

The primary reason why the marriage of AI with business processes has not been taken to a logical conclusion is the lack of skilled resources. For instance, data scientists can create AI-driven algorithms and models to power their systems. However, for most companies, there is a dearth of skilled technical manpower to create and maintain such infrastructure. Further, the complexities surrounding the implementation of AI are many. These include insufficient technical skills, lack of privacy and security, accessibility challenges, less understanding of use cases, and low volume and quality of data, among others. Such reasons may sow the seeds of scepticism in the minds of business stakeholders about AI not having the ability to have any transformative impact. So, what needs to be done to scale and govern AI initiatives? The answer lies in using ModelOps, or AI Model Operationalization.

What is ModelOps?

Models run by AI ML services need to improve their efficiency to enable better decision-making. ModelOps, or MLOps, deals with the management of a wide range of operationalized AI-based decision models. These may include linguistic models, agent-based models, machine learning, rules, knowledge graphs, and optimization, among others. It plays a crucial role in governance and risk management given that an increasing number of models are sent into production. MLOps allow stakeholders to monitor the performance of a model and identify risks, if any. These risks are preempted before they can cause real damage.

In a nutshell, ModelOps is a combination of technologies, tools, and best practices to implement, monitor, and manage ML-driven solutions. Based on DevOps, it allows for the scaling and governing of artificial intelligence initiatives at the enterprise level while deriving the maximum value. Additionally, to learn more about ModelOps, ML assurance, and ML validation from experts, and how they can help accelerate the digital transformation of big organizations, tune into the webinar.

How does ModelOps help businesses?

Models are complex assets, unlike traditional assets, which need lifecycles. To create value and have a greater impact on businesses, they should be automated to scale. The following points answer the question as to why businesses should invest in augmenting the capability of ModelOps:

Risk control: AI-powered algorithms and ML engineering are guiding business initiatives and decisions, thereby impacting risk structures and governance. These can help identify risks facing enterprises early and nip them in the bud before they morph into damage-causing events.

Reduce time to business: Implementing new modeling techniques into existing workflows enables large businesses to leverage MLOps capabilities better. However, they need to be introduced quickly given their short or unpredictable shelf life in a specific domain. Hence, introducing such models quickly can drive the core functionality of ModelOps.

Better transparency: The location and functioning of these models should be known to the stakeholders. This calls for increased transparency and accountability.

Create value for AI-led initiatives: Introducing models into the workflow is likely to unlock the value of AI initiatives for businesses over time. These can help them leverage any market opportunities.

Scaling AI initiatives with ModelOps

Enterprises have been utilizing models to derive suitable business insights and make informed decisions. However, introducing AI and ML models without proper diligence can be risky. This is because many processes are managed through solutions that need constant upgrades. The risks can emerge due to the introduction of new tools, technologies, and governance requirements.

The consequences of facing such risks often mean the non-deployment of models or a substantial delay in their deployment. Such approaches lead to suboptimal outcomes, thereby creating diminished value for the organization. The steps to operationalizing the AI/ML model may include:

Define the model lifecycle: When preparing a model for use in production, an end-to-end process, known as the model lifecycle or MLC, must be established. This is typically done by an enterprise AI architect.

Actual deployment: The model is integrated into a production environment, enabling stakeholders to obtain data-based business decisions. This is typically done by the data scientist.

Monitor in production: The model, in the form of a corporate asset, is then monitored in production systems for business use. Monitoring the health of models in production is typically done by a model operator.

Govern model operations: Finally, the model should be made part of the inventory and properly assessed to increase its efficiency. It should be treated as part of the corporate assets.

Conclusion

To achieve model governance at scale and run stable model operations, every aspect of the model lifecycle should be automated. Also, each model can have various paths of monitoring and continuous improvement. Businesses need to embrace ModelOps and augment its capability to achieve excellence and a guaranteed ROI. To learn more about ModelOps and how it can help businesses remain competitive in the future, log in to the webinar.

Stark is a software Tech enthusiastic & works at Cigniti Technologies. I'm having a great understanding of today's software testing quality that yields strong results