Elevate AI development by applying MLOps principles: Professionalizing the AI development process.

Many companies are eager to use artificial intelligence (AI) in production but struggle to achieve real value from the technology.

What's the key to success? Creating new services that learn from data and can scale across the enterprise involves three domains: software development, machine learning (ML) and, of course, data. These three domains must be balanced and integrated together into a seamless development process.

Most companies have focused on building machine learning muscle by hiring data scientists to create and apply algorithms capable of extracting insights from data. This makes sense, but it’s a rather limited approach. Think of it this way: They’ve built up the spectacular biceps but haven’t paid as much attention to the underlying connective tissues that support the muscle.

Why the disconnect?

Focusing mostly on ML algorithms won’t drive strong AI solutions. It might be good for getting one-off insights, but it isn’t enough to create a foundation for AI apps that consistently generate ongoing insights leading to new ideas for products and services.

AI services have to be integrated into a production environment without risking deterioration in performance. Unfortunately, performance can decline without proper data management, as ML models will degrade quickly unless they’re repeatedly trained with new data (either time-based or event-triggered).

Professionalizing the AI development process

The best approach to getting real and continuous value from AI applications is to professionalize AI development. This approach conforms to?machine learning operations (MLOps), a method that integrates the three domains behind AI apps in such a way that solutions can be quickly, easily and intelligently moved from prototype to production.

AI professionalization elevates the role of data scientists and strengthens their development methods. Like all scientists, these professionals bring with them a keen appreciation for experimentation. But often, their dependence on static data for creating machine learning algorithms which they developed on local laptops using preferred tools and libraries impedes production AI solutions from continuously producing value. Data communication and library dependency problems will take their toll.

Quite frankly, there are a number of AI powerhouse started to explore AI professionalization so that the work of data scientists connects with a more collaborative development workflow. Innovative ideas such MLOps processes to seamlessly integrate ML algorithms with?DevOps?and DataOps best practices. For complexity design in data management, application development and ML development and familiarity with how data scientists, software developers and data managers all prefer to work put the AI powerhouse in a prime position to integrate these domains and roles.

Data scientists can continue to use the tools and methods they prefer, their output accommodated by loosely coupled DevOps and DataOps interfaces. Their ML algorithm development work becomes the centrepiece of a highly professional factory system, so to speak.

Smooth pilot-to-production workflow

User's in AI applications are just as stable as any other application and we mitigate the complexity to do that. New integration mechanisms as "technology-agnostic" services include all the capabilities to create or recreate companies' AI environments from start to finish. The development services also include pre-built MLOps quickstarts to slide into place as needed. Customers can run the environment on the cloud or on-premises.

Pilot AI solutions become stable production apps in short order. DevOps technology and techniques such as continuous integration and continuous delivery (CICD) have standard templates for automatically deploying model pipelines into production. By using model pipelines, training and evaluation can happen automatically if needed when new data arrives, for instance without human involvement.

MLOps versioning and tracking components will ensure that everything can be reused, reproduced and compared if necessary. Advanced monitoring provides end-to-end transparency into production AI use cases (including data and model pipelines, data quality and model quality and model usage).

Using an innovative MLOps approach, the AI powerhouse is able to bring the pilot-to-production timeline for example in cases of one U.S. company’s AI app down from six months to less than one week. Another example for a UK company, the window for delivering a stable AI production app shrank from five weeks to one day.

The transparency of AI solutions, and confidence in their agility and stability, are critical. After all, the value lies in the ability to use AI to discover new business models and market opportunities, deliver industry-disrupting products and creatively respond to customer needs.