March 30, 2021 By Julianna Delua 4 min read

The scale, speed and volatility of the global business climate are forcing a reckoning to our approach to decision making, operations and use of technology. From expense and liquidity management, to customer engagements, to critical response, businesses can improve their agility and broaden use cases across industries by harnessing data science tools, open source communities, and industry know-how.

One crucial mandate in operationalizing trusted AI is building a ModelOps practice to optimize models and applications across clouds. The talent gap in AI requires unifying diverse teams across regions, and establishing ModelOps can help scale innovation and make AI explainable. For AI models and applications to generate value, businesses need to continuously collect, organize and analyze data; that’s why successful businesses are integrating diverse sets of apps, AI and analytics development talent to collaborate on a data and AI platform that can serve as the information architecture.  In this blog, I discuss use cases — including federated learning, automation and multicloud workload management — that can benefit from ModelOps.

AI and data science use cases from edge to hybrid clouds

Bringing AI models from experimentation to production involves complex, iterative processes. A significant driver of successful AI investment is access to training data that complies with privacy, governance and locality constraints — especially data moving between different regions, clouds and regulatory environments. Federated learning can boost model training with data collected from complex environments.

Automating and augmenting the AI model lifecycle — including data preparation, algorithm selection, and model validation and monitoring — can help businesses generate much better yields. Many enterprises that have built DevOps to deploy software are now stepping forward to build ModelOps lifecycles that complement DevOps. Intelligent automation can help support an agile practice by synchronizing ModelOps and DevOps.

DevOps also benefits from the proliferation of cloud usage in distributed environments, including a variety of cloud services and infrastructures that support private clouds and other diverse tools. Many organizations across various industries such as banking and finance, healthcare, and retail and manufacturing use multiple clouds to optimize AI investments and thus decide which workload is best suited to each cloud environment. The flexibility to align a spectrum of mission-critical AI workloads to specific requirements for key performance indicators (KPI), data location, scalability, resiliency and compliance is crucial.

Hyper-personalized banking experience and fraud prevention powered by ModelOps

As business disruptions emerge and as personal banking needs change, the ability for financial institutions to provide a superior customer experience for loan and mortgage inquiries is essential. Fierce competition demands targeted, often real-time offers. To tackle fraud or security incidents, banks need to tune models precisely to apps while maintaining privacy. Integrating ModelOps and DevOps can help you:

  • Tailor responses to shifting customer priorities and challenges reflected in raw data.
  • Provide offers and alerts to customers while managing staffing through disruptions and recovery.
  • Reduce processing wait time while ensuring risks and internal controls are managed.
  • Prevent fraud and support compliance with rules and regulations where cross-border data transfers are prohibited.

Patient-centered care based on predicted responses and drug interactions

On-the ground intelligence and proactive management of healthcare needs and workflows is vital to improving quality of care and operational efficiency. To optimize decision making for fair, safe and efficient operations, hospitals and healthcare workers must be kept abreast of staffing needs and supply availability. With critical data stored in different clouds and data centers, using AI to analyze data across these environments can dramatically improve the outcomes while addressing privacy and operational concerns.  By taking a multicloud approach, businesses can:

  • Share historical and updated records from multiple regions to improve staff allocation for care surges.
  • Deliver quality care by integrating patient history and real-time data monitoring.
  • Manage healthcare staff shortages, supply delivery and limited logistics.
  • Reduce misuse of critical resources and detect fraudulent transactions.

Supply and demand matching for AI-powered retail and manufacturing

In an evolving business landscape where demand is volatile and supplies often disrupted, retailers need to be able to reimagine their digital and physical presence. Global supply chains pose tremendous challenges to logistics, with data locked in myriad locations across geographies. Using prediction and optimization methods in staffing, pricing, product mixing and routing, AI can help businesses dynamically anticipate demand, manage inventory and optimize delivery. They can use ModelOps to address new consumption patterns and improve liquidity by:

  • Predicting inventory shortfalls and reallocating resources
  • Optimizing category management and pricing using accurate market data
  • Managing store closings and using demand insight to discover new business models
  • Avoiding fines from both physical and digital security violations

Build ModelOps for multicloud data and AI advantage

With advances in data science and AI, businesses can build models faster, scale experimentation and boost AI trust and transparency while also expanding AI talent pools. As more enterprises mandate AI-driven growth and initiate AI and app projects, now is an opportune time to revisit or start building a ModelOps practice and turn the lessons learned from DevOps into successful AI-powered app initiatives.

IBM Cloud Pak® for Data can help optimize cloud and AI investments on an open, extensible platform that runs on any cloud. Using this platform to build ModelOps can drive competitive advantage by enabling companies to:

  • Predict and optimize business outcomes using natural language interfaces to build predictive schedules, allocations and plans.
  • Balance a mix of capital expenditures (CAPEX) and operational expenditures (OPEX) to position for recovery and growth.
  • Flexibly deploy on the environment of choice, including IBM Cloud Pak for Data as a Service and IBM Cloud Pak for Data System.
  • Automate the AI lifecycle end-to-end.
  • Empower and reskill developers and analytics experts to be AI-ready.
  • Speed time to value with industry accelerators using sample data, notebooks and APIs.

Here’s how to get started:

Was this article helpful?

More from Cloud

How a US bank modernized its mainframe applications with IBM Consulting and Microsoft Azure

9 min read - As organizations strive to stay ahead of the curve in today's fast-paced digital landscape, mainframe application modernization has emerged as a critical component of any digital transformation strategy. In this blog, we'll discuss the example of a US bank which embarked on a journey to modernize its mainframe applications. This strategic project has helped it to transform into a more modern, flexible and agile business. In looking at the ways in which it approached the problem, you’ll gain insights into…

The power of the mainframe and cloud-native applications 

4 min read - Mainframe modernization refers to the process of transforming legacy mainframe systems, applications and infrastructure to align with modern technology and business standards. This process unlocks the power of mainframe systems, enabling organizations to use their existing investments in mainframe technology and capitalize on the benefits of modernization. By modernizing mainframe systems, organizations can improve agility, increase efficiency, reduce costs, and enhance customer experience.  Mainframe modernization empowers organizations to harness the latest technologies and tools, such as cloud computing, artificial intelligence,…

Modernize your mainframe applications with Azure

4 min read - Mainframes continue to play a vital role in many businesses' core operations. According to new research from IBM's Institute for Business Value, a significant 7 out of 10 IT executives believe that mainframe-based applications are crucial to their business and technology strategies. However, the rapid pace of digital transformation is forcing companies to modernize across their IT landscape, and as the pace of innovation continuously accelerates, organizations must react and adapt to these changes or risk being left behind. Mainframe…

IBM Newsletters

Get our newsletters and topic updates that deliver the latest thought leadership and insights on emerging trends.
Subscribe now More newsletters