Scale ModelOps to drive AI value from edge to hybrid clouds
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 knowhow.
One crucial mandate in operationalizing AI is building the 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. 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
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 helps:
- 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 integrated 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 Cloud Pak for Data as a Service and 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:
- Download this newsletter featuring two complimentary Gartner research reports and IBM’s point of view on ModelOps
- Watch this on-demand 451 Research ModelOps webinar on synchronizing AI and DevOps
- Visit IBM Data Science for Multicloud ModelOps
See why Watson Studio is a Leader in “The Forrester Wave™: Multimodal Predictive Analytics and Machine Learning, Q3 2020” report