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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

The post Scale ModelOps to drive AI value from edge to hybrid clouds appeared first on Journey to AI Blog.

In the age of Covid-19, business as usual just isn’t an option. Workforce resiliency, enterprise agility and cost reduction have all become top of mind for business leaders. Digital transformation through AI can play a key role in bolstering a COVID business recovery plan. From reallocating resources to increasing operational efficiency, the case for digital transformation and AI is growing stronger. According to “Covid-19 and the future of business” by IBM Institute of Business, 66 percent of surveyed organizations say they have completed digital transformation initiatives that previously met resistance. If the pandemic has shifted priorities, it has also made AI-powered digital transformation more justifiable and ModelOps more urgent.

But how do you make sure AI is trusted? What’s needed to manage model performance despite invalidated patterns caused by the pandemic? What can you do to fill data science skills gaps and realize value from your AI investments? IBM Watson® Studio on IBM Cloud Pak® for Data automates how your business can build, run and manage AI models at scale. It can reduce costs by simplifying AI lifecycle management and speed innovation with a flexible multicloud architecture and open source tools.

To discuss five ideas for accelerating your data science projects and how the latest version of IBM Watson Studio can support innovative and cost-effective digital transformation, I sat down with Thomas Schaeck, Distinguished Engineer, IBM Data and AI.

Idea 1: Bring together diverse skill sets for cross-team AI and data science collaboration 

Developing AI can take a village. Data science and AI teams have a lot to learn from application development and DevOps teams in operationalizing the process from ideation to results at an accelerated pace. Cross-collaboration is key. “By harnessing the power of talent with cross-discipline backgrounds,” says Schaeck, “organizations can accelerate learning, increase productivity and bring models to production faster on a unified environment.”

To speed AI development, data scientists and developers can use AutoAI, a model development tool within Watson Studio, to automate data preparation, feature engineering and hyperparameter optimization. The latest launch increases the size of data sets and enables the multiple input data sets to generate model pipelines and train candidate models. Schaeck says, “We added SDK support for AutoAI (available as a tech preview) to generate Python Notebooks or Scripts in addition to the current user interface.” This works from Python notebooks or scripts running directly in Projects on IBM Cloud Pak for Data, as well as from notebooks or Python scripts running in desktop IDEs like VS Code and PyCharm and connecting remotely to IBM Cloud Pak for Data APIs. With Anaconda Repository for IBM Cloud Pak for Data, teams can also accelerate open source innovation and policy control with enterprise-grade package management.

Learn more: Explore operating models in our analyst webinar “DevOps for AI.”

Idea 2: Increase cost savings and efficiencies by enabling machine learning prediction and optimization without locational constraints

Many locational factors that determine predictions and optimizations have changed as work arrangements and consumption patterns have shifted during COVID. AI can help reduce costs and meet regulations by dynamically reallocating resources and automating logistical operations across both physical locations and multiple clouds.

Federated learning, available as a tech preview in IBM Cloud Pak for Data, helps increase training accuracy by using data across clouds while meeting data privacy, security and other regulations. Schaeck outlines how you can train an algorithm at the edge without moving data. Use cases include:

• Fraud detection through improved access to training data for cross-border and multiple parties
• Medical diagnostics and drug discovery by sharing patient data, research data and other ecosystem data as training data
• Smart manufacturing to use data in robots and other devices across factories for model training

Discover industry leaders: Download The Forrester Wave: Multimodal Predictive Analytics and Machine Learning, Q3 2020

Idea 3: Expand your deep learning projects on an integrated data and AI platform

Deep learning is now more widely adopted than ever. Inspired by the human brain, deep learning uses artificial neural networks to develop advanced pattern recognition and improve model training and inference. It crunches through large and complex data, including unstructured data from speech, audio, video, or handwritten notes.

A data and AI platform that scales compute, people and apps dynamically can enable faster deep learning implementation. It provides a multitenant, multicloud architecture that helps automate AI lifecycles and speed time to results.

IBM Cloud Pak for Data breaks down data siloes and optimizes deployment without disruptions to AI training. Furthermore, says Schaeck, with recent advancements in deep learning on Watson Machine Learning Accelerator, a capability in Watson Studio, organizations can choose more mature deep learning patterns, saving costs and speeding digital transformation with transparency.

Dig deeper: Check out this infographic, ‘Accelerate deep learning workloads on IBM Cloud Pak for Data’

Idea 4: Predict and optimize your business with broader contributors

Prediction and optimization models have been indispensable in forecasting volatile demands for services, goods and other purchases. For example, AI can help calibrate the opening of specific market segments and optimize staffing, logistics and other resources to support them.

A unified data and AI platform can save time and costs by combining prediction and optimization on a single hybrid multicloud architecture. It can also empower non-coders to accelerate model development with visual data science tool SPSS Modeler.

IBM Cloud Pak for Data offers decision optimization and visual data science as part of IBM Watson Studio Premium. The platform can rapidly transform any participants into contributors, diversifying their skills while increasing productivity. With version 3.5, the interface becomes more user-friendly for operational researchers. SPSS Modeler adds extension nodes, new charts and streamlined integration to accelerate data preparation and model development. 

Learn more: Join our live 3-part data science webinar series.

Idea 5: Measure the ROI of model monitoring and explainable AI

“Investments in explainable AI are prerequisites for scaling AI with trust and transparency,” says Schaeck. By describing an AI model, its expected impact and potential biases, explainable AI capabilities can help organizations manage regulatory risks and, crucially during COVID, link model accuracy with monetary value. As one data scientist in financial services noted in a commissioned Forrester study¹, with explainable AI, “our models are now more accurate, which means we can better forecast our required cash reserve requirements. A 1% improvement in accuracy frees up millions of dollars for us to lend or invest.”

IBM Cloud Pak for Data makes model monitoring more interactive with shareable “what if” analysis and improved understandability​ with role-based access and visibility. By unifying data and AI services, the platform helps end users better understand model decisions and detect potential fairness issues due to unseen data correlations.

Measure outcomes: Read New Technology: The Projected Total Economic Impact Of Explainable AI And Model Monitoring In IBM Cloud Pak For Data

As the pandemic impacts the business landscape, companies that innovate with AI-powered digital transformation stand to weather the storm. You can be one of them.

Next Steps

By unifying data and AI services, the platform helps end users better understand model decisions and detect potential model drift issues faster with better visibility. To learn more, please watch the Part 3: Operate trusted AI with model governance of the OnDemand data science and AI webinar series today.

You can be one of them.  Let’s get started by downloading this newsletter featuring two complimentary Gartner research reports and IBM’s point of view on ModelOps.

¹ New Technology: The Projected Total Economic Impact Of Explainable AI And Model Monitoring In IBM Cloud Pak For Data, Commissioned By IBM, August 2020

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