Innovation and adaptability are more vital than ever. Our ability to discover new insights, examine patterns and build hypotheses continuously helps us adjust and improve our response to rapidly changing conditions. The Innovator’s DNA by Jeff Dyer, describes five discovery skills which are essential in helping innovators generate new growth opportunities: associating, questioning, observing, networking and experimenting. Indeed, successful innovators put a premium on a continuous flow of discovery and delivery.
When translating predictions and insights into schedules, plans and prescribed actions, we are often faced with a myriad of scenarios, objectives and corresponding variables. Countless touch points need to be transformed into an experience that is not only profitable and productive, but also safe, compliant and delightful. Imagine the decisions facing a retailer with extensive physical and digital footprints: They are constantly optimizing factors such as capital and operational expenses, supply chain, staffing, perishable goods and other assets. What is the projected customer demand today or tomorrow? How do we meet that demand with supplies, logistics and staffing? To simplify decisions, they are tapping into insights and prediction to prescribe actions in a timely, secure fashion. This requires the business to:
  • Unite the effort of collecting, organizing and analyzing data across any cloud
  • Expand the talent pool and reskill staff to contribute to data, AI and app pipelines
  • Activate the ecosystems of tools, technologies and collaborators to ease discovery and deployment
  • Promote trust and transparency by establishing AI model governance as part of their design principles
  • Build, run and manage models integrated with DevOps to continually make apps smarter with models.

A fully integrated multicloud data and AI platform can help you automate AI lifecycles, accelerating discovery and delivery while integrating the hand-offs between technological and organization silos. A platform also makes consumption of AI models easier and enables beginners to collaborate with and learn from experts so they can rapidly become key contributors. This is why a data and AI platform is the ideal environment for synchronizing app and AI cadences to build Model Operations (ModelOps) using the culture of Development Operations (DevOps). It can also serve as a foundation that enables organizations to monitor AI models and prescribe actions based on rapidly changing business conditions. And it helps an organization navigate through market fluctuation and modernize the environment to operate for the future.

Accelerating discovery and deployment with AI models on the data and AI platform

In “Unlocking business acceleration in a hybrid cloud world”, McKinsey notes that “Cloud accelerated leaders are moving to embed intelligence into the operations, ranging from rule-based automation to augmented decision making truly based on AI and machine learning.” Cloud and AI pioneers can build apps using assets across clouds. They can offer experiences that adapt to sudden changes or new mandates using a pre-integrated set of containerized services in an open, extensible cloud native platform. A unified environment makes it easier for people to flex the discovery muscles noted in The Innovator’s DNA as critical: associate multiple activities, ask new questions, observe and assess patterns, network and share new ideas and create experiments. This is where rubber meets the road. Aiming to align IT and technology with business priories, data scientists, data engineers, and ML engineers together with DevOps engineers and application developers can collaborate and speed deployment with better productivity.

IBM Cloud Pak for Data is a data and AI platform expressly built to meet these requirements. It automates AI lifecycle management, provides a unified experience across the organization, extends AI ecosystems, deploys models with a click and enables models to be automatically monitored for bias, fairness, drift, compliance, and more. It can help you adapt your business through accelerated discovery and deployment by building and scaling AI with trust and transparency.

Decision optimization empowers demand and supply matching while activating logistics

Prescriptive analytics and predictive analytics deployed as part of containerized microservices on a platform also unleashes the power of data and AI. With Decision Optimization, available in Watson Studio for Cloud Pak for Data, a business can define objectives, constraints and outcomes for challenges such as scheduling, resource allocation and supply and demand planning by using a natural language interface to build decision optimization models. Businesses can take the decision optimization and prediction models that are in a continuous feedback loop of improvement, and use them to navigate shifting demands, supply shortages, and logistics constraints.

Unified experience and governed frameworks help harness the power of agile AI teams

Cloud Pak for Data provides a unified experience that guides the creation, training and deployment of models using common patterns and algorithms. It also works with notebooks and workflows for more advanced use cases, supporting agile AI development. By using AutoAI, data scientists, analysts and application developers can collaborate on discovering insights, building hypotheses, and exploring results. Users can trust data and models by being better able to explain results, interact with models, visualize relationships and establish new experiments.

Expanded ecosystem empowers the use of scale as an advantage

Cloud Pak for Data has a vibrant, ever-expanding set ecosystem that extends its reach. It supports IBM Power Systems for mission critical AI workloads, optimizing AI training and inferencing. You can also use a catalog of third party and IBM services to extend the architecture. Also expanded are industry accelerators that include customer segmentation and attrition, predictive maintenance, and contact center optimization. This modular architecture helps you get started quickly and use scale as an advantage as you access more data sets, onboard contributors and integrate with external services using the open source tooling and libraries.

Uniting DevOps and ModelOps speeds a path to implementing AI into your apps

Built on Red Hat OpenShift, Cloud Pak for Data helps you scale deployment, provide real-time visibility and process high data volume with security and governance. It helps you keep your AI models and applications running to deliver outcomes that match the original intent and expectation. The platform enables developers to stand up production and integrate with continuous-integration-and-deployment (CICD) pipelines with one click. Data scientists and developers can take advantage of an integrated set of environments across disparate clouds.

Learn more about what’s new with data science and ModelOps

Data science is a critical practice for any organization seeking to discover and innovate new ways of improving financials, designing and improving experiences and streamlining operations. For a deeper look into what’s new in Cloud Pak for Data, sign up for our two-part webinar series. You start gathering more details by visiting the Watson Studio and the IBM ModelOps microsites. And, finally, don’t miss our Multicloud for ModelOps for Apps webinar, where you can learn about how to extend Model Ops from the edge to hybrid clouds and take advantage of federated learning. In addition, get your questions answered at the IBM Data and AI innovation exchange, a weekly live AMA (Ask me Anything) with IBM Research and data science and AI experts. It is a great opportunity for us to share our ideas and hear from you in a technical forum.

You can also download an infographic on “A case for building AI models on a data and AI platform.”

And learn how ModelOps can work with popular open source tools and DevOps by accessing this free eBook.

For more information about ModelOps, read our newsletter featuring complimentary Gartner Research.

Accelerate your journey to AI.

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