Artificial intelligence is steadily infusing itself into every component of cloud-native computing environments.

Developers are incorporating AI — in the form of deep learning, machine learning and kindred technologies — into cloud-native applications and business processes through tools that enable them to compose these features as data-driven microservices.

Innovative applications such cognitive chatbots, face recognition, image-based search and automated decision management depend on enterprise deployment of AI technologies within cloud-native architectures. Software developers are building containerized microservices and serverless functions that imbue cloud-native applications with AI-driven intelligence. In comparison to monolithic applications, microservices-based AI applications can be created, tested and deployed more quickly and independently.

Going forward, composable cloud-native AI microservices will be as diverse as the intelligent use cases they drive. For enterprises, the journey to AI involves modernizing your data management and analytics practices and platforms within your overall multicloud computing strategy. Wikibon recommends that enterprises incorporate the following steps into their journeys to cloud-based AI:

• Team: Implement dedicated teams of data scientists and other developers to build, train, deploy and manage AI applications as a standardized operational process across all business functions.
• Platform: Deploy an integrated, open and trusted platform for data science, machine learning, data engineering and application building across the multicloud.
• Data: Combine hybrid data from on-premises platforms and public clouds when building, training, deploying and managing machine learning, deep learning and other AI models.
• Scale: Deploy a fast, scalable hybrid data environment to manage both data at rest and data in motion for myriad AI workloads.
• DevOps: Adopt cloud-based AI DevOps tooling that incorporates popular modeling frameworks, automates model management and hyperparameter tuning, accelerates AI workloads across distributed graphics processing units and other compute nodes, and enables developers to access pretrained models from libraries across the multicloud.
• Optimization: Distribute and scale AI inferencing, training, modeling and data preparation workloads across public cloud, private cloud and on-premises systems in the multicloud.
• Management: Adopt robust tools for data integration, security, governance, lifecycle management and DevOps across all AI initiatives, projects, applications and workloads.
• Containerization: Build containerized AI microservices for orchestration across Kubernetes-based multicloud fabrics.

Another key step in your enterprise AI journey is to attend IBM Think 2019, which is taking place Feb. 12-15 in San Francisco. You can register here. Visit this site to learn more about AI, data, analytics and Watson curriculum programs at Think 2019. Think attendees can choose from 250-plus proctored certification and technical sales mastery exams. As a Think 2019 attendee, the first exam will be free and includes a discount on any additional exams. This offer is available on a space-available basis for people registered to attend Think 2019 in person. The exam must be taken at the conference.

Please join us on the #Think2019 CrowdChat, “The Journey to AI,” at noon EST on Thursday, Jan. 17. And don’t forget to tune into theCUBE for live interviews with IBM executives, developers, partners and customers during Think 2019.

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