Progress announced the latest release of Progress Flowmon.
Engineering teams balance many responsibilities in the development, monitoring and production of enterprise software. With modern insights and access to massive amounts of data, these teams have the opportunity to provide top-notch service to customers, higher-quality products and faster release cycles.
As DevOps transformations move enterprise organizations to the cloud, Cloud AI Developer Services help elevate and advance the software development lifecycle. By definition (according to Gartner), Cloud AI developer Services (CAIDS) are "cloud-hosted services/models that allow development teams to leverage AI models via APIs, without requiring deep data science expertise.” Cloud providers such as AWS, Azure, GCP, IBM and Oracle now offer these services.
When AI and cloud are combined, teams can store and analyze data in ways that empower more intelligent business and IT decisions. With Cloud AI services, engineering teams benefit from added capabilities such as natural language understanding, image recognition and automated machine learning.
Cloud AI developers often work with data engineers, machine learning engineers, and data scientists. They also maintain those systems to ensure everything is running smoothly. Google CAIDS offerings, for example, are made by developers for developers, and they approach development with empathy for the developer experience. Cloud-based AI services are not about replacing individual developers or engineering teams' roles but instead, helping them achieve their goals faster and more effectively through powerful technology.
Some of the ways that CAIDS provide support to engineering teams include:
Solving various business challenges using AI software
AI can help engineering teams leverage machine learning, to better understand and predict customer behavior and preferences to offer a more personalized experience. Not only does this help the organization solve customer challenges, but it creates a competitive advantage.
Designing, developing, implementing, and monitoring AI systems
AI can automate tasks and manage manual workflows to improve the engineering team’s productivity. When cloud and AI are combined, there is potential for access to massive amounts of data, providing more opportunities to be strategic and insight-driven. Additionally, as many cloud providers now offer CAIDS via APIs or applications, engineering teams can easily incorporate an AI system. Some of these services are offered through low-code or no-code options.
Explain to project managers and stakeholders the potential and limitations of AI systems
As teams leverage AI for data analysis, they are able to advise on how to make better business decisions. They can also notify other company stakeholders about limitations, such as lag time in data transmission, that can impact the analysis speed of machine learning algorithms.
Develop data ingest and data transformation architecture
CAIDS can also improve data ingestion — the process of transporting data from one location to another so it can be processed and analyzed. With access to CAIDS APIs, engineering teams can also leverage a data transformation architecture that will convert data, facilitating better insights without needing extensive data science knowledge.
New AI technologies to implement within the business
AI services will continue to emerge that will transform the way that companies interact with the customer.
Training teams when it comes to the implementation of AI systems
CAIDS makes it easy for engineering teams to train on how to best leverage AI services and APIs.
Where cloud computing and AI-driven applications meet, engineering teams leverage massive amounts of data for continuous improvement and delivery. It also offers the opportunity to delight customers by predicting their behavior without needing to fill in data science knowledge gaps. On June 30, 2022, DevOps Institute is hosting SKILup Day: Cloud and AI. Continue to learn about CAIDS and how these services and models can help you develop intelligent applications without requiring deep data science experience.