Accessing Model Ops and managing AI and ML requires IT leaders and professionals to put together four key elements of the business value equation, as outlined by the report author.
Ecosystem: Nowadays, successful technology efforts require connectivity and network power. “The AI-enabled ecosystem needs to be as open as possible,” the report said. “Such an ecosystem doesn’t just evolve naturally. Companies wishing to successfully use the ecosystem can support the ecosystem and implement open standards that can be easily adopted by outside parties. We need to develop a next-generation integrated architecture. “
data: Understand the data that is important to your efforts. “Verify training and production availability. If you don’t know future usage, tag and label your data for future usage. Run future projects over time. Create an enterprise inventory that helps speed up. ”
platform: Flexibility and modularity (the ability to replace parts as circumstances change) are important. Report authors recommend buying rather than building, as many providers have already considered the details of building and deploying AI and ML models. “Determine your cloud strategy. Do you want to do everything in one cloud service provider? Or do you use different CSPs for different initiatives? Or do some workloads on-premises and one? Do you want to take a hybrid approach of running the part in a CSP ?: Some major CSPs typically offer scalability and scalability, such as providing tools and libraries to help build algorithms and helping to deploy the model to production. It provides more than storage space. “
Man: Collaboration is the key to successful delivery of AI and ML, but it is also important that people have ownership of each part of the project. “”Who owns AI software and hardware? Is it an AI team, an IT team, or both? Here you get the boundaries of your organization that need to be clearly defined, clearly understood, and coordinated. Along with data scientists, an equally important group for ModelOps is “data engineers who bring important expertise in the use of analytics and business intelligence. Tools, database software, SQL data languages, and clean, high-quality ethical. Ability to generate data consistently. “
https://www.zdnet.com/article/at-last-a-way-to-build-artificial-intelligence-with-business-results-in-mind-modelops/#ftag=RSSbaffb68 Finally, how to build artificial intelligence with business outcomes in mind: ModelOps
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