Traditional enterprise software can be likened to buying a car. You choose a model and make based on your specific needs, then you go buy the car, and it (usually) works.
Building an AI solution is like acquiring a new skill — such as tennis, or a language, or learning to drive. You can buy all the lessons in the world, but you still have to put in all the hard work to learn and master the skill.
I simplify, of course.
But there is no denying that despite the tremendous promise of AI and machine learning in the enterprise, it takes hard work to make AI work. Companies struggle to determine the right problems to solve, to identify viable use cases, to set up an effective data culture, and to iteratively design, productionize, and scale solutions that prove truly transformative.
However, help is at hand. Machine learning continues to disrupt and democratize itself. For one, evolutionary leaps such as automated machine learning promise to take out much of the drudgery from the process of building predictive models, albeit for specific use cases.
Meanwhile, a second factor that’s driving democratization of AI is machine learning’s move to the cloud. The public cloud provides a configurable, self-service environment to jump-start capabilities for AI-optimized hardware and software, data pipelines, and infrastructure across compute, network, and storage. In essence, the cloud provides data scientists with a sandbox to use, flexibly, within the same environments in which their applications and data already reside.
No surprises, then, that cloud-based machine learning is gaining popularity, as it makes building and deploying AI-based capabilities more accessible, feasible, and scalable.
My colleague Charlie Dai and I have looked at this theme in two new Forrester reports titled “Leverage Public Cloud To Accelerate Your Machine Learning Adoption.” Forrester clients can access the reports for North America and for Asia Pacific. In these reports, we have also examined cloud machine-learning capabilities on offer from vendors such as Alibaba, Amazon Web Services, Baidu Cloud, Google Cloud, Huawei, IBM, Microsoft, Oracle, Salesforce, SAP, and Tencent Cloud.
Feel free to reach out to Charlie or me for an inquiry if you’d like to know more. I invite your feedback, experiences, questions, and comments.