Artificial intelligence (AI) and machine learning (ML) typically use of the cloud for AI/ML workloads. However, there is currently a focus on services that use AI in various forms, with a buzz around generative AI (GenAI). This has gained significant attention recently and has the potential to change how businesses and their employees work. Adoption of GenAI-powered tools is not limited to only those who are skilled in technology. The use of the cloud for these tools reduces the barrier to entry and speeds up potential innovation. AI refers to a computer program designed to imitate human intelligence, while ML involves building models using algorithms to learn from patterns in data without being programmed explicitly. Deep learning (DL) models mirror the structure of the human brain and are skilled at identifying complex patterns. GenAI is a subset of DL and generates new content based on learned patterns. As these methods get more capable, the complexity increases, requiring more compute and data, so Cloud offerings are invaluable in this regard.
Businesses worldwide have been using AI for years, and it is essential to consider ethics and responsible AI when moving an AI solution to production. The explainability of AI solutions decreases as they get more complex, which can make it challenging for businesses to understand why a given input results in a particular output. Considering the ethics of AI is equally significant, and it’s essential to determine when it is not appropriate to use AI. Identification of opportunities is the starting point for any business to successfully adopt AI. Look for areas where repetitive tasks are performed, especially those involving decision-making based on data interpretation. Additionally, identifying areas where manual analysis or generation of text is required is crucial.
Once opportunities are identified, clear objectives and success criteria must be defined. Operations can start small and prove the concept. Evaluating the success of a Proof of Concept (PoC) exercise requires a critical mindset and an understanding that not all problems can be solved using AI. After a successful evaluation, operationalizing the capability involves aspects like monitoring and observability. AI and ML are established technologies, and using them with the power of the cloud will define the businesses of the future. GenAI is currently generating the most hype, and the best use cases will soon emerge. Organizations need to think innovatively and experiment to find those use cases. The key is to take away learnings from articles, identify opportunities, prove feasibility, and then operationalize, with significant value to be realized with the necessary care and attention.
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