Considerations when selecting Google Cloud AI/ML solutions

1. Considerations when selecting Google Cloud AI/ML solutions

Google Cloud offers a range of AI and ML solutions and products, but there are several decisions and trade-offs to consider when selecting which to employ. The first consideration is speed. How quickly do you need to get your model to production? AI projects can typically take anywhere 3-36 months to plan and implement, depending on the scope and complexity of the use case. But business decision makers often underestimate the time it will take. Pre-trained API's require no model training, because that time-consuming task has already been carried out. Custom training usually takes the longest time because it builds the ML model from the beginning, unlike autoML and Big query ML. The next consideration is differentiation. How unique is your model, or how unique does it need to be? Google Cloud offers a range of outs of the box solutions for organizations that want to quickly use ML models in their day to day business operations. These include image recognition solutions and chatbots, which are quick to deploy and can be applied in various use cases. Alternatively, Vertex AI, which is Google Cloud's unified platform for building, deploying, and managing AI solutions, can give ML engineers and data scientists full control of the ML workflow. Vertex AI custom training lets you train and serve custom models with code on vertex workbench, which results in highly bespoke ML models. Another consideration is the expertise required when embarking on an AI or ML project. Infusing AI into business processes requires roles such as data engineers, data scientists, and machine learning engineers among others. Organizations should consider their current team and then determine a people strategy, which could include reusing or repurposing existing resources, upskilling and training current staff, or hiring or working with outside consultants or contractors. Google Cloud's AI and ML products vary from those that can be employed by data analysts and business intelligence teams, right up to those more suited to ML engineers and data scientists. The final consideration is the effort required to build an AI solution. This depends on several factors, including the complexity of the problem, the amount of data available, and the experience of the team. Google Cloud can help provide solutions for projects at both ends of the scale. However, any AI undertaking will generally require much time, effort, and expertise to have a worthwhile impact on business operations.

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