It should be evident by now that AAI (Applied Artificial Intelligence) has crossed the hype bubble and has it’s presence in several use cases in marketing, sales, operations, IT, HR, fintech, healthcare and beyond.
However, it is important to note that only 15% of enterprises are using AI per Adobe.
Hence, understanding the challenges faced in developing and putting to use a data and statistical driven product can greatly increase the chance of AAI adoption in an enterprise
Data, data and data
There is a well-known saying in machine learning that the one who has the most data wins! However, getting access to the desired data may not be easy. In particular, supervised learning methods demand labelled data sets which are hard to find and involves a lot of effort, not to mention a very mundane and laborious activity. There are some ways in which labelled data can be made available from publicly available data, crowdsourcing, transfer learning from pre-trained models, commercial vendors, open source and artificial data synthesis.
Getting clean data can be another challenge. Observatory analysis will lead to identification of patterns and anomalies in your data. While these are easier to spot, some of them are silent killers like data latency and leakage which, if not identified during implementation, can completely change the outcome of your models in production.
Yet another factor could be data sensitivity. This would demand tighter controls in your product architecture to give a level of confidence to your customers that such data will not be misused.
It’s no longer relevant!
As the data changes over a period of time, the models developed could stay irrelevant if there is no learning process in place. Hence, continuous learning should be an integral part of the product. Continuous learning can happen either through self-learning or by introducing a human in the loop.
Hammer and nail approach
While building a product, “one solution fits all” may not work. It may not be necessary that the answer to every problem is AAI. Heuristics, if applied correctly, can, at times, provide better results than AAI which is hard to beat primarily because heuristics is derived from human intelligence which, if provided by an SME, can outperform some models.
Power of cloud
With increased digitization, there has been a plethora of data which is now available and that demands significant storage and compute resources. This is where your architecture can have the required elasticity.
Additionally, many cloud service providers now provide off-the-shelf machine learning pre-built models built on a very large set of training data. Depending on the use case one can leverage, these ready- to-use models with minimal or no customizations.
Show me the product
Many of your customers would prefer to see the actual product in action. While it may not always make commercial sense to build the product before marketing, it is important to show a glimpse of what your product is capable of which is what we call MVP. A MVP (Minimal Viable Product) does not necessarily need to have all its features fully integrated as long as it is doable. The idea is to showcase the bigger picture which should be greater than the sum of its parts (product features).
To ease model building, there are now a varied set of machine learning platforms available to make the model development and deployment easier. They are scalable, fast and accurate and easy to use, these platforms can be used to accelerate your product development. Also, given the iterative nature of AAI/ML development, it makes even more sense to have an agile development process which has automated continuous integration and continuous deployment at its core.
The agility also demands an agile AAI architecture which is adaptable to changes, is scalable and resilient. The below diagram depicts one such architecture: