The Four Horsemen of an Ai Application

The obstacles and constraints on building a scalable, successful and financial prudent artificial intelligence application or a solution.

Data Science | 04/07/2020 UTC
Blog Hashbrown

As the playbook, for ai and data business and applications, is being written, here is looking at some of the “known unknowns” that can cramp development and deployment. Identifying these in advance can help you save money, save cost, save jobs and in fact save the idea in itself. Often times a lot more time and resources are spent in deployment of these products than anticipated. 

“More time and resources are spent on deployment than anticipated.” 

This is the concluding chapter of the pitfalls, as we move to practical advice or resume doing more sales-oriented writing. This series I personally feel is important as it helps our prospects avoid potential pitfalls – before they commit towards building an Ai solution for their business.  

Is it as bad as it sounds? Of course not, but Ai and Data Analytics is not for everyone. One must be willing to bear the costs and rate of the failure is high if one does not price the product properly. Sometimes later I will write about how one of the big four auditing firm derailed a remarkable product because they were not equipped to understand the nuances of data analytics, machine learning, deep learning, vision and overall ai concepts.  

Hashbrown Systems has been in the business of artificial intelligence before it become the cool thing it is. We have a head start in three important industries – Institutional Sales, Out-of-home advertising and financial markets automation. A lot of our know-how can be successfully leveraged into other specialized fields, and as a business we shall endeavor to do so.  

This is also the reason that we have a ready reckoner in place for our new clients. This way they can assess freely, consult outside and have a large gamut of questions before appropriating resources.  

The Non-Traditional Nature of Ai 

In part I we discussed the non-traditional nature of development and we talk about the start difference in development costs when compared to building an on-premise solution or the non-zeroing of cost associated with software as a service.  

Read it here in full - https://hashbrown.com/blog/data-science/the-business-of-data-analytics-and-artificial-intelligence-part-i 

The Cost of Cloud Infrastructure 

The cloud extracts a huge cost and there are a few techniques to address speed but still the cost associated remain high.  

Read more here - https://hashbrown.com/blog/data-science/the-business-of-data-analytics-and-artificial-intelligence-part-ii 

The People maketh the system 

It takes a lot of intelligent and experienced manpower to build a system that will not require people to run efficiently. So how do we keep the fire in an Ai engine burning? 

Learn about it here - https://hashbrown.com/blog/data-science/the-business-of-data-analytics-and-artificial-intelligence-part-iii 

The Vagaries of time 

Ai systems can be deceptive in the use of time, a point explained succinctly in the last part of the series, here –  https://hashbrown.com/blog/data-science/the-business-of-data-analytics-and-artificial-intelligence-part-iv 

The key to building a financially successful solution is to own the challenges, and we stand by our assessment of these points. These challenges remain, but if you or your organization are willing to throw down the gauntlet and meet those obstacles face to face, the results are extraordinary.  

This is conclusion to our discourse on constraints and we would move towards the possibilities and the process of building and creating exceptional Ai and data driven software and services.  

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