Artificial Intelligence software systems demand innovative development and deployment approaches.  The techniques that have been honed over the years by companies and software engineers do not scale effectively in the presence of AI systems. We understand these challenges, so we have built our AI solutions using methods that give us the ability to improve our systems quickly and deliver better content for your association’s membership.

Where we have come from

Traditional software systems have been built following a standardized set of practices that have evolved from collective experiences, gained by thousands of large and small companies, over many years. Software developers will quibble about the differences between Waterfall and Agile, Scrum and Kanban, but these different techniques and approaches represent variants on the same general approach: a degree of planning, followed by development to meet some specs, then testing to validate the adherence to the specs, and finally deployment and monitoring of A, B, C and D.

Do you measure turnaround time in hours and points with an Agile development cycle? Do you measure time in months with detailed FDD specs? The difference is in the scope of the steps (and in perceived chances of success with the difference in scope!), but the general approach remains the same.

For years, the primary push in this cycle has been to shorten feedback loops: get features to field as fast as possible in order to get feedback fast. Early feedback – bringing an idea from whiteboard to field – enhances the likelihood of success in the form of customer satisfaction. Unit and Integration Tests, Continuous Integration, Story Boards – many techniques have evolved to help development teams move faster with increased confidence in the features they deploy.

Where we are going

Artificial Intelligence and Machine Learning systems introduce new and different challenges in the software development and deployment cycle, challenges that demand innovative solutions.

First and foremost: Artificial Intelligence systems crave data; they need data from which to learn. One programmer’s adage says, “There are only 3 numbers: 0, 1 and 2. Everything else is just a generalization of 2.”  Many systems could be tested following that adage: enumerate a small number of conditions, then test those conditions and the corresponding edge cases. AI systems throw that adage out the window. These systems do not work without mountains of data to evaluate. The need for data introduces 3 specific challenges:

  1. Data Acquisition: We must first build tools to gather enough data to be able to evaluate an AI system. Until we have that data, we are limited in our ability to build systems to consume it.
  2. Development Time: Consuming data and evaluating it via AI systems can be computationally expensive. Development cycles that used to measure in seconds from deployment through testing may now be measured in hours: modify the AI system, deploy it, then run through the learning cycles.
  3. Result Evaluation: AI systems generate tons of predications given the massive amount of data pushed in. Those outcomes are based on the machine learning, which is itself based on the data consumed. In other words, there are too many outcomes to enable testing all of them individually. And the outcomes are not known apriori in a way that a Unit Test could be written to evaluate the model output.

rasa.io is paving a new path for associations to use Artificial Intelligence

The techniques software engineers have used in the past cannot scale to support the development of machine learning systems. At rasa.io, we have adapted our processes and tools to enable us to develop and deploy enhancements to our AI engine more rapidly. Our serverless platform, built using Amazon’s Lambda, EMR and RDS services, allows us to horizontally scale our platform, reducing the time required for our AI solutions to build their predictions.

Reducing the turnaround time for processing the vast amounts of available data gives us the flexibility to develop improvements to our AI engine faster. What this ultimately results in, is a sophisticated machine that informs you on what your association is interested in and thus how you can best communicate, on a daily basis, with your members.

We recognize that the growth of our AI is critical to the delivery of the best content for the members of your association.  We are committed to staying ahead of industry trends and technologies to ensure that our AI engine remains at the forefront of personalized recommendations. This allows us to get more relevant content delivered to your members and understand what motivates them. Learn more about how rasa.io can engage your members today.