Artificial Intelligence software systems demand innovative development and deployment approaches. The techniques that many companies and software engineers have honed over the years no longer 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 newsletter membership.
Where we have come from
Thousands of large and small companies have built traditional software systems following a standardized set of practices that have evolved from their collective experiences over many years. Software developers often quibble about the differences between Waterfall and Agile, or Scrum and Kanban. But these different techniques and approaches represent variants of the same general approach. It takes a certain 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.
So, do you measure turnaround time in hours and points with an Agile development cycle? Or, do you measure time in months with detailed FDD specs? The difference is in the scope of the steps and in the perceived chances of success with the difference in scope. However, the general approach remains the same.
For years, the primary push in this cycle has been to shorten feedback loops and get the features to the field as fast as possible to get feedback fast. Early feedback, bringing an idea from the whiteboard to the field, enhances the likelihood of success in the form of customer satisfaction. Many techniques have evolved to help development teams move faster and with increased confidence in the features they deploy, such as unit and integration tests, continuous integration, storyboards and more.
Where we are going
Artificial intelligence and machine learning systems introduce new and different challenges into the software development and deployment cycle. These challenges demand innovative solutions. First and foremost, artificial intelligence systems crave data and 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.”
You can test many systems by following this adage: enumerate a small number of conditions, then test those conditions and the corresponding edge cases. However, AI systems throw that adage right out the window. These systems don’t work without mountains of data to evaluate. This need for data introduces three specific challenges:
- 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.
- 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 take hours. We need to modify the AI system, deploy it, and then run through the learning cycles.
- Result Evaluation: AI systems generate tons of predications given the massive amount of data they receive. Those outcomes depend on machine learning, which is based on the data it consumes. In other words, there are too many outcomes to enable testing all of them individually. Also, the outcomes are not known apriori in order to write a unit test that could evaluate the model output.
rasa.io is paving a new path for associations and organizations to use Artificial Intelligence
The techniques software engineers have used in the past cant scale enough to support the development of machine learning systems. But at rasa.io, we have adapted our processes and tools to enable us to develop and deploy enhancements to our AI engine more rapidly. We built our serverless platform using Amazon’s Lambda, EMR and RDS services. This 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 of what your readers are interested in and, thus; how you can communicate the best on a daily basis with your members.
We recognize how 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 help you provide more relevant content to your members by understanding what motivates them. Learn more about how rasa.io can engage your members today.
