Let’s be honest: How many training datasets do you usually generate for your AI projects? Guess that one is not enough. It is quite common that every proof-of-concept launched by a company must start from scratch when it comes to train. When talking about bot training, this process is far too complex to be repeated once and again.
Just imagine: your enterprise wants to catch up with new technologies by creating an innovative Proof of Concept (POC) for an AI project through Dialogflow. First of all, you start generating all data required for a successful training. After that, you see that the results are not as good as expected. Ok. Let’s try this time with Amazon Lex. Once again, all training data must be generated from scratch. This means that those datasets previously generated for Dialogflow now remain useless and you were just wasting your time. No one can’t stand going back to square one that often: it’s about time to recycle training data. Let’s welcome platform-independent training data!
But what exactly is Platform Independence? It is said to be a technology that makes it possible to implement a corpus of knowledge on more than just one system without undergoing, practically, any change. The output of training, in turn, goes through a set of procedures to enhance, rate, support and train those datasets. Therefore, a wise choice would be using these platform-independent datasets to train "things that learn", as Gartner says.
Top 3 Benefits of Platform Independence for your Business
The first thing is: you are not alone. There are vendors providing companies with services such as natural language processing (NLP). When talking about having a platform-independent training dataset at disposal, these are some of the benefits you can expect:
- One training dataset usable for different projects: Make use of the same training dataset for AI applications in diverging or equal domains.
- Avoid lock vendors: More often than not, all training data and corpus knowledge for Machine Learning are entrusted to external vendors and cannot be reused by any other company. That’s why choosing a vendor that offers a platform-independent generation of training data is a key player to avoid a commercial monopoly from those service suppliers.
- Performance comparison: Using the same dataset for every training carried out with ML engines facilitates objectively measuring the performance of every single engine.
How to achieve the Platform Independence? Middleware solutions
Middleware approach is the solution to reach the platform independence. These kinds of technologies will help enterprises have it easy since…
- It will make training detach from the runtime by keeping and exporting it into a new intelligent technology.
- It will harmonize an ensemble of different solutions from multiple providers.
- It will give free rein to an exchange of training data to and from partners and third-party businesses.
Summing up, AI middleware software become, as a matter of fact, a gateway to the future facilitating a harmony between diverse AI projects within a company, a free-flowing data exchange with partners, a profitable use of training datasets provided by third-parties and a freedom of choice when seeking for individual solutions.
Here it is where Bitext technology comes into play. Bitext tools automatically generate those sets of data required to train AI in any platform as easy as pie. How does it work? It generates hundreds of variants from a single query seed allowing any bot to reach a 90% accuracy at the blink of an eye. These variants are generated in all formats fitting any of the main bot building platforms (Rasa, Dialogflow, Luis, Lex…). Consequently, enterprises have total control over their own training data and can use it once and again for their POCs until they find the right model to fit their needs. So, breathe easy and check here for a closer look at this solution.