As part of our Machine Learning Training service, we provide training, adjustment and data testing for algorithms that are used by our clients in Machine Learning and Artificial Intelligence initiatives. We work under the Human in the Loop (HITL) paradigm, since a large part of the implementation of both solutions requires a human as part of their processes.
The incorporation of Artificial Intelligence (AI) and Machine Learning (ML) in more and more industries has never stopped growing. On the contrary, it increasingly covers more areas, demonstrating its effectiveness in different scopes. Many industries take advantage of advanced analytics to improve the quality of their products. Meanwhile, applications have been thriving in e-commerce, health, finance, and education, among others.
Machine learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed. This learning is based on the fact that systems have the ability to learn from data, recognize patterns, and make decisions with marginal human intervention.
According to a Forrester report, this year all businesses – not just the 15% of those that already have a digital savvy – will duplicate technology-driven experiences, operations, products, and ecosystems. In addition, IDC estimates that within 3 years, AI will be an integral part of every area of companies. Forrester cautions that to better integrate automation and AI with the workforce, companies must create a good employee experience, strengthening the Human in the loop method.
The Human in the Loop paradigm involves incorporating human feedback during the training phase (modeling and simulation) of machine learning algorithms. With this approach, as long as people continually test, adjust and feed data live, and in a constructive and virtual fashion, models can be trained more precisely.
At Arbusta, we work on algorithm training, that is, on the Machine Learning Training phase, which, for example, is present in the development of virtual assistants or chatbots. These can be command-based (they get their answers from a database and make their selection based on the question they receive) or AI-based (they learn from the questions and answers they receive in order to also answer ambiguous questions).
Chatbots also use NLP (or natural language processing) techniques, which allow the systems to analyze, interpret and give meaning to human language and enable annotation processes in chatbots. And in turn, these techniques go hand in hand with those of text recognition, which allow information from documents with image formats to be obtained and analyzed.
WHAT CAN BE DONE WITH MACHINE LEARNING TRAINING?
In the context of machine learning, a system or model is created that answers one or more questions. Once created, it must undergo a training process which must result in an accurate model that answers the questions correctly. The model, or “machine” needs to be “trained”. In order to complete this training, it is furnished with a set of correct answers. Those answers, which are collected as training data, will help the model to connect the patterns found in the data with the correct answer. Based on the training it receives, the model will be enabled to predict future responses.
- Collect the data that will be used to feed the model. The quality of the data is important, although quantity also counts (for which web scraping techniques are frequently used).
- Prepare the data by mixing it up and making sure that there are no correlations that skew the answers one way or the other. The human role, once again key in this process.
- Evaluate the performance: if high success rates are not achieved in the predictions, the parameters (usually referred to as “hyperparameters”) will have to be reviewed, adjusted or reconfigured.
- Final stage: once the training and evaluation phases are completed, the model will be ready to answer questions – that is, to make predictions or inferences in real contexts.
As part of Arbusta’s machine learning training services, various specializations are offered, ranging from general proposals for e-commerce, for web or for applications, to other specializations, segmented by industry, such as banking or health services, for example.
We also work with our clients training computer vision models in a wide range of objectives, from applying AI for fraud detection, to training models in order to solve problems related to the global pandemic, such as automatically detecting the use of face masks.
If you are interested in talking to us about how Arbusta teams can contribute to your Machine Learning Training projects, write to us at [email protected] so we can start a conversation.