Titan Tutorial #12: Building a production-ready ML pipeline to predict hotel cancellations Introduction As we have seen in previous tutorials, Titan offers building blocks (Services and Jobs) to allow Data Science Teams to build their own pipelines and solutions in a simple yet powerful manner. In this tutorial, we will see how to build a complete real ML pipeline to predict hotel cancellations…

Titan Tutorial #11: Building a “batch-mode” churn prediction model Introduction Historically, batch processing has referred to the action of running computational tasks (arbitrary code execution) on demand or scheduled by the user with minimum or no human interaction at all. In our Data Science world, not all the Machine Learning models are meant to be consumed in real-time through an…

Titan Tutorial #10: A basic pipeline for Machine Learning Introduction Ever since its inception, every detail and feature of Titan has been designed and built with interoperability in mind. In order to facilitate the integration of our product in any corporate architecture, Titan is both agnostic with regard to the underlying Cloud (public or on-prem) and also with regard to…

Titan Tutorial #9: Integrating and consuming services in a healthcare use case Introduction Machine learning techniques are increasingly attracting interest from the healthcare sector due to its multiple applications in this field. From oncology screening to drug synthesis and voice assistants, Machine Learning is expected to play an important role in the coming years in the transformation and improvements of health systems.

Titan Tutorial #8: Building and deploying a a collaborative-filtering recommender service from scratch Introduction Recommender systems are information filtering systems oriented to customize and personalize the experience of the users using a service. In order to achieve this, recommender systems make predictions about user preferences based on multiple sources of information (interests, past actions, similar users, context…).

Titan Tutorial #7: Building and deploying a basic Sentiment Analysis model Sentiment Analysis is a subset of NLP (Natural Language Processing) focused in the identification of opinions and feelings from texts. For example, these techniques are commonly used to understand the feelings of the customers about a product or service, or to measure the success of a marketing campaign. In this…

Titan Tutorial #6: Managing service versions for a price prediction model Version Control Systems have a paramount importance in all types of software development, including of course the AI/ML models we work with in a daily basis. Version control applies to any kind of practice that tracks and provides control over changes to source code and, in the case of Data…

Titan Tutorial #5: Defining deployments straight from a Jupyter Notebook One of our most important objectives at Akoios is to make daily life easier for the Data Scientists. To this end, Titan has been designed to enable Data Scientists to perform as many tasks as possible straight from the tools they use every day (e.g. Jupyter Notebook). In this new…

Titan Tutorial #4: Deploying an object detection model based on YOLO In this new tutorial we will see how to develop and deploy a more complex model, more specifically, an object detection model. For those new to these topics, object detection is an umbrella concept that encompasses all those technologies related to computer vision and image processing dealing with the identification…