NLP (Natural Language Processing) is making an incredible progress through the application of advanced deep learning techniques. In particular, the advent of the Transformer architecture is paving the way towards the generalized use of NLP in all type of environments.
In this tutorial, we will cover for the first time the end-to-end process of a ML initiative, going from the development of the model to its consumption.
To this end, we will combine three building blocks:
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 based on historical data.
This tutorial will help us illustrate how to combine the different capabilities of Titan in order to deploy and maintain a prediction service.
NOTE: To run this tutorial, a Google Cloud account is needed.
The following figure depicts the structure of the pipeline:
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 API interface as we have seen in previous tutorials.There are many use cases where we just need to run our prediction synchronously or asynchronously (e.g. on a monthly basis) to obtain the desired results.
In this tutorial, we will see how our new feature, Titan Jobs, can help Data Scientists…
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 all the potential integrations with other applications and pipelines.
Continuous Integration and Deployment (CI/CD) is a global denomination for all those (automatic) processes used to build, package and deploy all types…
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.
In this tutorial we will illustrate how, starting from a ML model, we can build a basic MLOps system to manage the deployment and operation of a Breast Cancer Classification model.
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…).
This type of systems are currently pervasive in many tools which we use in a daily basis. Some examples would be:
Recommender systems can be built in different ways which can be basically classified into 3 different approaches:
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 tutorial, we will see how to build and deploy a basic Sentiment Analysis using TextBlob, a well-know library to process textual data.
Sentiment Analysis can be tackled from two different perspectives, a Machine Learning approach (with supervised or unsupervised models), or a Lexicon approach:
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 Science, also to datasets.
In this new tutorial, we will detail our approach to service versioning in order to manage deployed services.
When designing Titan, we took a very clear approach to versioning that can be summarized in two main ideas:
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 edition in our tutorial series, we will see how we can easily define in which environment we want our models to run once they have been deployed.
Since Titan version 0.5, it is possible to easily define the environment we want our service to run on top of. Our approach…
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 of objects of a certain class (humans, animals, vehicles…)
More specifically, detection means classification (what is the object) and localization (where is the object). The common output of detection models is shown below:
For our example, we will use YOLO (You Only Look Once), a well-known, state-of-the-art object detection system…