Providing API and Machine Learning Solutions
We think that AI can meaningfully improve people's lives and that the biggest impact will come when everyone can access it. While the technology will require a tremendous amount of management in areas like privacy and security, the potential of the technology to enhance our lives is significant and we love to create these on demand ai services.
The story behind iduna.ai
Why do we choose the name Iduna.ai?
In Norse mythology Idun or Iduna is the goddess of renewal and immortality and she is the guardian of the golden apples who gave the gods eternal youth and thus immortality.
So we thought as a technology company, we would like to support other companies in their web presence by offering fresh cloud api services. So Iduna's tree is the apple tree and that's how the idea for the logo of iduna.ai came about.
Iduna.ai logo and technologies
How do we come up with that logo?
Our logo features an abstract apple tree.
In the software development discipline, there is the Git tree, which also has multiple Git branches, like an apple tree.
So we can imagine that each branch represents a service, which will be developed by us in the near future. A round dot on top of each branch represents an apple or a commit in git. Our logo can also be understood as a Git tree from the world of developers.
Experts in frontend and backend development
Our team consists of different experts of frontend, backend development, UX / UI design and AI.
The team is young, dynamic and comes from all over the world and works remotely via git status and git commit !
This flexibility allows us to bring in resources from around the world to deliver products faster.
Creation and implementation of technical on-demand development projects.
Providing innovative tech products and cloud services
Iduna.ai's mission and goal is the constant further development and improvement of digitization with a focus on modern cloud and AI services. Our vision is to create cloud and AI services to help you finish your tasks faster, so you have more time to produce better products.
We develop scalable, secure and user-centric digital products and we also want to improve b2b and b2c existing processes by simplifying and automating these processes, so you can better focus on things that really matter.
Feedback is valuable to us because we constantly want to improve ourselves and our services. If you are successful then we are also successful and that is what we believe. In the near future, AI services should be available to everyone.
DevSecOps is made up of the words development, security and operations. Traditional software development processes typically take months or even years to develop and release new versions of applications. In this case, companies usually only have enough time in the final phase of development to subject the created code to a separate security and quality control in order to identify problems and weak points. In recent years, software development has changed fundamentally due to the spread of agile software development methods.
Automation and modern cloud-native technologies such as containers and microservices support developers and allow the development steps to be divided into many independently running processes. In addition, DevOps strategies ensure that development cycles are accelerated, reducing the time between releases and new application versions are available within weeks or even days.
Because the development phases are being accelerated, previous security practices are becoming obsolete: In many companies, the available time before the next release is usually not sufficient to effectively check the code for errors. This unnecessarily slows down development or neglects essential security measures and that's why the risk of bugs and opportunities for cyber attacks are increasing.
DevSecOps represents the evolution of the DevOps idea and complements the collaborative development organization with the topic of security. Security measures are integrated directly into the development process and everyone involved is jointly responsible for ensuring security standards. By taking the security aspect into account in the development process, agile processes are not restricted and there is always the possibility of reacting quickly to security risks.
We like to work with an end-to-end open source machine learning platform such as TensorFlow. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
We also do research on an open source machine learning framework like PyTorch that accelerates the path from research prototyping to production deployment. PyTorch is based on Python that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration and Deep neural networks built on a tape-based autograd system.
The step of gathering relevant and reliable data is very important as the quality and volume of the data directly impacts the outcome of the machine learning model. After gathering data, we have to prepare it for use in our machine learning training in the following step.
Data Preparation ensures that the dataset is free of erroneous or corrupt data points. It also involves standardizing the data into a single format. The dataset is also split into two parts to be used for Training the Data Model and evaluating the performance of the Trained Model, respectively.
Training a Model
After preparing the data and choosing a specific model, we move on to what is often considered the main part of machine learning - the model training. The training data set is used to predict the output value and it is also used to gradually improve the model's prediction accuracy.
Once the model training is complete, it's time to evaluate its performance. The scoring process uses the data set provided in the data preparation process. This data was never used to train the model. So, testing the data model against a new data set gives an idea of how the model will perform in real applications.
Just because the model is trained and evaluated doesn't mean it's perfect and ready to go. The model is further improved by tuning the parameters. Prediction is the final step in machine learning. In this step, the data model is provided and the machine uses its learning to answer questions.
App Api Development
We focus on reliable, safe and user-oriented product development. Through the experience of various experts and through our many years of experience with technology and design, we know how to make applications visually appealing, user-friendly, secure and compliant. We use the latest UX & UI technologies and trends to build responsive and scalable applications that transform customer experiences across diverse channels.
Machine learning applications are all around us - in social media, shopping carts and entertainment media, as well as in many homes and even in our healthcare. Machine learning is a subfield of artificial intelligence (AI) and involves training algorithms to find patterns and correlations in large data sets and to make the best decisions and predictions based on this analysis. ML applications improve with use and become more accurate the more data they have at their disposal. We're focusing on the latest machine learning services and building them up so we can make them available to everyone in the near future.
Open Source Integration Service
Agility is now a business imperative. Today it requires an application architecture built with modern, highly responsive, cloud-native technologies that work, adapt, and scale to support rapid change. We believe, that the open source software can offer unlimited vertical and horizontal scalability that's needed for reliable software. We can help you to migrate your applications in different cloud providers or even on premise servers.