The implementation of Machine learning is often much more difficult than it should be for more business because the process of building and modeling, as well as their subsequent distribution into production, is usually too complicated and too slow.
Firstly, the IT dept need to collect and prepare training data to find out which elements of the data set are important. So, they need to select which algorithm and framework they want to use.
Once the approach is decided, it is necessary to teach the model how to make predictions through a training that requires a large amount of calculation. Next, the model needs to be tuned to provide the best possible forecasts, which is often a heavy manual task.
Once you've developed a fully-educated model, you need to integrate it into the application and deploy that application to a sizing infrastructure. All these operations require great specialized skills, access to significant amounts of computing and storage capabilities, and a lot of time to experiment and optimize each component of the process. Ultimately, it is not surprising that the whole process is beyond the reach of many developers or IT engineers.
Benefit implementing our solutions
- Switch quickly to production with machine learning
- We can use any framework or algorithm
- Free your infrastructure from the burden of training and distribution
- Easy integration with any business existing workflow
- Easy access to trained models
Using our models and services, you and your clients can optimize, for example, return on advertising costs. Here in Cathedral we can easily educate and deploy machine learning models that are more effective in targeting online ads, for better customer engagement and conversion. Recommendation systems, click-through rate forecasts, customer segmentation, and overall value enhancement models can all be learned in the serverless and distributed environment of our datacenter.
|Forecast of credit defaults
Our alghoritms makes it easier to predict the probability of credit default, a common problem of machine learning. We can integrates tightly with existing analytical frameworks, allowing your model to publish large, diversified data sets any storage system and then quickly transform them, auto-training machine learning models and run them immediately hosting for online forecasts.
|Industrial IoT and machine learning
We can enable real-time forecasts to prevent machinery breakdowns or maintenance planning, to achieve higher levels of efficiency. It is possible to generate a digital copy (or replica) of physical assets, processes or systems in the form of models, to forecast maintenance or optimize the output of complex machinery or industrial processes. The model can be updated continuously to allow it to learn almost any change in real time.
|Supply chain and demand forecasting
We offers the infrastructure and algorithms needed to develop sales forecasts for any product in the e-commerce environment. Only with time series and product category data, our models can detect seasonal variations, trends and similar products to provide accurate forecasts, even for new products.
|Predictions on the adverts clicks
Here in Cathedral we provides CPU implementations on both single machines and distributed XGBoost algorithms, very useful in different use cases of classification, regression and positioning, for example in the creation of predictions on the percentage of clicks of the advertisements. Systems of this type are vital to most online ad systems, because it is extremely important to obtain the most accurate click-through rate forecasts so as to provide end users with an optimal experience. Thanks to the XGBoost algorithm, it is possible to execute a real-time forecast program that returns weighed results. Then you can determine whether or not you want to forward ads for a particular advertiser and improve forecasts of clickthrough rates in the ad view.
|Forecasts of the quality of contents
We use a set of tools for preliminary processing and identification of structures within a text, to use the information obtained to provide predictions about the quality of a content. Our alghoritms can generate word embedding to identify semantically and syntactically similar terms in large volumes of text and group similar words together to avoid poor concentration. Thus, Cathedral's advanced topic models allow you to group similar documents into independent clusters. Finally, it is possible to create independent cluster classification models on the data of grouped terms with smaller dimensions, to determine if the moderation of documents is necessary.