Artificial Intelligence Service Provider Company| AI Development Services Company in USA India

With our years of expertise in complex problem solving and business transformation, we at skybridge provide Artificial Intelligence services to help business derive value. Our Artificial Intelligence experts will strive themselves in the business knowledge and domain to find the change factor and offer solutions to them.

Companies leverage Artifical Intelligence and custom Machine learning gather analytics insights about customer behaviour, their liking for products and response to their services. For a critical function like this, it is very important to work with right Artificial intelligence service provider that will make a world of difference for an organisation.


Machine Learning

We closely link the computational stats to focus on the predictions. We follow solid mathematical optimizations that results in quality delivery.

Deep Learning

We help to rise intellectual business frameworks that can perform like the human brain with deep learning.

Statistical Modeling

We offer an organised and quantitative approach to make sure the set of data are in form of statistical models with right conclusion and more accuracy.

Data Analytics

Our services build over the years in machine learning platforms has helped us to analyse the raw data sources to develop more effective marketing strategies for customers.

Business Intelligence

Empower the non-technical users to get the complete insights by providing self-serving analytics where we play with search, analysis and visualization.

Model Design and Building

Skybridge has practical experience building AI models for various businesses Our data scientists offer unique insights into machine learning research, and advanced expertise in developing models that match your needs. Our data sourcing and planning apparatuses, preparing engineering and model deployment technology will get you fully operational quickly, and stretch the exhibition of your models to the edge.

Artificial Intelligence Service Provider Company| AI Development Services Company in USA India UK

Model Validation

Skybridge plays an important role in Model validation in executing the below tasks to ensure the developed model fulfilling the business requirements and providing an esteemed solution for the problem statements and achieve the expected business benefits post deploy in production environments.

  • Data quality check
  • Effective usage of Exploratory Data Analysis
  • Feature Engineering
  • Development standards
  • Model architecture
    • Algorithm selection
    • Parameters selection
  • Accuracy check WRT prediction/classification/clustering power, etc.,
    • Scalability
    • Interpretability
    • Impact of using the model for the new (unseen) set of future data
  • Application Performance as a whole
    • Functionality Assessment
    • Non-Functionality Testing.

Post successful execution of above validations of the developed model, we need to monitor the model behaviour with the new data sets from production and provide the sign-off of the model.

Model Monitoring & Feedback

For companies, it is very important that a model is effectively deployed to start the decision-making process. Decision is making will be severely limited if the you cannot get practical insights from the deployed models. Model deployment if done properly will give lots of value to the customers. For deploying a model, coordination is required between Business team, Data scientists, IT team and Software engineers to make sure that model is working as expected in the deployed environment. There should not be any discrepancy between language in which the model is written and the language your deployed system can understand. Rewriting the code can possibly extend the project timeline.

Skybridge’s AI Platform and the expert team can help you reduce the deployment time significantly so that your business users can start using them soon and make practical decisions that can improve the bottom line of the company.

Machine learning models drive some of the critical decisions for an organisation. Models deployed in production makes decisions based on ever changing data. Life of machine learning model does not end after the model is deployed. Model monitoring task is important task after the machine learning model is deployed. It is very important for a model to remain relevant to the current data once deployed in production. Even a well trained model with large datasets can degrade over a period of time if there is a data skew or data distribution could have changed compared to training data set. Certain features of the data may be unavailable due to real-world user behaviour might have changed. This may impact a well-trained model’s ability to accurately predict. Hence monitoring the change in ML model behaviour and most recent data is very important.

Observing the progressions in model's conduct and the qualities of the latest information utilized at surmising is along these lines of most extreme significance. This guarantees that the model remaining parts pertinent to the ideal exhibition as guaranteed during the model preparation.