By using Machine Learning we can help optimize applications and achieve the most efficient outcome possible. AI allows us to predict and avoid potential problems in an application life-cycle, as well as help inform businesses of otherwise unpredictable use cases.

How We Do It

We bring the cloud services for developers that helps you build and analyze better models quicker.

We help your data science teams and developers to deploy models into production by covering as many use cases as possible which includes:

  • Experimentation
  • Data processing
  • Choice of Algorithms
  • Feature engineering
  • Extendability
Adil Analytics + Artificial Intelligence Analytics = 360° view that provides analytics solutions to new intelligence.

Deep Learning

AI is becoming smart with the help of deep learning. Deep Learning represents the bleeding edge of machine learning. It can read and understand natural human behavior with extensive computational power.

Neural networks and deep learning goes hand in hand for AI. Deep learning is composed of many different layers and the outcome layer is more accurate in predicting without being programmed.

Data Science

Data Science as a Service and its development is a complex analytics system which supports competitive intelligence for business technology and service providers.

Cognitive Intelligence

Cognitive skills deal with factual knowledge such as documenting known information. It can be redefined as supercomputing; for having said that, it uses both computers and the process of human behavior. Using AI, it has the capability to generate reasoning and also enables machines to learn from data by applying statistical models.

For example, smart vehicles have AI based features such as self-parking, cruise-control etc. Such vehicle manufacturers are using deep learning techniques and neural techniques to deal with traffic.

AWS Machine Lens

Our Machine Learning solutions follow the best practices, suggestions and recommendations documented by AWS Machine Learning Lens. In this way, we increase efficiency and reduce costs of ML systems in the cloud, as well as identify potential areas of improvement.

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AI Ops

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