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Artificial Intelligence & Machine Learning

AI Image Source - Freepik

Machine Learning Solutions

We are expertized on building end-to-end data science pipeline required for Data Analytics, creating ML models, and for business intelligence. We offer expertised engineering services and solutions on various domains.

Our Core AI & ML Practices
  • Design-led engineering
  • Quick response to change
  • Fast go-to-market approach
  • Agile led processes
  • Partners rather than vendors
  • Scalability plans from the beginning

Machine Learning & AI Technology

Tensorflow

Flexible architecture of Tensorflow allows easy deployment of computation across platforms integrated with multiple data collection and conversion devices

Scikit learn

Straightforward API, breadth of algorithms and the quality of its documentation make scikit-learn simultaneously very approachable and very powerful

Theano

We are experts in using Theano. It allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently

Theano

We are experts in using Theano. It allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently

The Rise of Machine Learning

Machine learning is a revolutionary technology that enterprises are currently deploying to drive business results. As a seasoned Machine Learning solutions and service provider, Promact explores effective ways to enhance efficiency and implement smart ways to lower business risks.

machine-2x Image Source - Freepik
machine-2x Image Source - Freepik

The Rise of Machine Learning

Machine learning is a revolutionary technology that enterprises are currently deploying to drive business results. As a seasoned Machine Learning solutions and service provider, Promact explores effective ways to enhance efficiency and implement smart ways to lower business risks.

Real-time data science with AI

Key building blocks for applying artificial intelligence in enterprise applications are data analytics, data science and machine learning, including its deep learning subset. Promact helps you to leverage data science to build and modify AI applications in order to obtain information from huge data clusters and data sets.

Real-time-ai

Our Development Process

  • 01

    Analysis

    Detained analysis to identify if the client’s problem is AI or ML related.

  • 02


    Discovery

    Studying every aspect to understand the problem area.

  • 03


    Algorithm Selection or Creation

    Identifying the correct algorithm to solve the client’s issue or creating one if needed.

  • 04


    Self Learning

    The algorithm self learns and improves its efficiency.

  • 05

    Deployment

    Deploying the AI solution at the client’s end.

  • 01
  • 02
  • 03
  • 04
  • 05
  • Analysis

    Detained analysis to identify if the client’s problem is AI or ML related.

  • Discovery

    Studying every aspect to understand the problem area.

  • Algorithm Selection or Creation

    Identifying the correct algorithm to solve the client’s issue or creating one if needed.

  • Self Learning

    The algorithm self learns and improves its efficiency.

  • Deployment

    Deploying the AI solution at the client’s end.

  • 01

    Analysis

    Detained analysis to identify if the client’s problem is AI or ML related.

  • 02


    Discovery

    Studying every aspect to understand the problem area.

  • 03


    Model Selection or Creation

    Identifying the appropriate model to solve the client’s issue or defining one if needed.

  • 04


    Use of Data

    Incorporating historical data into the Machine Learning Model.

  • 05


    Training

    Training of the Model to use the data to make accurate predictions.

  • 06

    Deployment

    Deploying the ML solution at the client’s end.

  • 01
  • 02
  • 03
  • 04
  • 05
  • 06
  • Analysis

    Detained analysis to identify if the client’s problem is AI or ML related.

  • Discovery

    Studying every aspect to understand the problem area.

  • Model Selection or Creation

    Identifying the appropriate model to solve the client’s issue or defining one if needed.

  • Use of Data

    Incorporating historical data into the Machine Learning Model.

  • Training

    Training of the Model to use the data to make accurate predictions.

  • Deployment

    Deploying the ML solution at the client’s end.

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