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TensorFlow AI Data Prediction - Machine Learning Development

TensorFlow AI Data Prediction - Machine Learning Dev.

What is TensorFlow AI Data Prediction - Machine Learning Dev.?

TensorFlow is an open source machine learning framework for everyone. It comes with great benefit while it contains a wide range of functionality and mainly designed for you to recreate your own machine learning based on your needs.

AI Machine learning is actually quite often used for daily business usage, such as:

  • Recommendation System

For example, in eCommerce business, machine learning filters detail of products that are likely to be of interest for the customers.

  • Financial Trading

Machine learning is also a great use for financial trading company, in which it predicts the stock prices based on market data with a sharp accuracy.

Our solution will be built with TensorFlow as the main framework for your own AI machine learning with wide range of functionalities. It operates on graph representation, which allows user to specify mathematical operation (such as: graph data, variable, and other math operation) as its main element.

TensorFlow originally built by Google Brain Team—one of the successful IT leading group— with a primer focus to conduct machine learning that is applicable in big variety of general user domain.

What would you get by purchasing this product?

Here is the detailed features you will get

01. Frontend

TensorFlow is an open-source software library that provides you:

  • Inference code
  • Training code
  • Evaluation code

Of which you can modify and reproduce their original data by using various programming languages, either Python, Java, Go, JavaScript or C++.

02. Progress Dashboard

After setting the basic input function, next thing that you should do is building a library of layers. Tensor Board is a nice visualization tool that TensorFlow develops. It acts as a dashboard and helps you to visualize every layer that you defined, so it is easier for you to spot any errors inside the architecture.

03. Progress Estimator

TensorFlow has an Estimator feature to check the training progress and evaluate the learning model. After you train the AI, the model can be adjusted for any prediction. Estimator allows you to evaluate the model and once it appropriately response, all you have to do is to export the model with TensorFlow Serving.

04. Learning Checkpoints

TensorFlow implements a check-point system which connects: training, evaluate, predict and export which always works in sync. This checkpoint is important to help in inspection; especially for several machine learning models that works separately but contributes in unity.

05. Robust Deployment

TensorFlow Serving is a flexible, high performance serving system for machine learned models, and perfectly designed for production environments. It has main purpose to deploy the research data from Estimator to production segment. What makes it different from self-build serving system is that TensorFlow Serving is safe and robust to pushout multiple models with an ability to roll back.

TensorFlow Serving keeps the model data in portable format which can be experimented on three major pillars, such as:

  • C++ libraries
  • Binaries
  • Hosted service

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