Distributed machine learning platform to handle TensorFlow projects

Author: Carol Davis

Machine learning platforms are geared towards enabling other software applications to make better decisions or recommendations and thus must easily integrate with them. A platform for automating and accelerating the delivery lifecycle of predictive applications is capable of processing big data using machine learning or related techniques. A few of the key ideas captured in this definition are:

  • Acceleration- There are two meanings of acceleration for machine learning platforms. (a) Acceleration of the solution delivery lifecycle. (b) Through technical advances run-time acceleration achieved such as distributed and in memory computing.
  • Automation- The real-world job of data analyst consists of many tasks which are repetitive and time consuming. Automation of tasks saves time and eliminates repetition and helps organizations to deliver new projects more quickly, update existing projects and take on more projects without increasing staff.
  • Big data- From a wide variety of sources a machine learning platform must be capable of helping users to process large volumes of data.
  • Platform- Machine learning platforms are geared towards enabling other software applications to make better decisions.

Distributed machine learning has become more important than ever in this era of big data. Distributed machine learning tool kit is a platform deigned for distributed machine learning. In recent years, the trends have been demonstrated that in various applications more training data and bigger models tend to generate better accuracies. For common machine learning researchers and practitioners it remains a challenge to learn big models from a huge amount of data as the task require a huge number of computation resources.

The rise of big data has led to new demands for machine learning with millions to billions of parameters to learn complex models that promise adequate capacity to digest massive datasets and offer powerful predictive analytics.

A TensorFlow "cluster" is a set of tasks that participate in distributed execution of a TensorFlow graph. The global team of TensorPort consists of scientist and engineers who aim to bring the best tools and practices to machine learning teams. TensorPort is the machine learning platform for distributed TensorFlow projects uniquely intended for machine learning teams. It has number of features that helps you carry out the task and -complete it in fast and easy way. This AI platform makes it easy for developers of all skill level to use machine learning technology with fewer efforts and in efficient way. TensorPort is highly flexible and scalable machine learning platform developed by the AI scientists. It uses powerful algorithms to develop new models and use existing data. The major purpose behind development of this highly sophisticated AI platform is to help machine learning teams to carry out their projects in less time with higher accuracy. So, if you are in search of the best AI platform can help you organize TensorFlow projects then only prefer TensorPort.