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Do AI/ML in real time with TensorFlow and Deephaven

Expedite your ML workflows with intuitive, accessible, and robust methods for state-of-the-art ML models during all phases of the development lifecycle

tensorflow and deephaven

TensorFlow easily integrates with Deephaven Core​

TensorFlow is one of the de facto standard libraries for AI/ML. Its Python API boasts features including built-in support for automatic differentiation, distributed computing, and eager execution. TensorFlow also contains a vast array of functionalities for building networks, network operations, loss functions, performance metrics, optimizers, and more. Furthermore, the open-sourced TensorFlow is used all over the software industry. Users don't have to search far to find example use cases, instructional guides, and blogs that showcase its usage in meaningful ways.

Deephaven users should leverage TensorFlow for its incredible range of functionalities for AI and machine learning. It allows users to easily and quickly build, train, test, validate, and deploy models that work natively on common data structures like NumPy arrays. Its API features multiple abstraction layers for both high- and low-level control over deep learning networks. Furthermore, TensorFlow can be integrated in your Deephaven build with the suggested Docker image.

Real-time AI​

Use TensorFlow with Deephaven Learn to perform real-time AI.

AI/ML engineers and data scientists alike love Python for its incredible ecosystem of modules that make complex data processing easier. TensorFlow makes AI and machine learning more accessible than ever before. TensorFlow, however, doesn't give you an intuitive table API that makes real-time and big data processing simple. As a matter of fact, no Python module does that like Deephaven does. Let Deephaven take your Python AI/ML to the next level.

Python and TensorFlow together may be prove adequate for historical data analysis. However, you may run into difficulty when it comes to real-time processing. Enter Deephaven, the power up that gives Python more speed, grip, and control over the unpredictable conditions of your data.

Deephaven's bread and butter are big and real-time data processing. With deephaven.learn, you can apply AI/ML routines to data in both static and real-time contexts using the gather/compute/scatter paradigm. This paradigm reduces the limitations in applying complex routines to variable data of all shapes, sizes, and formats. Enhance Python with Deephaven and take on the daunting challenge of real-time data streams.

Deephaven examples that leverage TensorFlow​

Deephaven features numerous example apps, code snippets, and guides to help you get started with your own projects quickly. Here are some that leverage TensorFlow:

Your favorite Python libraries in real time​

Using TensorFlow in real-time applications can be as simple as a one-line method call to deploy a trained and tested model. Conversely, you can use more complex deployment strategies to customize how your model will interact with data and how its performance will be measured in multiple contexts.

Example projects with TensorFlow​

Cool things you can do with TensorFlow + Deephaven in real time:

  1. Sentiment analysis
  2. Classification
  3. Deep reinforcement learning
  4. Pattern recognition
  5. Forecasting

Develop with Deephaven Core​

Get the code

Pull the code directly from our GitHub repository to begin developing with Deephaven Core. Then you can easily create your Docker environment in three CLI commands.

Getting Started

Set up your environment and install Deephaven.

Need help? Ask a question onGithub DiscussionsorSlack.

TensorFlow docs:​