Published: October 5, 2017 Last Updated: December 7, 2021

ActivePython for Machine Learning: Transform Data into Knowledge

UPDATE 2021 – ActiveState’s Community Edition (CE) language distributions (ActivePython, ActivePerl and ActiveTcl) are being phased out and will soon only be available to our enterprise customers. Instead, ActiveState is replacing them with the ActiveState Platform ecosystem, which provides a modern experience for developers working with machine learning.

Python has emerged as a key productizing tool for machine learning. ActivePython provides all the packages for data science and machine learning, and is also pre-optimized for computational performance to ensure productivity right out of the box.

Transform Data Into Knowledge Using Machine Learning

Machine Learning is fast becoming a key, strategic initiative within the enterprise, allowing you to derive insight from all the data you’ve been collecting, and then leverage it to create differentiation in the marketplace.

Machine learning can help you solve business problems utilizing mathematical models that extract knowledge from data. Falling under the discipline of Data Science, machine learning can aid enterprises in achieving everything from sales & marketing goals to strategic & financial planning to risk & fraud detection. Machine learning models separate signal from noise in real time to help you interactively spot trends and anomalies in large data sets better and faster than simple statistical analyses.

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Getting Started In ML

While there are numerous vendors of proprietary data science, big data, and scientific computing applications, building a custom machine learning solution can provide you with greater agility and control, especially if you have unique data that grants you key differentiation in the marketplace.

However, operationalizing machine learning models by embedding them either in a production application or business process remains a major challenge.

Python has emerged as a key productizing tool for machine learning projects due to its high productivity (being a scripting language), and wealth of third-party data science components, but it can be orders of magnitude slower than native code.

Machine learning models are derived from numerical algorithms that are typically computationally expensive, which makes performance a key consideration. More importantly, for realistic scientific modeling it’s critical to scale from a prototyping environment, which is typically a laptop or a workstation, to multiple nodes.

  • Faster Algorithms – Pre-integrated with intel MKL and pre-optimized for speed.
  • Less Data Work – Automate the bookkeeping of metadata with Pandas.
  • Better Collaboration – Share code & annotations via Jupyter.
  • Drive Results – Rapidly build & evaluate models with Keras.
  • Decrease Time To Market – Get precompiled Python & machine learning packages.
  • Reduce Risk – Python security scanned & backed by SLA-based support.
  • Ensure Compliance – Vetted pre-bundled 3rd-party packages.
  • No Vendor Lock-in – 100% compatible with community open source Python.

 

ActivePython For Machine Learning

Machine learning projects start with the data. Typically, 80% of the work is cleaning up the data, feeding it to your algorithms and training the machine learning component. If you’ve done a good job normalizing the data, you’ll get convergence and a model you can use.

ActivePython includes open source community packages like Pandas to help with the data pre-processing. Packages like TensorFlow, and Keras, as well as scikit-learn, provide the algorithms, additional libraries, computational power and user-friendly control to develop the learning stages. A key bottleneck in any machine learning project is the processing of algorithms.

ActivePython incorporates Intel’s Math Kernel Library (MKL), which takes advantage of multiple cores and vector registers to accelerate basic linear alge-bra operations and solvers, Fast Fourier Transforms (FFTs), arithmetic and transcendental operations, and more. This means mathematical routines and model training run faster, so you can get your project to market faster

Datasheet ActivePython For Machine Learning Graphic


Python By ActiveState

ActiveState Python provides you open source community packages like Pandas to help with the data pre-processing. Packages like TensorFlow, and Keras, as well as scikit-learn, provide the algorithms, additional libraries, computational power and user-friendly control to develop the learning stages.

Choose the packages you need and get your machine learning project to kick off right away! Install Python by ActiveState to get started or contact us to learn more about using ActivePython in your organization.

Mike Kanasoot

Mike Kanasoot

Mike is the Web Marketing Manager at ActiveState. He has worked in industries ranging from security and document management to mobile commerce, but enjoys the culture of open source technology in particular. As a marketer, Mike believes in providing great user experiences and tracking everything.