- Accelerating Your Algorithms in Production [Webinar Replay] - Oct 16, 2018.
Numerical algorithms are computationally demanding, which makes performance an important consideration when using Python for machine learning, especially as you move from desktop to production.
Tags: ActiveState, Intel, Production, Python
- DevOps 2.0: Applying Machine Learning in the CI/CD Chain - Oct 2, 2018.
Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria.
Tags: ActiveState, Development, DevOps, Machine Learning, Software
- [Live Webinar] MLOps: Machine Learning Operationalization, Sep 27 - Sep 19, 2018.
Successfully pushing ML to production requires a shift in your DevOps practices to become MLOps, machine learning operationalization. Learn how to do it in this Sep 27 webinar.
Tags: ActiveState, Deployment, DevOps, Machine Learning
- Machine Learning with TensorFlow - Aug 16, 2018.
In this on-demand webinar, you’ll get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why you should be running on a CPU.
Tags: ActiveState, Intel, Machine Learning, Python, TensorFlow
- Accelerating Algorithms: Considerations in Design, Algorithm Choice and Implementation - Dec 18, 2017.
If you are trying to make your algorithms run faster, you may want to consider reviewing some important points on design and implementation.
Tags: ActiveState, Algorithms, Implementation, Python
- Robust Algorithms for Machine Learning - Dec 11, 2017.
This post mentions some of the advantages of implementing robust, non-parametric methods into our Machine Learning frameworks and models.
Tags: ActiveState, Keras, Machine Learning, Python, Statistics
- Accelerating Your Algorithms in Production with Python and Intel MKL, Sep 21 - Sep 8, 2017.
We will provide tips for data scientists to speed up Python algorithms, including a discussion on algorithm choice, and how effective package tool can make large differences in performance.
Tags: ActiveState, Intel, Production, Python
