- Python 2 End of Life Survey – Are You Prepared? - Sep 18, 2019.
Support for Python 2 will expire on Jan. 1, 2020, after which the Python core language and many third-party packages will no longer be supported or maintained. Take this survey to help determine and share your level of preparation.
- TensorFlow Optimization Showdown: ActiveState vs. Anaconda - Sep 5, 2019.
In this TensorFlow tutorial, you’ll learn the impact of optimizing both operators and entire graphs, how to efficiently organize data in training and testing datasets to minimize data shuffling, and how to identify a well-optimized model using Anaconda and ActivePython.
- Comparing Decision Tree Algorithms: Random Forest® vs. XGBoost - Aug 21, 2019.
Check out this tutorial walking you through a comparison of XGBoost and Random Forest. You'll learn how to create a decision tree, how to do tree bagging, and how to do tree boosting.
- Exploratory Data Analysis Using Python - Aug 7, 2019.
In this tutorial, you’ll use Python and Pandas to explore a dataset and create visual distributions, identify and eliminate outliers, and uncover correlations between two datasets.
- How to Share Data Science Secrets Without Sacrificing Security - Jul 24, 2019.
Learn how to incorporate security into your practices without slowing down your project. Read this ActiveState blog post to learn more.
- How to Learn Python without First Needing to Learn Python - Jul 10, 2019.
Learn how data scientists and anyone coding with Python can set up a made-to-order runtime in minutes - not days. Read the 3-minute blog post.
- Customer Support Chatbots: Easier & More Effective Than You Think - May 20, 2019.
Learn how to create your own free chatbot environment with just a few commands, as well as learning more about the benefits of customer service chatbots.
- [White Paper] Unlocking the Power of Data Science & Machine Learning with Python - May 8, 2019.
This guide from ActiveState provides an executive overview of how you can implement Python for your team’s data science and machine learning initiatives.
- Top 10 Python Use Cases - Apr 24, 2019.
This paper covers 10 of the most common use cases by industry for Python that ActiveState has witnessed implemented by its customers.
- Build Python for Data Science in Just a Few Clicks - Apr 10, 2019.
There is only one Python distro that lets you add new versions of packages, remove unused packages, and rebuild in minutes. Yes, for free. Download ActiveState Python 3.6 build now.
- [PDF] Python: The Programmer’s Lingua Franca - Mar 27, 2019.
This paper presents the case that Python is the language best suited to becoming a programmer’s lingua franca.
- [PDF] Executive Guide To Machine Learning - Mar 13, 2019.
The Executive Guide covers the benefits to your business, the build-or-buy process, and gives a practical overview for implementing ML in your organization.
- Python 2 support ends this year. Are you ready to migrate? - Feb 27, 2019.
Python 2 ends on Jan 1, 2020. Migrating from Python 2 to 3 can be a scary process, so get this solution sheet with different options for moving your existing packages and applications from Python 2 to 3, along with best practice guidelines.
- How to Adopt Machine Learning: Interviews with Technical & Business Leaders - Feb 11, 2019.
This 8 chapter series includes interviews with technical and business leaders from a number of large companies with the aim to help you adopt machine learning in your organization.
- Making Machine Learning Accessible [Webinar Replay] - Nov 27, 2018.
Learn the business "why" and technical "how" for implementing machine learning in your organization - watch now.
- The Evolution of Build Engineering in Managing Open Source [Webinar Replay] - Nov 13, 2018.
Explore how the role of build engineering is evolving to reconcile two key trends: massive wide-scale adoption of open source; the most devastating cyber-attacks in recent history tied to unpatched dependencies and other vulnerabilities.
- How to Mitigate Open Source License Risks - Oct 30, 2018.
This whitepaper from ActiveState investigates the various types of OSS licenses, common myths and risks, DIY risk management, the importance of enterprise legal indemnification, and more.
- 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.
- 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.
- [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.
- 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.
- 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.
- 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.
- 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.