ActiveState Blog

  • Helping Enterprises Keep up With Coder Innovation
    stevejobs

    ActiveState just announced our SaaS Platform. This is another huge step in the evolution of our company. A company that has 20 years rooted in open source languages. And a company that I have had the privilege of leading and growing over the last decade. I’m excited about what the year ahead brings. 

    A quick look back

    When I joined ActiveState in 2006, we were known as a provider of commercial-grade open source languages. But we’re so much more than that, at our core, we’re an innovation lab. 

  • Shifting Security Left, into the Application
    Shift left security

    How do you:

    • ensure you don’t join the trash heap of hacked enterprises? 
    • make security part of the SDLC from the get-go and not an afterthought?
    • address the latest attacks that bypass traditional network security protections?

    You need to take a paradigm shift in your application security. 

    Organizations have been moving security controls leftward in the software development lifecycle. Now it’s time to consider baking security in from the start, before a single line of code is written. 

  • Options for Deploying Machine Learning Algorithms to AWS
    Amazon Machine Learning

    AWS is a great place for accessing scalable, cheap resources on which to deploy data models.

    However, actually using AWS for this purpose can be challenging. If you didn't begin your project on AWS, you have to figure out a way to migrate it there. In addition, you have to determine how to handle the dataset against which you run your algorithm: should you move all of that data into AWS (and deal with the privacy challenges that this raises), just stream the data (which is not cheap), or do something else?

  • Poodle, Pug or Weiner Dog? Deploying a Dog Identification TensorFlow Model Using Python and Flask
    Dog identification with TensorFlow, Python and Flask

    In my last post I surveyed the growing array of options for deploying your ML models into production. In this post, we’ll create a demo to see how simple it is to develop your own service using Python’s Flask library.

    There are a number of cases where you might not be able to use a cloud service to host your model and would be required to roll-your-own inference service. In many large enterprises, on-premise solutions are mandatory. Approvals to purchase third-party solutions can also be lengthy and complex. So developing your own small service may be the best solution.

  • How to Deploy Machine Learning Models to a .NET Environment

    Python and R are among the most popular programming languages for data-centric engineers. However, they are not always the languages that the rest of an application is built on. This is why you sometimes need to find a way to deploy machine-learning models written in Python or R into an environment based on a language such as .NET.

    In this article, I show how to use Web APIs to integrate machine learning models into applications written in .NET.

  • How to Get Hadoop Data into a Python Model

    Hadoop is an open-source software framework for distributed storage and distributed processing of very large data sets. All the modules in Hadoop are designed with an assumption that hardware failures should be automatically handled by the framework.

  • Komodo IDE 11.0.2 Released
    Komodo IDE 10.2.3 Released

    While we are hard at work on Komodo 11.1 we didn't want to keep you waiting for some vital fixes to bugs introduced since Komodo 11 was released. The theme for this release is mainly around CodeIntel 3.0 and its completions mechanic. Due to the large amount of changes in CodeIntel bugs are almost unavoidable, fortunately we are always around to address these as soon as possible.

    This release mainly addresses the following areas:

  • Top 3 Features I’m Excited About with Komodo 11!
    Komodo 11

    We’re heading towards the end of year and I’m reflecting on what I’ve accomplished in 2017. A big part of my 2017 effort was on the recent release of Komodo 11. That got me thinking about my favourite features in Komodo 11. I’m still excited about them and have been using them a lot since the 11.0 release. I think users will love them too.

    The features are (listed in alphabetical order, I couldn’t like one more than the other!):

  • Integrating AWS Machine Learning Models with Your Java Microservice
    Integrating AWS Machine Learning Models with Your Java Microservice

    I built my first Artificial Intelligence (AI) program almost 30 years ago. I took an identification key from a bird field guide and turned it into an application riddled with an embarrassing number of GOTO statements which led the user through a series of adaptive A/B questions and then presented them with the most probable species identification at the end.

  • Operationalizing Machine Learning
    Operationalizing machine learning

    Machine Learning (ML) powers an increasing number of the applications and services that we use daily. For organizations who are beginning to leverage datasets to generate business insights -- the next step after you’ve developed and trained your model is deploying the model to use in a production scenario. That could mean integration directly within an application or website, or it may mean making the model available as a service.

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