2020 will forever be remembered as the year that COVID disrupted all our lives, But there were some significant advances in technology over the past 12 months that will also result in dramatic changes to our personal or business lives. Many of them are linked to COVID itself, as organizations scrambled to innovate workarounds to the limitations imposed by COVID restrictions, including the delivery of a COVID vaccine faster than was ever thought possible.

For those of you that may have been overwhelmed by all the COVID news and missed them, here’s a roundup of some of the year’s most…

We’re researching how ActiveState can help improve enterprise CI/CD tooling and practices. Take the State of Enterprise CI/CD Survey.

Continuous Integration and Continuous Delivery or Deployment (CI/CD) is an agile software development best practice designed to enable more frequent and reliable code changes. And if you’re building a commercial application, who wouldn’t want that? As a result, there are a large number of CI/CD vendors with various offerings vying for a place in the software development processes of organizations.

With so many vendors and solutions, no universal out-of-the-box approach exists. In fact, most enterprise implementations are a mix of proprietary…

Python 2 EOL Survey Report
Python 2 EOL Survey Report

ActiveState surveyed >1200 developers at the end of 2019 to better understand their plans for Python 2’s impending End Of Life (EOL). The results are now in, and have been compiled into a synopsized report that’s currently available for download. If you haven’t started to deal with the EOL issue yet, you’re not alone. The report can help you:

  • Gauge where your EOL efforts stand relative to your peers
  • Plan for migration to Python 3
  • Understand your options if your migration efforts are blocked

This blog post will dive into a few more details, and give you some insight as…

Python 2 — The Time is Running Out

If you’re like most Python developers, you’ve been working with Python 2 for years. Now, with the Python Software Foundation announcing the End of Life (EOL) for Python 2, the core development team will no longer support, update or provide new versions of Python 2 as of January 1, 2020.

As a developer, you’d probably prefer to adopt Python 3, but for various reasons (corporate policy, lack of resources, legal restrictions, contractual obligations, etc.), your organization may be stuck maintaining existing applications on Python 2 (or even continuing to develop with Python 2) for the foreseeable future. …

When it comes to driving, we don’t make the student assemble the car before learning how to drive. Yet when it comes to learning Python, users first need to learn how to:

  1. Install Python, preferably in a virtual environment
  2. Use pip to install all requirements
  3. Resolve any dependency conflicts, as needed

Before they can even begin to start their Python course. This approach assumes the user has some familiarity with Python before they’ve even installed it, which can be both a false assumption by the course creator, and intimidating for the student. For example:

  • Which Python version should I use…

Summary: automating build engineering is the only viable way to ensure lower costs and increased throughput. Up till now, fully automating the build engineering for an open source language hasn’t been worth the cost. As a result, manually building your own languages is costing you more than you might think.

As discussed in a previous blog post, all of the costs of build engineering are rarely taken into consideration when determining overall project costs. …

Create your own free chatbot environment with just a few commands and learn more about the benefits of customer service chatbots in this post.

Opinions are divided over the corporate use of chatbots: surprisingly, most people seem to love them (see below), while others find them annoying. What’s not up for debate is the cost savings and 24×7 customer service chatbots enable.

Some key points to consider:

If you’re like most enterprises, you’ve got teams working on multiple applications that share much the same technology stack.

Let’s take Python as an example. You might have teams working on web-based applications that incorporate Python’s web packages (like Django or Flask). But these days you’re also likely to have other teams working with Python on a data science project involving numpy, scipy and TensorFlow.

If you’re running a modern development process, the output of these teams is likely getting deployed as docker-ized instances of standalone, discrete services. All best practices to ensure immutability. …

These days, it seems like Machine Learning (ML) is everywhere. Well, everywhere except in legacy applications. But you can hardly blame the application if it was created prior to the recent advances in Python that have made ML mainstream (i.e. the last 3–5 years). Unfortunately, rewriting or refactoring legacy applications is typically a cost-prohibitive non-starter. But if you don’t modernize older applications by adding capabilities like ML, your customers will sooner or later stop using them.

Certainly, the market is now full of established analytics and reporting applications that are all making the leap to include some kind of AI/ML…

It seems like Artificial Intelligence (AI) and Machine Learning (ML) are everywhere these days. In fact, it’s surprising how much of it we already take for granted, such as recommendations about what we watch and buy (Netflix and Amazon), personal assistants (Siri and Alexa), fraud alerts (Visa and MasterCard), route optimization (Google maps), and spam filtering.

But these examples are just the tip of the iceberg. It won’t be long before ML-driven applications are the norm, rather than an interesting novelty. …

Dana Crane

With 25+ years in software, I’ve had my share of both crossing and falling into the chasm. I’m currently the Product marketing Mgr at ActiveState Software.

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