You have programmed for a few years and have cluttered up your sandbox, your local development machine, with downloads of many different libraries, frameworks, and maybe a virus or two.
The above described my life in 2008. The arduous tasks below detail the giant PIA (you are on your own to resolve the acronym) in my life at the time:
As part of the AI 1.0 surge (1983–1987), I felt that AI, to be of practical use, had to be distributed. Since then, I have been building distributed operating systems.
I admit I stopped when I encountered Kubernetes in 2016 because from a Software Architect and Engineer viewpoint, there was little I could add, and what I could add would be minor.
Yes, I think Kubernetes is a great distributed operating system architecture for a cloud of virtual machines (VMs) to a hive of Rasberry Pis.
I returned to Machine Learning about nine years ago during the AI 2.0 …
I was taught to transform a complex problem into a simpler problem, by dividing the problem into smaller sub-problems.
I made the process paradigm of Machine Learning Operations (MLOps) simpler by dividing it into five different, but overlapping process groups or Operations (Ops) groups.
MLOps is automating the Machine Learning product life cycle.
In 1990, we had 80%+ IT projects that were never rolled out. Failure rate dropped because of standardized developer tools, repeatable process iteration, death of the Waterfall method, a rise of the Agile method, and unit testing — to list some of the code development advances.
I plan to increase stability and performance and to decrease the cost of maintenance in the Photonai code base. I add clustering functionality to the original code base and change the architecture.
A small code refactoring task is usually fixing bugs. Some consider that it is not refactoring if the bug-fixing occurs before releasing to test.
A significant code refactoring project example is causing a program to be Y2K-compliant but not changing functionality. Y2K compliance is enabling code to operate correctly with dates at or beyond January 1, 2000. (Yes, this was a thing!)
Python is a dynamically typed language. However, starting with Python 3.5 (PEP 484), type hints were introduced. Type hints (note: not strong type checking) make it possible, post coding of Python, to do static type checking of code.
Here’s a great figure showing the evolution of Python type hinting:
Docker Containers inherit environment variables from their host, set externally at launch-time, or set internally in the Dockerfile. Also shown is how to patch a running Docker container.
A software engineer needs to know a broad range of Linux commands. Why?
Use Scikit-Learn as a core Machine Learning (ML) library and use other packages to broaden your ML solutions. As an ML scientist, you may use Deep Learning (DL) frameworks such as Pytorch or Tensorflow for specialized ML models.
Photoai incorporates Scikit-Learn and any other ML/DL frameworks with one unifying paradigm. Photonai adopts Scikit-Learn’s
Photonai is a great Kaggle framework or framework for your enterprise solutions (work).
Scikit-Learn supplies the majority of Photonai’s core of machine learning (ML) algorithms.
Photonai adds code that reduces manual coding and error by transforming pre- and post-learner algorithms into components with parameters. Examples…
The rendering of high-quality architecture diagrams of Azure, AWS, and GCP is shown using the Python package Diagrams. Diagrams depend on the Graphviz runtime. This article shows step-by-step how to create a Docker image with Diagrams and Graphviz. All code is included and can be downloaded.
I have posted several articles on creating development and test Docker images [see references 4, 5, and 6 below]. I assume you know of Docker and have read them.
Docker is used for encapsulating an individual image of your application.
Docker-Compose is used to manage several images at the same time for the same…
I recommend that you audit edX AWS SageMaker. I would not pay for the course certificate, and I recommend neither should you. I do detail twenty-one alternative resources (free).
I go through the AWS (Amazon Web Services) Sagemaker Certification EdX course .
In the beginning, I used my standard review filters of: “I wish I had known that.” or “I wish that had been emphasized.”
In a way, I guess I did ask the above review filters. Unlike my previous articles, it ended up being a shortlist of “I wish I had known that.”.
I TRY to stay neutral. I…
Yes, the title is a little controversial. However, our experience leads us to predict that GitHub Actions will be the dominant choice for Continuous Development, Continuous Integration, and Continuous Deployment on and off GitHub. We list nineteen reasons why we prefer GitHub Actions over Jenkins. We finish with a walkthrough of creating and then running a GitHub Actions script for automatic checking of PEP-8 formatting.