Physicist, Machine Learning Scientist and constantly improving Software Engineer. I extrapolate the future from emerging technologies.

How often have you heard “The Machine Learning Application worked well in the lab, but it failed in the field. “? It is not the fault of the Machine Learning Model!

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Image 1. Failure. Source: Unsplash by cuttersnap

This blog is not yet another blog article (YABA) on DataOps, DevOps, MLOps, or CloudOps.

I do not mean to imply xOps is not essential.

For example, MLOps is both strategic and tactical. It promises to transform the “ad-hoc” delivery of Machine Learning applications into software engineering best practices.

We know the symptoms: Most machine-learning models trained in the lab perform poorly on real-world data [1, 2, 3, 4].

What is the critical Problem with Machine Learning Success?

Machine Learning created profits in the year 2020 and will continue to increase profits in the future. …


Equivalent mappings of seventeen cloud services of the three top market share cloud vendors: Azure, AWS, and GCP, are described and compared. The exception is the Machine Learning services, where Google has many more complete Machine Learning SaaS (Software as a Service) offerings [1].

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Figure 1. Cloud Computing. Image from Unsplash.

Cloud vendors continually add services. Diagrams is a work in- progress, as all services are not added yet (10/21/2020).

I do not discuss the category of security — a significant category for the cloud that is still evolving and needs a blog for itself.

I discuss Identity Management, which is secured by the authorized account.

Multiple accounts have been around since one of the first multi-process operating systems (MULTICS in the 1960s). …


We show Python code and benchmarks for 27 different NLP text pre-processing actions.

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A natural pipeline: Source: Unsplash

Estimates state that 70%–85% of the world’s data is text (unstructured data) [1]. New deep learning language models (transformers) have caused explosive growth in industry applications [5,6.11].

This blog is not an article introducing you to Natural Language Processing. Instead, it assumes you are familiar with noise reduction and normalization of text. It covers text preprocessing up to producing tokens and lemmas from the text.

We stop at feeding the sequence of tokens into a Natural Language model.

The feeding of that sequence of tokens into a Natural Language model to accomplish a specific model task is not covered here.

In production-grade Natural Language Processing (NLP), what is covered in this blog is that fast text pre-processing (noise cleaning and normalization) is critical. …


It is code review time. Some of you would rather avoid the code review process. Whether you are new to programming or an experienced programmer, the code review is a shared learning experience for all involved. Rather than talk about “code review process best practices,” I share with you coding techniques I use to change code review from WTFs (What’s That For?) into WOWs (Wonderful! Oh! Wow!).

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Wonderful! Oh! Wow! from Unsplash

My Approach to the Code Review Process

The anticipation of a code review process causes us to raise our game because we open-up our code for other programmers to see (criticize). It may look, feel, and bark like criticism. And just maybe it is. But like a bar fight, it is a chance for you to grow and bond with your team-mates. …


Visualize your architecture

clouds seen from above
clouds seen from above
Figure 1. Cloud computing consists of a tangled weave of datacenters throughout the world. (Photo by Thomas Richter on Unsplash)

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.

Docker Solution for Graphviz, Diagram, and Cluster

I have posted several articles on how to create 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 application. This tool offers the same features as Docker but allows you to have more complex applications. …


I recommend Architecting with Google Cloud Platform (GCP) or Developing applications with GCP Coursera specializations. Going through these specializations will significantly help you obtain the Google Cloud Architect certification or Google Cloud Developer certification. More importantly, hands-on labs enable you to build GCP applications.

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The Google Cloud Platform.

The course, Essential Google Cloud Infrastructure: Foundation is the first of the five courses in Cloud Engineering with Google Cloud specialization and the second of the six courses of Cloud Architecture with Google Cloud Professional Certificate specialization.

Google Cloud Associate Cloud Engineer Certificate and Cloud Architecture with Google Cloud Professional Certificate share the first three classes.

The Essential Google Cloud Infrastructure: Foundation class consists of six-part of videos virtual…


I recommend Architecting with Google Cloud Platform (GCP) or Developing applications with GCP Courseraspecializations. Going through these specializations will significantly help you obtain the Google Cloud Architect certification or Google Cloud Developer certification. More importantly, the hands-on labs enable you to build GCPapplications.

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The Google Cloud Platform.

The course, Coursera Google Cloud Platform Fundamentals: Core Infrastructure, is first of the five courses in Cloud Engineering with Google Cloud specialization and first of the six courses of Cloud Architecture with Google Cloud Professional Certificate specialization.

Google Cloud Associate Cloud Engineer Certificate and Cloud Architecture with Google Cloud Professional Certificate share the first three classes.

The Coursera Google Cloud Platform Fundamentals: Core Infrastructure class consists of five essential…


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).

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AWS Sagemaker pipeline. Image Source: slideshare.net

I go through the AWS (Amazon Web Services) Sagemaker Certification EdX course [1].

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 TRY not to write down my opinion. I TRY to write down only facts. …


I list concepts and lesson gap-filling content you will need for the edX AWS Developer course to pass and be awarded a certificate.

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There are many Amazon Web Services, like many Amazon tributaries. Image Source: Unsplash

I go through the AWS (Amazon Web Services) Developer Certification EdX course [1].

I define the terms and concepts of the course. Also, I add additional explanations and tips that may help you.

I use it as my filters: “I wish I had known that” or “I wish that had been emphasized.” I do not expand on material that I think is adequately covered.

I recommend using this blog content week by the week

  1. Skimming through concepts and exercise sections for that…


Microsoft licensed GPT-3 [0]. So my answer is yes. GPT-3 and forthcoming Natural Language Processing (NLP) models can create a bigger bias problem by generating believable “alternative facts.”

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Source: twitter.com

I read Dr. Hinton’s tweet yesterday. He estimates 4.4 trillion parameters to obtain the … the answer to life… ). I think he used this graph from:

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Source: Language Models are Few-Shot Learners [1].

I may be wrong on guessing how Dr. Hinton derived 4.4 trillion parameters from the above graph. However, given the number of significant figures given (four), it implies a standard error of +/-0.0005 trillion (or +/- 500 million).

I will take a risk and state that given the number of significant figures and Dr. Hinton’s background in cognitive brain science, he is “tongue in cheek,” giving the 4.4 trillion parameters an answer to life….

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