No Code Automated Solutions for Machine Learning and Artificial Intelligence

What People Are Not Telling You about AI / ML

May 23, 2024

I will always remember the day I automated data cleaning and ingestion for my ensemble machine learning algorithm for the first time, but that was 20 years ago! For an entire decade or so, I thought automation would take over Data Science, Machine Learning, and Artificial Intelligence, and I was wrong. Today, too many people think you need to be a code warrior with profound software programming skills to be successful in Artificial Intelligence and Machine Learning. This is garbage because industry biases are not telling you about the true nature of Data Science, Machine Learning, and Artificial Intelligence.

Brief History

In the late 1960s and early 1970s, IBM started offering software packages that included statistical techniques that can be classified today as Machine Learning. The linear models were the fundamental building blocks that led to the creation of Neural Networks in the 1980s at AT&T Bell Laboratories that can classified today as Deep Learning. At the same time, several statistical software packages continued to provide Machine Learning solutions, starting with BMD (Biomedical Computer Programs) in the 1960s with the FORTRAN programming language at my alma mater UCLA, followed by various other Machine Learning solutions like SPSS, Minitab, Statistica, and S and R programming languages, until everything changed in the early 2000s.

The Influence of Big Tech on AI / ML

The success of big technology companies, like Amazon, Apple, Google, Meta Facebook, Microsoft, and others pushed the popularity of C++, C#, Java, JavaScript, and other programming languages for essential software development programs that contributed to tremendous company growth. Yet, it permanently changed the sentiment of Machine Learning, causing enormous confusion . . . by erasing the above history. Promoting boot camps, investing in universities with scholarships and partnerships, open-source software contributions, and countless public advocacy on the importance of manual coding. Almost everybody believes any role in artificial intelligence and machine learning requires profound coding skills, even though automated no-code software solutions have existed for several decades, including from Big Tech companies!

No Code Machine Learning

A few weeks ago, I was rewriting the curriculum of a Machine Learning course at the University of California, San Diego, when I watched a 2005 Tube video of a professor explaining how to process ensemble machine learning solutions with no code using automated software. There is very little content that I had to change because the overwhelming majority of machine algorithms are several years old, providing ample time and opportunity to automate. But what software solutions also offer no-code solutions for Machine Learning? Here is a list, but it is not comprehensive.

Aible

Alteryx Machine Learning

Amazon SageMaker Canvas

Microsoft Azure Machine Learning Automated ML

Microsoft Azure Custom Vision

Obviously

Orange

RapidMiner

SAS Visual Data Mining and Machine Learning

Of course, many factors contribute to your business deciding whether the above no-code solutions are a good fit. Some of the above solutions are expensive, but some are cheaper. To keep this article short, here are additional links for other resources you may need to automate your AI / ML for your company.

Generate SQL Statements for Database Queries

AI2SQL

Athena

OpenAI Codex (GPT-3)

Or you can prompt large language models like Claude and Perplexity

If you would like to learn more and discuss the above automated solutions, schedule a discovery call. Lower your business costs and operational risk, and free up your time for more important issues for your organization.

Individual AI Accelerator for (non) technical executives and directors.

AI Incubator for Corporate Team Training for both business and technology groups.

The Global Institute of Data Science

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