With a background in database design/development, software engineering, and machine learning/artificial intelligence, Dan Wellisch has built domain knowledge in Data Engineering. Dan is dedicating this site to discussions on this subject on the Blog page and to description of his projects on the Clients page. The site is maintained by Wellisch Software Technologies, Inc. (WST, Inc) and all projects are procured by WST, Inc.
Dan graduated from NC State University with a B.S. in Computer Science and an M.S. in Computer Science (Artificial Intelligence Concentration). NC State University was the first school to offer the Master Of Science In Analytics. Dan has spent most of his career in software design, development, and database, but has easily moved into new areas as Dan is a lifelong technology learner. Machine Learning is simply a branch of AI, but it is an area that is concerned with helping humans to be more efficient in their work. It is not meant to replace humans and their superior judgement over machines. Efficiency (in processing the inexhaustible amount of data) is the strength of machine learning and where it is better than humans. When Dan was getting his Masters in AI, the focus was on Expert Systems (actually trying to clone the decision making of a human) using computer languages such as LISP and Prolog. This was done by creating if-then rules. Today, we rely on data patterns, which is the focus of machine learning/deep learning.
Machine Learning has actually been around since the 1950s so it is not new; it has simply evolved just like any other area in computing. We also have much easier access to cheap computing power by virtue of the cloud. Very limited access to computing power was a big problem in the 1950s, but not today.
Lately, there are 2 labeled professional titles: Data Engineers and Machine Learning Engineers. Data Engineering is simply Software Engineering with added Data Manipulation. Machine Learning includes Feature Engineering, configuring Machine Learning Models, Software Engineering, and Data Manipulation. WST, Inc. merges these 2 disciplines into a form of Hybrid Engineering. Machine Learning Engineering is simply an extension of Data Engineering.
Dan has completed the following machine learning/deep learning certifications delivered by Andrew Ng of Stanford.
Dan has also completed courses in Basic and Inferential Statistics from Coursera. He was a Python contributor to the Healthcare.AI open source software sponsored by HealthCatalyst.com. His contributions are documented here. He has also built a Machine Learning Framework which he provides to clients as part of engagements.
Dan Wellisch may be contacted here.:
danw AT wellsoftware DOT COM