Machine Learning is Like Cooking
March 6, 2021
Attending the machine learning introduction orientation along with the first module was an interesting experience (at times not sure if I was coming or going). The pre-preparation did provide the participants some background to machine learning along with some real-life scenarios.
One of the Facilitators, Amit Varma, indicated that machine learning is similar to human learning. He provided everyone a comparison of how machine learning is comparable to cooking.
|1||Data Gathering||Ingredients Gathering|
|2||Cleanse data||Clean the Ingredients|
|3||Data organizing||Prepare ingredients for cooking|
|4||Build model||Prepare the meal|
|5||Deploy/Present model||Share and eat the meal|
What if you do not cook?
Fair question it is!
Humans learn mainly by two methods. First, they learn by example. This can be by watching someone doing an activity over and over.
Secondly, they learn by repeated practice. For example, riding a bike until it becomes second nature.
So yes, you can learn to cook along with machine learning!
How do I feel after completing my first class module?
To be honest, at times, overwhelmed and lost. However, I know that this is a big challenge that will require repetitive learning and practice. My classmates feel the same way, especially learning this cutting edge technology and how we are going to connect machine learning to an application in our industry.
This is a challenge that will push the participants to put the effort in to learn new concepts, software, and to think and apply ourselves differently.
Machine learning is a process. Once you learn the process and the various techniques to prepare data, you will be fine. Even though the software does many activities (algorithms, model performance, etc.) behind the scenes that support you, you still need to understand them. That’s one of my challenges that requires me to roll up my sleeves and learn these topics. In some cases, it has been some considerable time since I was exposed to these topics.
Regardless, once you define the business requirement and collect and cleanse the data the machine will assist you with typical models, algorithms and results that will support you. At the end, the deployed models are there to assist you in making decisions related to your business requirement.
PETER’S CONFIDENCE METER
Peter Buscemi brings energy in his role with Manitoba Hydro’s Supply Chain Management Strategic Sourcing group.
With over 25 years of business experience specializing in supply chain management, Peter has worked in a number of industries including Utilities, Public Sector, Retail, Distribution & Warehousing, Technology and Aerospace.
His past directorships include serving on Supply Chain Management Association’s (SCMA) Board of Directors along with local contributions with Winnipeg Chamber of Commerce and YES Winnipeg.
Peter holds a Supply Chain Management Professional (SCMP) designation and in 2015, he received the Fellow Award from the Supply Chain Management Association for his career achievements, contribution to the association and service to the community. It is the highest honour bestowed by SCMA.
Peter is actively involved in the community supporting the Kidney Foundation as a Board member. During his downtime, he enjoys cooking, gardening and time with his family.