Machine Learning in industrial settings operates in a different context than in academia. To increase success, one must deviate from the original Waterfall-like method of conducting modelling and adopt a more flexible approach. This is what we describe as “Lean in ML”.
Machine Learning in industrial settings operates in a different context than in academia. To increase success, one must deviate from the original Waterfall-like method of conducting modelling and adopt a more flexible approach. This is what we describe as “Lean in ML”.
One of the key and most overlooked aspect of Machine Learning is data labelling. I wrote about this here before, most recently in "Data Science Risk Categorisation" but as I was collecting my thoughts for our new e-book "Machine Learning Product Manual" I decided to revisit the topic one more time.
One of the most vibrant topics on the MLOps.community slack channel is the discussion around the difference between MLOps and DevOps. In this article, I will attempt to clarify the boundaries between the two derived from the difference between Machine Learning and Software Engineering using my previous article on Separation of Concerns.
To paraphrase Tolstoy: “Successful data projects are all alike; every unsuccessful project is unsuccessful in its own way.”
Reflecting on the article: “Why Scrum is awful for data science”.
One of the primary reasons for the lack of understanding of machine learning in corporate environments is the detachment of business value and statistics.
Despite all the hype and broken promises, Machine Learning has an incredibly strong advantage: It is a declarative paradigm.
Despite all the hype and broken promises, Machine Learning has an incredibly strong advantage: It is a declarative paradigm.
In a modern data driven enterprise there are three functions a data scientist can work on: data collection, data driven product or data driven prediction.
The term Data Science become such an umbrella term in the last years that the only way to define it is: “Data Science is what a Data Scientist does”.
