Data science and Machine Learning practice have been widely accepted by a large number of companies as a potential source of transforming business decisions and solving complex business problems. But when it comes to deploying machine learning models in production, it becomes a painful job for a data scientist and machine learning engineers to automatically orchestrate the entire data science flow smoothly like a traditional software development and deployment lifecycle and process.
For a generic data science problem, there can be many common parts involved like collecting data, building ETL pipelines, performing exploratory data analysis, fixing data insufficiencies, doing feature…
Introduction
Semantic Search can be defined as a way to perform a search query on the basis of not just word-by-word or character-by-character overlapping but through understanding the semantics/meaning of the content. If we will compare any possible search strategies with human decision making, then semantic search lies more closely to the human way of searching or looking for something. Because we understand the content and then look for it or try to relate it.
For a Digital Asset Management (DAM) company, semantic search can enable a powerful way to search and locate the right content with a single search that…
