Data scientists and data analysts are constantly required to answer questions for the business. This could result in a more ad-hoc analysis or some form of model that will be implemented into a company’s workflows.
But to perform data science and analytics, teams first need access to quality data from multiple applications and business processes. …
Did you know that $28.5 billion were spent on investing in machine learning projects, tools, and employees in 2019?
Machine learning has taken over pretty much every industry, owing to the automation and flexibility it is bringing to work. Companies of all sizes are using tools and cloud services like AWS Comprehend and other similar services to improve their business workflows and create new products.
However, one of the concepts that is slightly newer and proving to be helpful in complex machine learning deployment environments is MLOps.
Is the suffix “Ops” a little overused? Yes.
But MLOps has its place in the technology world. It’s actually a sign of a maturing discipline. As best practices on machine learning model management and deployment become more crystalized, it becomes much easier to develop automated platforms that can manage many of the mundane and error-prone steps in your machine learning workflow. …
Parsing and processing documents can provide a lot of value for almost every department in a company. This is one of the many use cases where natural language processing (or NLP) can come in handy.
NLP is not just for chatbots and Gmail predicting what you are going to write in your email. NLP can also be used to help break down, categorize, and analyze documents automatically. For example, perhaps your company is looking to find relationships through all of your contracts or you’re trying to categorize what blog posts or movie scripts are about.
This is where using some form of NLP processing could come in very handy. It can help break down subjects, repetitive themes, pronouns, and more from a document. …