Companies of all sizes have embraced using data to make decisions. However, according to a 2019 report from Goldman Sachs, it’s actually quite difficult for businesses to use data to build a sustainable competitive advantage.
Our team has worked with and for companies across industries. We’ve seen the good, the bad, and the ugly of data strategy. We’ve seen teams implement successful data lifecycles, dashboards, machine learning models, and metrics. We’ve also had to come in and untangle, delete, migrate, and upgrade entire data systems.
Throughout these projects, we’ve seen several issues that pop up repeatedly: alack of data governance; bad data; complex Excel documents; a lack of alignment between data teams and the businesses; and an over abundance of dashboards, leading to confused decisions. …
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. …