Today’s enterprise data science teams have one of the most challenging, yet most important roles to play in your business’s ML strategy. In our current landscape, businesses that have adopted a successful ML strategy are outperforming their competitors by over 9%. The implications of ML on the future of business are clear. However, only 4% of enterprise executives today report seeing success from their ML investment.
Start-ups founded on analytics have been shaking up every industry. Finance has been disrupted by Monzo's data focus, Netflix’s analytics has upended film entertainment, and Swyfft has used data to change the game for US home insurance. Today's users have come to expect analytics in their applications.
The pandemic has accelerated enterprises’ digital transformation investments, notably their efforts to use AI and the cloud to meet rising customer expectations.
A watershed year brought continued rapid growth, major engineering advances, and partner awards from Google, AWS, Snowflake and Databricks.
It is a truth universally acknowledged that the culture change side of any technology transformation program is the hardest and slowest part to get right. If you cannot efficiently operationalize a technology investment, that investment is wasted. This is no different in the world of APIs and microservices, where every service is designed to support a change to a digital-first culture. APIOps makes this change possible.
Usage-based pricing, consumption-based pricing, and PAYG (Pay As You Go) are relatively new SaaS pricing models that enable you to drive both top of line growth while also increasing net revenue retention over more traditional subscription pricing models such as license or seat-based pricing. With Pay As You Go, a customer only needs to pay for what they consume such as hours of a VM or number of messages sent.
ActiveRecord is Ruby on Rails’ most magical feature. We don’t usually need to worry about its inner workings, but when we do, here’s how AppSignal can help us know what’s going on under the hood.
Releasing with speed and confidence is every mobile team’s dream. To help you on that journey, mobile release trains can introduce a quicker release model and faster mobile cycles — making distributed development teams, that work on different parts of an application, become more aligned — regardless of their size.
Spreadsheets are everywhere! They are one of the most useful productivity tools available. They make organizing, calculating, and presenting data a breeze. Google Sheets is the spreadsheet application included in Google Workspace, which has over 2 billion users. Machine learning, or ML for short, has also become an essential business tool. Making predictions with data at low cost and high accuracy has transformed industries.
