For data science teams to succeed, business leaders need to understand the importance of MLops, modelops, and the machine learning life cycle. Try these analogies and examples to cut through the jargon.
Because building reliable data pipelines is hard, and the first step to becoming a data-driven organisation is trusting your data.
Implement observability in strategic areas of the software development life cycle, especially with complicated microservices and cloud-native apps.
When time is money or safety, or you face data compliance regulations, edge computing may be your best bet. Here are 5 scenarios where edge makes sense.
Sadly, there’s a chance that your organisation might have to downsize because of an economic downturn or other financial conditions.
Let’s say a company’s data science teams have documented business goals for areas where analytics and machine learning models can deliver business impacts.
Establishing a common understanding of IT technical terms helps manage executives’ expectations and gain their support.
How do users know when to simply relocate an app and when to rebuild from the ground up? Here are some tips on evaluating the technical side and the business case.
Whether companies want to leap or tiptoe into the metaverse, they should decide which of these key technologies aligns with their business strategies.
Despite the rise in no-code/low-code platform usage, IT still has to step in. Watch for these red flags that mean solutions aren't hitting the target.
Changing assumptions and ever-changing data mean the work doesn’t end after deploying machine learning models to production.
Data-driven decisions require data that is trustworthy, available, and timely. Upping the DataOps game is a worthwhile way to offer reliable insights.
Industry leaders agree that data governance belongs to everyone in IT. Managing the privacy, security, and reliability of data impacts all aspects of the business.
Important knowledge is scattered throughout the organisation. Simplify everything, make it easy for employees to find what they need.
Devops is tough, but the choice between faster development and improving reliability shouldn't be. Now is the time to consider shifting-left.