How to apply design thinking in data science
Design thinking is critical for developing data-driven business tools that surpass end-user expectations. Here's how to apply the five stages of design thinking in your data science projects.
Stories by Isaac Sacolick
7 mistakes to avoid when developing RPAs
Bots at their best offer a high return on investment—but there are risks. Here are seven mistakes software developers should watch out for.
Developing ecosystem-ready APIs and applications
Ecosystem-ready is not just about robust engineering, security, and operational practices. Here's what your devops team needs to know.
Computer vision's next breakthrough
Computer vision can do more than reduce costs and improve quality. Here's how hardware, software, and AI innovations are saving lives and improving safety on and off the factory floor.
5 ways to use AI and machine learning in dataops
Data quality is more important than ever, and many dataops teams struggle to keep up. Here are five ways to automate data operations with AI and ML.
3 ways to upgrade continuous testing for generative AI
As more CIOs and devops teams embrace generative AI, QA teams must also adapt their continuous testing practices to keep up.
Six interview questions for agile tech leads
The technical lead role is an important career milestone for many engineers. Here's an inside look at the questions interviewers ask and what they’re looking for.
What can ChatGPT and LLMs really do for your business?
Large language models already are transforming industries such as financial services, healthcare, education, and government. But what’s hype and what’s yet to come?
When low-code and no-code can accelerate app modernisation
Could a low-code or no-code platform work for your application modernisation scenario? Here's what you need to know.
5 best practices for software development partnerships
Partnerships can accelerate technological innovation in agile, devops, and data science. Just make sure you start with a strong foundation in place.
5 priorities that cut cloud costs and improve IT ops
With infrastructure as code, virtual desktop infrastructure, and a proactive approach to incident management, you can help keep cloud costs reasonable.
How to explain machine learning to business execs
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.
Why observability in dataops?
Because building reliable data pipelines is hard, and the first step to becoming a data-driven organisation is trusting your data.
Best practices for devops observability
Implement observability in strategic areas of the software development life cycle, especially with complicated microservices and cloud-native apps.
What to do when devops teams are downsized
Sadly, there’s a chance that your organisation might have to downsize because of an economic downturn or other financial conditions.
