Visionary leadership is widely seen as key to strategic change. That’s because visionary leadership does not just set the strategic direction — it tells a story about why the change is worth pursuing and inspires people to embrace the change. Not surprisingly, then, science and practice have a very positive view of visionary leadership as a critical leadership competency.

But our research finds that the positive impact of visionary leadership breaks down when middle managers aren’t aligned with top management’s strategic vision. This can cause strategic change efforts to slow down or even fail.

Organizations of all kinds have long struggled to accurately measure the performance of individual members. The typical approach is to assess an individual’s performance against a metric usually tied to whether or not they performed a task and the amount of output they generated by doing so. There’s a lot riding on these assessments: everything from compensation increases and bonus payments to promotions. And as anyone who has ever given or received a traditional performance review knows, this process can be highly subjective — even in the most metrics-obsessed organizations.

But what about the kinds of jobs where measuring someone’s “output” isn’t about counting the number of widgets they produced, but rather it’s about how they managed a team or influenced others or helped people collaborate better? While it might be easy to measure someone’s output on an assembly line, how do we decide how well a manager manages or a leader leads?

In recent years, the real-world impact of machine learning (ML) has grown in leaps and bounds. In large part, this is due to the advent of deep learning models, which allow practitioners to get state-of-the-art scores on benchmark datasets without any hand-engineered features. Given the availability of multiple open-source ML frameworks like TensorFlow and PyTorch, and an abundance of available state-of-the-art models, it can be argued that high-quality ML models are almost a commoditized resource now. There is a hidden catch, however: the reliance of these models on massive sets of hand-labeled training data.

Most managers and agile coaches depend on metrics over feedback from their teams, users, and even customers. In fact, quite a few use feedback and metrics synonymously, where they present feedback from teams or customers as a bunch of numbers or a graphical representation of those numbers. This is not only unfortunate, but it can be misleading as it presents only part of the story and not the entire truth.

When it comes to two critical factors—how we manage or guide our teams and how we operate and influence the product that our teams are developing—few exceptional leaders and teams get it right." (

AWS is one of the most popular cloud computing services. AWS is short for Amazon Web Services and boy, they have lots of services.

Lots of application we use every day are hosted on AWS. They include Netflix, Airbnb, Slack to name a few.

Maybe you’re working in a company that uses AWS and you want to know more about it. Or maybe you want to deploy your application to AWS. Or you’re just heard AWS somewhere and can’t get it out of your head. Anyways, here’s what you’ll need to know." (