Armon Dadgar (@armon), HashiCorp CTO and co-founder joins Aaron Schlesinger (@arschles) to school him on all things service meshes. You’ll understand what a service mesh actually does, when and why it makes sense to use them, the role of observability, and the differences between data planes and control planes (and what’s relevant to app developers).

 Armon makes concepts real with specific examples and analogies, Aaron sees how to easily apply it to his favorite project (Kubernetes, of course) and they sign off with their favorite resources, so you can apply to your apps. 

Here’s a riveting documentary from the BBC about Ada Lovelace, the “Countess of Computing.” It’s a fascinating look at the contradicting world views of her parents and how their subsequent separation helped form a young Ada into a mathematical superstar. It’s also offers a glimpse of the Victorian Computer Age (SteamPunk?) we almost got.

Learn about how to send a message to your Microsoft Teams channel when a rule is fired in your IoT Central app using Microsoft Flow. We’ll cover what is Microsoft Flow, and go through how to build workflows easily using the hundreds of connectors available.


Get more details about the Microsoft Flow connector: https://docs.microsoft.com/en-us/azure/iot-central/howto-add-microsoft-flow
Create a Free Account (Azure): https://aka.ms/aft-iot

I’m often asked where’s the best place to get started in data science and AI. My answer is almost always the same: statistics. Statistics is the bedrock of data science and it’s a core pillar of AI. You could make the argument that statistics make up the core for understanding reality itself, but I have not had enough coffee yet to engage in such philosophical banter.

Here’s a great video on Probability vs. Likelihood. In common conversation we tend to use these words interchangeably. However, statisticians make a clear distinction that is important to understand if you want to follow their logic. Like most of statistics, they are both super simple and easy to get mixed up. F

In Part 3 of this mini-series on TensorFlow high-level APIs, TensorFlow Engineering Manager Karmel Allison runs us through different scenarios using TensorFlow’s high-level APIs.

With the TensorFlow model defined, Karmel discusses how to establish layer architecture, and compile the model, adding the optimizer, loss, and metrics that we are interested in. She also runs through the various steps taken to refine the model.

At this vertical farm startup, Bowery Farming proprietary software that makes most of the critical decisions — like when to harvest and how much to water each plant.

But (for now) it still takes humans to carry out many tasks around the farm. Katie Morich, 25, loves the work. But as roboticists make gains, will her employer need her forever? This is the fourth episode of Next Jobs, a series about careers of the future hosted by Bloomberg Technology’s Aki Ito.