One of the most interesting areas of innovation in machine learning and artificial intelligence is Natural Language Processing or NLP. It is basically about teaching machines to both understand human language, and then reproduce convincingly human language in return

Our guest on this episode and her company build what they call “machines that can read and write”.

Amy Heineike is the VP of Engineering at Primar AI. I talked to her about how she, and her team, are using applied machine learning techniques to build software that tells stories from data. In particular, we talked about “Quicksilver” - a project to fill some gaps in Wikipedia by producing new content for people without pages.

Machines that tell stories with Data (Guest: Amy Heineike)

We are talking to the people that build, run and secure the digital future. Who are the people bringing the new and the next? You heard it heard here first. Welcome to the new Masters of Data podcast.

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Masters of Data is back! We have a new host and a new mission. On today's episode Sumo Logic's Chief Communication Officer, Aaron Feign, speaks with Dana Torgersen about goals for the podcast. 
 Highlights of the conversation include: Talking about bringing back Masters of DataChallenges the podcast wants to addressTransformation and accelerating the adoption of technologies to battle disruptions like COVIDAlert fatigue and new technologies solving problems in securityThinking horizontally across different lines of an organization's business


The Masters of Data Podcast is back!

Masters of Data is back! We have a new host and a new mission. On today's episode Sumo Logic's Chief Communication Officer, Aaron Feign, speaks with Dana Torgersen about goals for the podcast. 
 Highlights of the conversation include: Talking about bringing back Masters of DataChallenges the podcast wants to addressTransformation and accelerating the adoption of technologies to battle disruptions like COVIDAlert fatigue and new technologies solving problems in securityThinking horizontally across different lines of an organization's business

The Masters of Data is Back!

Everything in modern tech — including observability — is actually a culture change. Observability is a different way of working, thinking, and composing a team. It is essentially a love letter to the future, helping the people who will support the application later. Instead of an operator sitting back and looking at a dashboard, observability tackles what can be done during the process of developing an application so that production can feel more comfortable. In James Governor’s word, “observability is about troubleshooting.”

James Governor is the co-founder of RedMonk, a developer-focused industry analyst company. After working as a tech journalist, James saw room for a new research firm with a different focus and thus RedMonk was born. Listen to this week’s episode to hear James and Ben discuss the generational shift in technology building, and learn more about how observability can help developers.

This week’s episode is the last installment of the special three-part series on observability in data. Thank you for joining us!

James Governor - The Culture Change Observability Caused in Modern Tech (Observability Series - Part 3)

There are three types of people in the data world: mathematicians, scientists, and engineers. Mathematicians are interested in understanding things that are true or false. Scientists are interested in furthering knowledge and enjoy answering challenging questions. Engineers are interested in building things that are useful, so they can solve a problem that’s important. Engineers in the software industry are currently searching for ways to resolve the issues associated with microservices. Right now, the software industry is facing a massive architectural transformation, and engineers have the opportunity to create systems that solve important problems. 

That’s why Ben Sigelman — CEO and co-founder, started Lightstep, to create something useful and impactful. He saw an opportunity to accelerate the industry’s transformation while improving the developer and end-user experience, and he took it. Using observability, he built something that could help people gain more confidence and understanding of their own system.

As an ex-Googler and co-creator of Dapper, Ben Sigelman witnessed the birth of microservices at Google. He learned a great deal from his experiences, and Lightstep is in many ways a reaction to and a generational improvement beyond those approaches. Sigelman’s fascination lies in deep systems and how they break, but he is also passionate about separating the telemetry from the rest of observability. There is a lot of noise in the marketplace and confusion about how to approach observability, but Sigelman is confident that in the next 5-10 years, applications could change the way the software actually works, not just the way we understand it. Listen to Ben Sigelman and Ben Newton discuss the future of observability, and learn more about how this transformation could impact the industry.

This week’s episode is the second installment of a special three-part series on observability in data. Tune in each week to hear about how the world of observability in transforming into a major player in the data realm.

Ben Sigelman - The Future Of Observability & Why It's Not Just Telemetry (Observability Series - Part 2)

Observability doesn’t have three pillars, and it is not a monitoring tool. While the concept of observability is often misunderstood, what it can do for ops is revolutionary and transformative. Observability is achieved when a system is understandable — which is difficult with many things failing at once in a complex system. Many large technology companies use systems to debug code and understand how it runs in production, but those tools are not available to engineers outside of those companies. That’s why Charity Majors — CTO and co-founder, started Honeycomb, to tackle the issues with logging, monitoring, and metrics. Despite the confusion surrounding observability, it can help us ask the right questions of our systems in a way that is predictable, fast, and scalable over time. Utilizing observability to fully understand complicated production systems makes the systems more resilient to errors. Listen to Charity and Ben discuss the innovations and revelations of observability, and learn more about this transformational tool in data. Say goodbye to spending all your time on debugging, and get ahead of those issues now.

This week’s episode is the first of a special three-part series on observability in data. Tune in each week to hear about how the world of observability in transforming from a major player in the data realm.

Machines that tell stories with Data (Guest: Amy Heineike)

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