Trustworthy Data

We've all heard or lived the following phrase: It's all fun and games until you need to put the model into production...

This week we had Ale Solano, who talked with us about his transition from a Data Scientist to MLOps and now to Software Engineering. He guided us through how he went from being that Data Scientist getting crushed by not being able to use the models he created to taking a hands on approach to learn MLOps. To make it even better, he provided a clear example of what is expected on the current job market vs. the years 2015-2020.

We loved his attitude and learning approach.

Hope you guys enjoy as much we did listening to Ale!

The insurtech company Lemonade is one of the strongest engineering cultures in the world. The Tel Aviv-based company is renowned for its efficiency and ability to drive real user and product impact with excellent technical architecture and engineering. Wouldn’t it be cool to hear about how Lemonade runs machine learning?

We. Did. That.

This week, we had on Orr Shillon, the ML Platform Lead at Lemonade, who told us about how machine learning engineering happens at Lemonade and the amazing impact their platform and approach has made.

This episode is the epitome of why people listen to our podcast. It’s a complete discussion of the technical, organizational, and cultural challenges of building a high-velocity, machine learning platform that impacts core business outcomes. Orr told us about the focus on automation and platform thinking that’s uniquely allowed Lemonade’s engineers to make long-term investments that have paid off in terms of efficiency. He told us the crazy story of how the entire data science team of 20+ people was supported by only two ML engineers at one point, demonstrating the leverage their technical strategy has given engineers.

Every ML engineer thinking about higher-level platform development should listen to this episode to understand what happens when you do with a principled, farsighted approach. There’s so much more to dig into here, go listen!

Till next time,
Vishnu

In his previous guide, Lloyd explored the concepts and methods in deploying machine learning model on AWS Elastic Beanstalk. Despite being largely automated, services like AWS Elastic Beanstalk still requires the deployment of key services such as EC2 instances and Elastic Load Balancers. Provisioned resources on AWS Elastic Beanstalk are always active, even when not required.

The concept of serverless orchestration of code moves away from traditional implementation of cloud computing resources by eliminating infrastructure management tasks. Serverless cloud computing is an evolution of the hands free approach to infrastructure management offered on Elastic Beanstalk but without the provisioning or management of servers.

Engineering Labs are back!

Compared to previous editions, we change a bit the format of the initiative.
We decided to conduct this new round in collaboration with Flyte, the open-source project team which builds an MLOps platform trusted by companies such as Lyft, Spotify, Freenome, and others.

As member of the community, start building your team to join and stay tuned on the #engineeringlabs channel. With @Demetrios and Flyte team, we are going to provide all details in the coming premier!

P.S. Big shoutout to @IlNardo92 for putting this together.