Building Scalable ML Infrastructure at Outerbounds with Savin Goyal

Machine learning is changing fast, and companies need better tools to handle AI workloads. The right infrastructure helps data scientists focus on solving problems instead of managing complex systems. In this episode, we talk with Savin Goyal, Co-Founder and CTO at Outerbounds, about building ML infrastructure, how orchestration makes workflows easier and how Metaflow and Airflow work together to simplify data science. 

Key Takeaways:

(02:02) Savin spent years building AI and ML infrastructure, including at Netflix.

(04:05) ML engineering was not a defined role a decade ago.

(08:17) Modernizing AI and ML requires balancing new tools with existing strengths.

(10:28) ML workloads can be long-running or require heavy computation.

(15:29) Different teams at Netflix used multiple orchestration systems for specific needs.

(20:10) Stable APIs prevent rework and keep projects moving.

(21:07) Metaflow simplifies ML workflows by optimizing data and compute interactions.

(25:53) Limited local computing power makes running ML workloads challenging.

(27:43) Airflow UI monitors pipelines, while Metaflow UI gives ML insights.

(33:13) The most successful data professionals focus on business impact, not just technology.

Resources Mentioned:

Savin Goyal -

https://www.linkedin.com/in/savingoyal/

Outerbounds -

https://www.linkedin.com/company/outerbounds/

Apache Airflow -

https://airflow.apache.org/

Metaflow -

https://metaflow.org/

Netflix’s Maestro Orchestration System -

https://netflixtechblog.com/maestro-netflixs-workflow-orchestrator-ee13a06f9c78?gi=8e6a067a92e9#:~:text=Maestro%20is%20a%20fully%20managed,data%20between%20different%20storages%2C%20etc.

TensorFlow -

https://www.tensorflow.org/