You may remember we released a report from all the data collected about using LLMs in production.
I am currently creating the questions for the next survey. Reach out if you want to help!
Coffee Session
Santona and Roy join us to share their insights building products in the data engineering and ML industry. We talked about everything from navigating strong opinions and personal preferences to the importance of being opinionated about who you are building for.
Here are some gems from the convo:
Scale and Effective Communication: How is building product for internal customers different from external customers? External-facing products need to focus on scalability, whereas internal products should prioritize reliability and availability.
Building a product capable of accommodating future growth is crucial. Feedback mechanisms play a vital role in learning and product improvement, but they are often lacking in internal tools.
Target specific audiences and cater to their needs. Nuff said.
Extensibility and Flexibility: Do not alienate users while building products. Make them extensible.
Offering APIs, SDKs, and hooks enables users to leverage their preferred tools and interfaces, improving efficiency and avoiding the need to force users into unfamiliar interfaces.
Understanding User Needs: Extract pain points and identify the best solutions, either through existing or new technology.
What do we really mean by product-oriented mentality? How are others facing challenges implementing it?
Job of the week Senior PM // Ramp - Responsible for leading a core team of engineers and designers to build products that serve thousands of businesses. Own the technology solutions (algorithms, models, etc) that underpin one or more key business programs.
Orchestrate Your ML Workloads
ML Workload Orchestration continues Wallaroo’s mission to make it much easier to deploy and scale production ML, lower the cost of inferencing significantly, and give AI teams the ability to get to results much faster through a unified operations
center - in the cloud, on-prem, or at the edge.
Etsy works as a vibrant online marketplace for handmade and vintage items. At this in-person meetup in Mexico City we delve into the role of ML Enablement teams driving innovation and success at everyone's favorite place to buy fried chicken necklaces.
Manju and Miguel discuss their use cases, tech stack, and how their teams are organized. They also talk synergy between ML Enablement, Product, and Data Science teams.
Thanks for reading. This issue was written by Demetrios and Mohamed Sadek edited by Jessica Rudd. See you in Slack, Youtube, and podcast land. Oh yeah, and we are also on Twitter if you like chirping birds.
