[Part 5]
💭 If you're reading this & you've been involved with developing an ML Platform, how did you approach the "centralize vs distributed" discussion? What worked? What failed?

👇 Let me know in the comments below!

#mlops #mlplatform #ai #strategy #dataops #dataengineering #devops #platformengineering #platformdesign #mlengineer

⚡ Startups in the earliest stages are in a really bad spot to be building a separate #MLplatform

👉🏻 Want Future-proofing Architecture BUT Get Resume-Driven Development
👉🏻 Want Greenfield Toolstack BUT Have no money or devs
👉🏻 Try Using #DORA Metrics BUT Don’t Have Standardization On What Matters for #MLOps

The focus for startups should be providing business value & GTM strategy, not a grandiose, vainglorious treatise on disitributed cloud design.

RT @BazeleyMikiko: If you do build an #MLops #MLPlatform, only adopt metrics that:
1. Your team can directly influence;
2. Are appropriate to the level of engagement;
3. Directly measure the behavior you’re trying to capture.

#🐘 #platformengineering https://t.co/BTU2O5W520

If you do build an #MLops #MLPlatform, only adopt metrics that:
1. Your team can directly influence;
2. Are appropriate to the level of engagement;
3. Directly measure the behavior you’re trying to capture.

#🐘 #platformengineering https://t.co/BTU2O5W520

RT @BazeleyMikiko: Don’t build an #MLPlatform unless you're:

✅ Post early-stage startup;
✅ Can centralize ppl

Anyone else feel that #mlops & #mlplatform teams feel a bit complicated, especially when mapping topologies?

Oftentimes it seems that ML platforms & teams are either spun out of tooling teams (or complicated-subsystem teams) or as enabling teams (oftentimes ML engineers) and may occasionally begin to organize as platform teams.

#teamtopologies

Don’t build an #MLPlatform unless you're:

✅ Post early-stage startup;
✅ Can centralize ppl

If the answer is similar to:
1️⃣ ASAP
2️⃣ Minimal
3️⃣ Divorced
4️⃣ We can't
5️⃣ Less than 5

Then your first step shouldn't be building an ML platform, it should be developing models or ML-drive product features using the simplest, tried & true patterns possible.

#mlops #mlplatform #datascience #mlengineering #platformengineering #dataengineering #ai #mlinproduction