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Learn ML and AI infrastructure in the browser

Hacker News

Learn ML and AI infrastructure in the browser

We are the developers of an open-source package Metaflow that we started at Netflix. Metaflow provides a human-friendly interface to the full stack of ML infrastructure, including data access, compute, workflow orchestration, and versioning. It is used by hundreds of companies across industries. Over the past years, we have seen that there are two major stumbling blocks for folks who want to learn to build real-world ML applications: 1) Setting up the full infrastructure stack in the cloud costs time and money. The investment is worth it once you know what you want to do, but that's not always the case in the beginning when you just want to explore. 2) While many excellent ML/AI tutorials exist publicly, there aren't that many hands-on, interactive environments showing how to map models to real-world workflows, running on real infrastructure. This Show HN release is about our attempt to address (1) and (2): We host free, fully-featured sandbox environments that include a private Kubernetes cluster, metadata service and UI, and a workflow orchestrator backed by Argo Workflows (1). If you like what you see, you can set up a similar environment in your cloud account using our open-source Terraform templates. In addition, leaning on industry experts, we have created a number of tutorials covering NLP, computer vision, OpenAI Whisper, and recommendation systems (2). Hopefully you'll find these resources useful. Let us know what you think!

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, computer, models · Missing: mac, agents, macos
91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, including · Missing: supports, reddit linkedin, podcasting
82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released
81%81% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host, friendly, interface · Missing: plus, platform, intuitive
32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue
14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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