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One-Click MLOps Platform Sandbox with ZenML, MLflow, Kubeflow and MinIO

Hacker News

One-Click MLOps Platform Sandbox with ZenML, MLflow, Kubeflow and MinIO

Founder of ZenML here, the company behind the sandbox service. We created the MLOps Platform Sandbox to give ML and MLOps Engineers the ability to pick and choose their preferred infrastructure and tooling to build a platform that fulfills their company’s needs. Deploying a rudimentary MLOps platform can be time-consuming for new users. The MLOps Platform Sandbox bridges this gap by providing a one-click deployment platform for a pre-built ephemeral MLOps stack, simplifying the deployment process. For now, you can create a sandbox for 8 hours with a stack made up of ZenML, Kubeflow, MLflow, and Minio Bucket. We plan to expand it to other services like BentoML, Seldon, and other MLOps tools in the future. The Sandbox comes with pre-built example pipelines that you can run and try out. It provides a seamless experience for you to experiment with these tools without worrying about infrastructure setup and management. Looking forward to the HN's communitys feedback!

Visit productView on Hacker NewsJun 6, 2023
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6points
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
83%83% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, new · Missing: mac, agents, macos
68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: ide, pipe, io · Missing: https docs, excited, just released
64%64% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews
56%56% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% 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.

Incorrect prediction on native model

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