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Evidently AI VS JarvisLabs.ai

Compare Evidently AI VS JarvisLabs.ai and see what are their differences

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Evidently AI logo Evidently AI

Open-source monitoring for machine learning models

JarvisLabs.ai logo JarvisLabs.ai

Let's make AI simple
  • Evidently AI Landing page
    Landing page //
    2023-08-19
  • JarvisLabs.ai Landing page
    Landing page //
    2023-09-05

At JarvisLabs, our mission is to democratize the power of AI by providing accessible and innovative solutions to all. You can select any latest frameworks or train and deploy AI, ML and DL models in few clicks. Say goodbye to the traditional barriers

Evidently AI videos

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How to Monitor Machine Learning Models (Evidently AI)

JarvisLabs.ai videos

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How to use Jarvislabs.ai

More videos:

  • Review - Rent GPU using JarvisLabs.ai for your AI, ML and DL workloads.

Category Popularity

0-100% (relative to Evidently AI and JarvisLabs.ai)

Evidently AI

JarvisLabs.ai

AI
71 71%
AI
29% 29
Developer Tools
81 81%
Developer Tools
19% 19
Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Analytics
56 56%
Analytics
44% 44

User comments

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Social recommendations and mentions

Based on our record, JarvisLabs.ai should be more popular than Evidently AI. It has been mentiond 5 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Evidently AI mentions (2)

  • [D] Using MLFlow for model performance tracking
    It is doable. However the main focus of MLFlow is in experiment tracking. I would suggest for you to look into another monitoring tools such evidentlyai . You can track more things than performance (e.g.data drift). Which may be helpful in a production setting. Source: over 1 year ago
  • Five Data Quality Tools You Should Know
    Evidently is an open-source Python library that analyzes and monitors machine learning models. It generates interactive reports based on Panda DataFrames and CSV files for troubleshooting models and checking data integrity. These reports show model health, data drift, target drift, data integrity, feature analysis, and performance by segment. - Source: dev.to / over 2 years ago

JarvisLabs.ai mentions (5)

  • How to pay for rundiffusion
    Try https://jarvislabs.ai if rundiffusion doesn't work for you. Its cheaper. Source: about 1 year ago
  • [D] Training a 65b LLaMA model
    Someone also mentioned https://jarvislabs.ai/ to me the other day, haven't used it myself but it looks promising. Source: about 1 year ago
  • jarvislabs.ai GPU Rental (Not sponsored, personal recommendation)
    Jarvislabs.ai is a cloud platform where you can rent GPU's, I've had a problem when I first tried their platform, and they helped me instantly on Google Meet and solved my problem. They're a start-up based in India and they told me they have their GPU's in-house. Source: about 1 year ago
  • Which cloud environment do you recommend for AI projects based on GPU-dependent deep learning?
    Checkout https://jarvislabs.ai/. Been using them for a good 6 months, their GPU instances are way cheaper than AWS/azure/gcp and it's perfect for running hundreds of modeling experiments. It's practically an extension of your local jupyterlab to a server jupyterlab. Customer support is great too. Source: about 1 year ago
  • [D] GPU access without limit increases
    Tried that, didn't work... It's an account wide limit on GPU instances... Seems to be consistent across all cloud providers except lambda labs/indie guys like jarvislabs.ai; using indie now. But, very strange why this happens. Source: over 2 years ago

What are some alternatives?

When comparing Evidently AI and JarvisLabs.ai, you can also consider the following products

ML5.js - Friendly machine learning for the web

Vast.ai - GPU Sharing Economy: One simple interface to find the best cloud GPU rentals.

ML Showcase - A curated collection of machine learning projects

iko.ai - Real-time collaborative notebooks on your own Kubernetes clusters to train, track, package, deploy, and monitor your machine learning models.

Lobe - Visual tool for building custom deep learning models

Censius.ai - Building the future of MLOps

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