What is Seldon and what are its top alternatives?
Top Alternatives to Seldon
KubeflowThe Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. ...
MLflowMLflow is an open source platform for managing the end-to-end machine learning lifecycle. ...
PredictionIOPredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. ...
ClearBrainClearBrain identifies which of your users will upgrade or churn before they do so. We build and retarget look-alikes for your users in minutes, without a single line of code. ...
Obviously AIIt started on the belief that business users should get insights from their data without waiting on engineers. Our mission is to make data science effortless by enabling anyone to run complex predictions and analytics, simply by asking questions ...
Peoplelogic.aiPeoplelogic.ai is mission control for your teams. It keeps an eye on the health of your company 24/7/365—so you can focus your time where it really counts. ...
AllstacksIt is the leading forecasting and risk management tool for software development. We offer more than just metrics; we’re a partner that enables better business outcomes. Our holistic solution helps organizations track and improve their software delivery. ...
Framed DataFramed helps you retain subscribers by understanding their behavior. Do you know why your users are leaving? Do you know why your users love your app? Framed does. Our automated predictive analytics platform runs a battery of machine learning models on your data to predict future user behavior. Know the exact point at which a user decides to stay with your product. ...
Seldon alternatives & related posts
- System designer9
- Google backed3
- Customisation3
- Kfp dsl3
- Azure0
related Kubeflow posts
Can you please advise which one to choose FastText Or Gensim, in terms of:
- Operability with ML Ops tools such as MLflow, Kubeflow, etc.
- Performance
- Customization of Intermediate steps
- FastText and Gensim both have the same underlying libraries
- Use cases each one tries to solve
- Unsupervised Vs Supervised dimensions
- Ease of Use.
Please mention any other points that I may have missed here.
We are trying to standardise DevOps across both ML (model selection and deployment) and regular software. Want to minimise the number of tools we have to learn. Also want a scalable solution which is easy enough to start small - eg. on a powerful laptop and eventually be deployed at scale. MLflow vs Kubernetes (Kubeflow)?
- Code First5
- Simplified Logging4
related MLflow posts
I already use DVC to keep track and store my datasets in my machine learning pipeline. I have also started to use MLflow to keep track of my experiments. However, I still don't know whether to use DVC for my model files or I use the MLflow artifact store for this purpose. Or maybe these two serve different purposes, and it may be good to do both! Can anyone help, please?
Can you please advise which one to choose FastText Or Gensim, in terms of:
- Operability with ML Ops tools such as MLflow, Kubeflow, etc.
- Performance
- Customization of Intermediate steps
- FastText and Gensim both have the same underlying libraries
- Use cases each one tries to solve
- Unsupervised Vs Supervised dimensions
- Ease of Use.
Please mention any other points that I may have missed here.
- Predict Future8
related PredictionIO posts
related ClearBrain posts
related Obviously AI posts
related Peoplelogic.ai posts
related Allstacks posts