TeachableHub is a fully-managed platform bringing ML teams together to deploy, serve, and share impactful models as public or private APIs(a.k.a. Teachables) with ZERO MLOps, seamless scalability, and no costly infrastructure.
Created to make a difference, inspired by the open community spirit.
Teachable - [/ˈtiːtʃəb(ə)l/] - noun - A powerful machine learning model deployed as a REST, gRPC or GraphQL API, entirely documented, available to be consumed by any server-side or client-side applications. It can be published privately or shared in public for the community's benefit.
You create meaningful models, we give you powerful tools and infra to deploy and host them.
Together we change the future!
Connect your model from anywhere, using any framework, deploy it as an API & share it with the world. Versioning, model validation, and more available out of the box.
Securely serve & instantly scale Teachables on our fully-managed serverless infrastructure. Forget about MLOps tasks, underutilized resources, and infra costs.
Monitor, manage, and control model usage and performance in production, set ACLs & permissions - all from a centralized hub side by side with your entire ML team.
For each model, you get a fully documented & ready to use API you can easily connect with any web, mobile, commerce, or backend platform via our modern SDKs.
Deploy & Share models freely as public APIs, infra costs are on us!
Our solution is available as a Managed Cloud Service or an On-Premise version
suitable for any infrastructure supporting Kubernetes.
Deploy & share models freely as public APIs, infra costs are on us!
Ideal for innovators, freelancers, university practitioners, students, and visionaries building open projects.
Ideal for teams of all sizes, app agencies, enterprises organizations, and professionals working on projects at scale.
Here's what Machine Learning practitioners have to say about our product.
Co-founder & CTO @ Kelvin Health | Head of AI @ Imagga
"This demo forever changed my views regarding the model deployment process.
TeachableHub is making the deployment of complex ML models a simple task involving a few lines of code. No need to be a DevOps or MLOps expert anymore. It focuses entirely on teamwork & collaboration, giving the ability to easily involve different stakeholders in the process."
Machine Learning Engineering Senior Manager @ Accenture
"A great point on UX simplicity, ML Engineers tend to over-engineer front-end.
It's really difficult to bring simplicity to a complex solution without loosing perspective, so, congrats!
Rolling update release workflow was really easy to set, it works seamlessly.
The modular architecture designed for the ML lifecycle is another point for the team."
Data Science, Computer Vision, MLOps @ NTT Disruption
"TeachableHub is a platform that tracks every version of your model, cleverly organized in environments. Every version comes with automatic documentation and metadata so you can check where the model came from. With a few lines of code you can upload your models from wherever you are. And with every upload, a new API endpoint is created that you can beautifully test from an integrated Postman. Once you're comfortable with the model, you can easily promote it."Read full review
Co-founder & Chief Technology Officer @ WeOne
"TeachableHub is early stage but have a mature feature set: multi-environment, blue/green deploys, model versioning, automatic model documentation generation, seamless autoscaling, etc. If you look for a solution to free your Data Science team from writing Dockerfiles and Ops you might check it."Read full review
Machine Learning Operations Engineer @ Retail AI, Inc
"I'm currently using Kubeflow pipelines integrated on GCP for most of my MLOps endeavours, but after looking at the deployment platform from the TeachableHub team, it really streamlines the MLE process and serving the models, especially for Data Scientists."Read full review
MLOps Engineer @ Neu.ro Inc
"Very clear scope: deploying ML models. Let's all focus on one thing! Neat UX: the team seems to have found the balance between flexibility (hackability) and strict declarative configuration. Let's all think of our users! Modular architecture: not just the APIs of the platform are designed with easy integrations in mind, the team always try to utilise third-party tools firs. Let's all stop reinventing the wheel!"Read full review
Product Management & Business Growth @ Neurocat.ai
"Recently I had a chance to view a product presentation of TeachableHub!
I was impressed by the product vision and storytelling behind the product.
If you are in the MLOps space, this is worth a checkout!"
Machine Learning Engineer @ MLmargin | Co-founder @ KG Intelligence
"It was eye opening to see how TeachableHub approaches MLOps...They are focusing on providing state of the art, easy to use Machine Learning Models deployment experience, which unties the hands of the software developers to focus their energy on other challenges.
Looking forward to work with TeachableHub!"
and get exclusive early access to TeachableHub!