AI News, Introducing Seldon Deploy

Introducing Seldon Deploy

Seldon’s mission is to help people predict and shape the future with machine learning.

We’re firm believers that machine learning is the enabling technology driving this AI renaissance, as it helps people solve problems of all shapes and sizes and enriches all of our lives.

It’s designed to streamline the data science workflow, with audit trails, advanced experiments, continuous integration and deployment, rolling updates, scaling, model explanations, and more — all served through a delicious UI.

This experience helped us gain vast amounts of product feedback from a broad range of stakeholders, and sign customers in the banking and financial services space.

In a survey from August 2016 we learned that stakeholders were prioritising on average 4.5 use cases, the most popular include customer segmentation (48%), content recommendation (36%) and churn prediction (34%).

One of our biggest decisions last year was whether to focus on some of these vertical use cases or continue to build a foundational technology that operates on a generalised and horizontal basis.

Companies can build a defensible moat by combining deep domain expertise and the data network effect that improves the service itself as more data is fed into it — in terms of accuracy and performance of the model.

Another significant trend is the increase in the quality and availability of machine learning and deep learning model building tools and frameworks, such as TensorFlow, Keras and new entrants like PyTorch and Caffe 2.

Data science is like agile software development: effective organisations don’t enforce specific programming languages, teams have the freedom and autonomy to use the best tools for the job and connect with the outputs from other teams via APIs and microservices.

I would go further and say solving deployment means creating an entirely new category in machine learning that sits on the intersection of data science and ops.

Seldon Deploy will provide a fully integrated deployment workflow via a delicious new UI that supports the following team members: Out of the above roles, Seldon Deploy focuses mostly on productising the function of a data engineer, the role for which there is the largest skills shortage in industry.

We’ve taken a view on how the above data science team should map to the features, but we appreciate all teams are different and have made the roles-based permission system customisable on a granular level.

It’s easy to traverse the decision tree generated by a random forest algorithm, but the connections between the nodes and layers of a neural network model are beyond human comprehension.

It’s used by a global community of data scientists across industry and academia to deploy machine learning and deep learning models into production on-premise or in the cloud (e.g.

Artificial intelligence. Real impact.

We are currently building a new enterprise product that helps data science teams move from R&D into production faster with more transparency and better compliance.

Seldon Deploy streamlines the data science workflow, with audit trails and approvals, advanced experiments, model explanations, and more — all served through a stunning UI.

Introducing Seldon Core — Machine Learning Deployment for Kubernetes

Seldon Core focuses on solving the last step in any machine learning project to help companies put models into production, to solve real-world problems and maximise the return on investment.

These graphs can be composed of: Efficiency — traditional infrastructure stacks and devops processes don’t translate well to machine learning, and there is limited open-source innovation in this space, which forces companies to build their own at great expense or to use a proprietary service.

You can get started quickly by installing the official release via Helm: We’ve designed a Kubernetes Custom Resource for a Seldon Deployment, which means you can manage deployments directly with kubectl instead of having to learn a new CLI: Our team of data scientists and engineers at Seldon are building and managing the Seldon Core project directly on Github, not in a private company repo and project management systems, so expect to see lots of activity from us.

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