Azure Machine Learning
Bring AI to everyone with an end-to-end, scalable, trusted platform with experimentation and model management
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Pros
- Code and designer experience
- Large range of compute options
- Good range of built-in Python frameworks
- Flexible model hosting options
- Automated machine learning (AutoML)
- Support for ONNX
- ML.NET
- MLOps features
- Azure Synapse Pipelines integration
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Cons
- No serverless SKUs
Read our blog posts about Azure Machine Learning
Exposing legacy batch processing code online using Azure Durable Functions, API Management and Kubernetes
In this post we show how a combination of Kubernetes, Azure Durable Functions and Azure API Management can be used to make legacy batch processing code available as a RESTful API. This is a great example of how serverless technologies can be used to expose legacy software to the public internet in a controlled way, allowing you to reap some of the benefits of a cloud first approach without fully rewriting and migrating existing software.
NDC London Day 1
In this post, Ian describes some of the highlights from the NDC London conference
NDC London Day 3 Retrospective - from personal projects to developer comedy
Along with several of my endjin colleagues, I attended NDC London in January this year - here's a run through of the sessions I attended on Day 3 and my thoughts. This final day was a mixed bag, taking in talks on drumming and AKKA.net, as well as something a bit more close to home - a session from endjin's own Jess Panni and Carmel Eve on our recent project for OceanMind.