Animesh Singh is an STSM and lead for IBM Watson and Cloud Platform, currently leading Machine Learning and Deep Learning initiatives on IBM Cloud. He has been with IBM for more than a decade and is currently working with communities and customers to design and implement Deep Learning, Machine Learning and Cloud Computing frameworks. He has been leading cutting edge projects for IBM enterprise customers in Telco, Banking, and Healthcare Industries, around cloud and virtualization technologies. He has a proven track record of driving design and implementation of private and public cloud solutions from concept to production. He also led the design and development first IBM public cloud offering, and was the lead architect for Bluemix Local. Find Animesh on Twitter @AnimeshSingh.

Posts from this devCenter

Scalable distributed training using Horovod in FfDL

The latest FfDL release enables support for Uber's Horovod mechanism for distributed deep learning training.

H2O-3 on FfDL: Bringing deep learning and machine learning closer together is the newest member of the FfDL stack, providing an in-memory, distributed, and scalable machine learning and predictive analytics platform.

Serve it hot! Deploy your FfDL trained models using Seldon

With a newly available integration, you can now deploy Fabric for Deep Learning trained models to the Seldon machine learning deployment platform.

Democratize AI with Fabric for Deep Learning (FfDL)

With the Fabric for Deep Learning project, you can add a Deep Learning as a Service offering to your Kubernetes environment, creating a scalable, resilient...

Make your microservices resilient and fault-tolerant using Istio

Learn how Istio health checks, timeouts, and retries can be enabled without requiring changes to your microservice code.

Spring Boot and Kubernetes: A great combination for your microservices!

The Spring Boot framework takes an opinionated view of building Spring applications and is focused on getting you up and running as quickly as possible.

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