Introducing Missinglink.ai, a platform for accelerating Deep Learning Lifecycle

Yesterday, MissingLink.ai launched as a platform with a purpose to accelerate deep learning progress.

Machine learning has shown immense promise across many use cases, from helping to diagnose diseases to powering autonomous vehicles, for a long time, but the actual process of delivering business outcomes currently takes too long and it is too expensive.

MissingLink.ai was born out of a desire to fix that, say the MissingLink team. It enables data scientists to spend less time on the grunt work by automating and streamlining the entire deep learning cycle, including data, code, experiments, and resources. It automates repetitive, time-consuming tasks, shortening model training times, and accelerating learning cycles. This way data scientists can get more time to apply the actionable insights the data provides.

Here are it’s top features:

The system uses version-aware data management eliminating the need to copy files and only syncs changes to data.

It offers real-time monitoring and tracking of experiments via visual dashboards. It also automatically tracks data, experiments, and code, so engineers can easily reproduce any experiment at any time.

Users can run experiments on a training machine without the need to copy or move data. Once the data is integrated, MissingLink can manage it on a machine on premise.

MissingLink.ai allows teams to manage both local and public cloud resources as a single environment, allowing them to grow and shrink compute resources elastically as needed.