Segmind Vs Databricks

Databricks is a high level platform for data science projects. This Apache-Spark based platform allows building and scaling data pipelines without having to setup and manage the cluster.

Although Segmind also provides you tools to run your ML Workflows on a Cluster, it uses Native K8s to schedule the workloads. For Deep learning projects, Segmind offers advance workflow tools specifically built for DL. Other key diffrences as shared below make Segmind a modern data platform for ML teams.

Segmind is built for Deep Learning

Segmind is the MLOps platform built to meet the unique challenges of operationalizing deep learning at scale. For engineers, it automates infrastructure and workflows. For teams and managers, it provides real-time project visibility, accountability and enhances collaboration. And for companies, it reduces cost and shortens time to value.

Databricks is Apache Spark only

While Apache Spark is a powerful framework to process on a cluster, learning curve for Apache Spark is steep. Since Segmind runs on K8s, it can provide native Tensorflow and PyTorch distributed training, even on multiple GPU nodes. Dask is another library that allows Pandas and NumPy code to directly run on clusters with minimal code change. Segmind will support Dask clusters soon.

Multiple IDEs

Segmind offers clean Jupyter Lab and Visual Studio Code IDE to work on your projects. Leverage open extensions to customize the setup. Databricks provides only modified version of Jupyter Notebooks interface with restricted feature access.

Cost monitoring tools

When running a K8s cluster, multiple resources are created all the time. Segmind's cost monitoring setup helps you monitor costs closely. Get project-wise, user-wise and more such reports and insights. Also, leverage integration with Spot and Autoscaler to decrease costs further.

Updated 10 Jan 2022
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