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 set up and manage the cluster.
Although Segmind also provides you tools to run your ML Workflows on a Cluster, it uses Native Kubernetes to schedule the workloads. For Deep learning projects, Segmind offers advanced workflow tools specifically built for DL. Other key differences as shared below make Segmind a modern data platform for ML teams.
Segmind cloud platform is built to meet the unique challenges of operationalizing deep learning at scale. For engineers and scientists, it automates infrastructure and workflows. For teams and managers, it provides real-time project visibility and accountability and enhances collaboration. And for companies, it reduces costs and shortens the time to value.
Supported languages for development
Optimized for Python
Scala, R, Python, SQL
Big Data Processing
Scaling up pre-procssing and training jobs
Native DL Containers
Use of docker containers to run jobs and training sessions
Templates for models
Training ready containers for different models
Platform charges including cloud resources cost
1.1x to 1.2x
1.8x - 2x
Open Source Integrations
Support for OSS frameworks and libraries (TF, PyTorch, CUDA)
A vast range of OSS libraries
Integrated Dev Environments
JupyterLab, VS Code
Jupyter Notebook with no customizations
Deploy a ML model to production
Optimize and Deploy*
Deploy via ML Flow
Segmind focuses only on one langauge: Python. This way you get a seamless experience while using python for your training sessions and other jobs. Segmind supports all the latest versions of Python 3.x and natively supports Tensorflow, PyTorch and other deep learning libraries via docker containers.
Databricks is Apache Spark only. While Apache Spark is a powerful framework to process on a cluster, the 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 very minimal code change. Segmind will support Dask clusters soon.
Segmind containerizes every training session and job under the hood. This helps move the environments between different types of compute required during the course of developing a model including CPU and GPU resources. Containerization also helps improve task repeatability and eventually automate processes. Segmind comes with over 100 templates in computer vision, natural language processing (NLP) and genomics use cases.
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 a modified version of Jupyter Notebooks interface with restricted feature access.
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.
Databricks: Good for ML development with restricted environments and focus on big data using Apache Spark.
Segmind: Deep learning development on flexible python environments with a focus on rapid development and deployment.