Why developers like Sematic

What is Sematic?

It is an open-source development toolkit to help Data Scientists and Machine Learning (ML) Engineers prototype and productionize ML pipelines in days not weeks. It is based on experience building ML infrastructure at leading tech companies.

Sematic is a tool in the Machine Learning Tools category of a tech stack.

Sematic is an open source tool with 941 GitHub stars and 62 GitHub forks. Here’s a link to Sematic's open source repository on GitHub

Sematic Integrations

Slack, Grafana, Apache Spark, Google BigQuery, and Amazon Redshift are some of the popular tools that integrate with Sematic. Here's a list of all 8 tools that integrate with Sematic.

Slack

Grafana

Apache Spark

Google BigQuery

Amazon Redshift

Snowflake

dbt

Google Cloud Dataflow

Sematic's Features

Develop and run ML pipelines using native Python functions, no new DSL to learn
Monitor, visualize, and track all inputs and outputs of all pipeline steps in a slick UI
Collaborate with your team to keep the discussion close to the pipeline as opposed to scattered elsewhere
Execute your pipelines locally or in your cloud

Sematic Alternatives & Comparisons

What are some alternatives to Sematic?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

CUDA

A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.

Sematic