“Obviously, the highest type of efficiency is that which can utilize existing material to the best advantage”
We deliver solutions that consumes cutting edge technologies from the open source community deployed on top cloud providers to minimize cost and eliminates vendor locking.
Python is one of the most popular programming languages used by developers today. In our endeavour to identify what is the best programming language for AI and neural network, Python has taken a big lead
TensorFlow is an open source library for numerical computation and large-scale machine learning, created by the Google Brain team. In addition it supports deep learning models and algorithms.
PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing. It is primarily developed by Facebook’s artificial-intelligence research group.
Apache Beam is an open source unified programming model to define and execute data processing pipelines, including ETL, batch and stream processing
Apache Spark is an open-source distributed general-purpose cluster-computing framework. Originally developed at the University of California, Berkeley’s AMPLab.
Kubeflow project is a tool making deployments of ML workflows on Kubernetes simple, portable and scalable therefore deploying open-source ML to diverse infrastructure.
Google Cloud Platform is a managed service that enables developers and data scientists to build and bring ML models to production. Cloud ML Engine offers training and prediction services, which can be used together or individually.
Amazon Web Services we use to deploy ML models to scalable and powerful GPU powered infrastructure. Amazon SageMaker enables us to quickly and easily build, train, and deploy machine learning models at any scale.
Azure Machine Learning service provides a cloud-based environment we use to prep data, train, test, deploy, manage, and track machine learning models. It supports Open Source technologies besides Microsoft’s.