Katonic MLOps Platform delivers the following AI/ML functionality

• Spins up distributed, scalable, machine learning, and deep learning training environments in minutes rather than months — on-premises, public cloud, or in a hybrid model.

• Leverages the power of containers to create a complex machine learning and deep learning stacks that include distributed TensorFlow, Apache Spark, and Python ML and DL toolkits.

• Enables a choice of programming languages and open-source tools to support even the most complex ML pipelines. For example, start with data pre-processing in Spark with Scala, followed by model development with TensorFlow on GPUs, and finally model deployment on CPUs with TensorFlow runtime.

• Implements CI/CD processes for your ML projects with a model registry. Model registry stores models and versions created within Katonic ML Ops Platform.

• Improves the reliability and reproducibility of machine learning projects a shared source control repository (GitHub & BitBucket).

• Enables the deployment of models in production with secure, scalable, highly available endpoint deployment with out-of-the-box auto-scaling and load balancing.

• Allows data scientists to focus on the core task of building ML models to improve business outcomes rather than managing infrastructure.

• Enables Out-of-the-box application images to rapidly deploy containerized environments – sandbox, distributed training, or serving (inferencing) — with popular ML and DL tools, interfaces and languages. Such as Python, R-Studio, TensorFlow, Spark, and more.

• Enables the creation of custom application images with any combination of tools, library packages, and frameworks to suit your needs.