OVERCOMPLICATED MODEL IMPLEMENTATION PROCESS
Using analytical data for model training and inferencing in real time. The parameter store ensures that the same data is used for the models during training and inference allowing to eliminate weaknesses in the training and implementation process. Koalas, Apache Spark and Apache Airflow make it possible to create universal independent data preparation pipelines.
Pre-configured laptops with data processing tools. Model building environment contains recommended data science tools (e.g. Tensorflow, SciKit-Learn, PyTorch) and provides for integration with data sources and interaction environment - model repository, Git, etc. Many data science and analysis teams work with various processing tools at the same time.
Scalable environment for model training On-demand access to a scalable containerized application platform (from single node to distributed multi-node environment) allows creating high-performance machine learning pipelines.
Flexible, scalable deployment with multiple endpoints. MLOps Center allows creating an embedded runtime image for Python, R, Java ML models with high availability, load balancing, secure implementation and multiple endpoints (e.g. REST, gRPC, Apache Kafka, Apache Spark).
Monitoring of all stages of the ML lifecycle. Ability to collect metrics to implement ML models, create dashboards and reports, and make decisions about training/retraining models.
The containerized application platform enables optimization of training and implementation of the model, and utilization of the available cluster resources. Provides high availability, load balancing, automatic scaling and monitoring of the functioning of ML services.
Local, cloud and hybrid deployment MLOps Center runs in the local environment Kubernetes or on the platform RedHat Open Shift, in public cloud (Amazon Web Services, Google Cloud Platform, Microsoft Azure) or hybrid model to ensure resource efficiency.
Efficient management of the entire
machine learning lifecycle.
Fast innovation implementation due to full control of the machine learning lifecycle.
Resource control and management system for machine learning.
Easy deployment of highly accurate models anywhere.
Efficient management of the entire machine learning lifecycle.