Automation of ML-models lifecycle
Automation of ML-models lifecycle
to automate the work of data science teams and enable quick and easy deployment of ML-models to production
Automation of ML-models lifecycle
Automation of ML-models lifecycle
OVERCOMPLICATED MODEL IMPLEMENTATION PROCESS
MLOps platform supports all stages of the ML lifecycle: data preparation, model creation, training, deployment and monitoring
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 Platform 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.
We create solutions for data analysis and processing using Big Data technologies, offering new competitive advantages to our clients.
Our solutions are based on Apache Hadoop containing multiple libraries to process "big data" in clusters that may have multiple thousands of nodes.
We solve data processing tasks using the Apache Spark library featuring the maximum use of random-access memory, thereby ensuring high performance. Complex data processing algorithms are implemented using Spark SQL and Spark ML components.
In today's world, many tasks require real-time big data analysis in addition to conventional processing and storage. Our projects utilize Apache Kafka libraries to implement Data Injection /Data collection mechanisms, while Spark Streaming is used to implement a complex logic of data processing.
Access and data analysis are ensured by Apache Hive, which makes it possible to execute requests, aggregate and analyse data stored in Apache Hadoop, as well as provide users with a well-established SQL-based data processing tool.
Our work is based on using programming languages (Scala, Java, Python, etc.) and our proprietary accelerators to design data flows in Spark using a visual editor.
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 Platform runs in the local environment Kubernetes or on the platform RedHat OpenShift, 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.
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.
Case Studies
