Mark Andreev

#[Machine Learning Engineer]

// Experience, 2017-now #[Machine Learning Engineer]
  • Developed Machine learning lifecycle platform for Industrial Automation (on top of Kubernetes, Dynamic resource allocation. Java for orchestration, Python for serve models)
  • Developed Incident Management service with custom workflow (on top of Spring State Machine)
  • Developed Data Storage Service for sensors measurements (based on PostgreSQL, Clickhouse and Kafka)
  • Developed end to end machine learning application for flight analysis
  • Created ad hoc analysis for tabular, geo, textual data for customer needs
  • Microservices based architecture
// Contribute to Open Source
  • Apache Camel
    • Add fix for header override by Azure Storage Blob download consumer.
  • Apache Ignite
    • Implemented target encoding preprocessor.
    • Implemented catboost inference integration.
    • Implemented new distances (BrayCurtis, Canberra, JensenShannon and etc).
    • Code cleanup in Util classes.
  • Zipkin
    • Implemented experimental PostgreSQL storage.
  • Tornado Swagger
    • Swagger API Documentation builder for tornado server.
// Development Stack Data Processing. Spark, Cassandra, Hadoop, Kafka, PostgreSQL. Java. Spring: MVC, Data, AMQP, Kafka, Integration, Batch, Security, State machine, Apache Camel, Quarkus. Python. Pandas, Scikit-learn, XGBoost, LightGBM, Catboost, Matplotlib, Tornado, FastAPI Third party. PostgreSQL, MongoDB, RabbitMQ, Redis, Kafka, Keycloak, Prometheus, Docker, Kubernetes, Linux, Airflow Cloud. AWS: EC2, S3, RDS, CloudFront, SQS, SNS, Lambda, IAM, Registry; Azure: VM, BLOB, Registry
// Education Lomonosov Moscow State University Master's degree. Computational Mathematics and Cybernetics Moscow Power Engineering Institute Bachelor's degree. Institute of automatics and computer science
// Publications A New Approach to Determining the Attitude of Authors of Short Texts to the Topics Discussed in the Texts on the Example of Estimating the Inflations Expectations, Oct 2017 DATA ANALYTICS AND MANAGEMENT IN DATA INTENSIVE DOMAINS, DAMDID / RCDL’2017, Andreev M. Big Data approach to measure inflation expectations: the case of the Russian economy, Jul 16, 2017 IFABS 2017 Oxford Conference, Goloshchapova I., Andreev M. Measuring inflation expectations ofthe Russian population with the help of machine learning. Voprosy Ekonomiki. 2017;(6):71-93. (In Russ.), Goloshchapova I., Andreev M.