Applying MLOps best-practices ensures optimal performance and quality of data science models applied to real-time data streams. Our experts implement and optimize algorithms to meet the most stringent use-case requirements (e.g. latency and/or throughput).
With anomaly detection, machine learning, artificial intelligence, neural networks and predictive analytics all part of our daily tasks, we are perfectly equipped to support you in any analytics scenario you might have.
Examples of analytics technologies we frequently use include:
- Apache Flink, Apache Spark Streaming, Kafka Streams for stream processing
- Apache Spark and Jupyter/JupyterHub/JupyterLab notebook environments
- Python, Spark MLLib and Tensorflow machine learning and deep learning
- MLOps model lifecycle management and CI/CD
To learn more about how Klarrio can help you take your data science capabilities to a whole new level, contact us today.