We already wrote above that building infrastructure from Open-Source tools requires more resources from the IT team. This not only increases development costs but also increases Time-to-market.
Therefore, we have developed the Cloud ML Platform, a convenient pre-configured environment where you can implement the full cycle of ML development. Out of the box, JupyterHub and MLflow are available in the ML Platform, which is already integrated. These solutions are familiar to many data scientists and allow you to solve basic MLOps problems.
We also integrated MLflow with the cloud infrastructure to solve server tasks. MLflow Deploy can automatically package ML models into Docker containers and make them available via a REST API to solve real-time maintenance tasks. The service is integrated with ML Platform components: JupyterHub and MLflow.
For the Cloud ML Platform, we chose Open-Source tools for several reasons:
Also, there are key tools for Data Engineering: Hadoop, Spark, Greenplum, ClickHouse, and Airflow.
We accrue 3,000 bonus rubles to new users to test the Cloud ML Platform from. Register with the service and start building MLOps in your company.
Pachyderm automates data transformation with data versioning, data lineage, and end-to-end pipelines in Kubernetes. You can use the same syntax for version control as in Git. In Pachyderm, the top level of the object is the repository, so Commit, Branches, File, History, and Provenance can be used to track and version a dataset.
Here are a few key features of Pachyderm:
The available version is for small teams. And those who need advanced features can choose the Enterprise version.
It supports REST and gRPC protocols and manual and automatic scaling. This is one of the most popular deployment solutions. Seldon Core is another Open-Source platform used for ML models similar to Kubernetes. Among the features of the tool:
Also Read: How To Work With Big Data Faster And More Efficiently: Kubernetes For Data Science
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