Orchestrating MLOps Through Data Connector Innovation

December 19, 2022

Business data sources have continued to steadily increase, with the average company requiring multiple databases to support the operation of over 40 different SaaS applications. Data Connectors make retrieving data easy by encapsulating the details of a given database, scoping it to a specific workspace,  and allowing the ingestion of data for ML models to power machine learning applications. With simultaneous access to disparate data stores, the time and effort required to build predictive intelligence and machine learning models can be significantly reduced.

Without data connectors managing the data flow to and from ML models, collecting the information required for predictions is often an involved process and one that is repeated when taking a model into production. However, for connectors to truly deliver value, they need the ability to orchestrate those data flows directly within your pipelines. 

Learning the Drawbacks of Connectors in MLOps 

Despite the massive gap that data connectors have filled regarding data flow and distribution, they still possess several limitations that can hinder efficiency and integration with MLOps:

  • Data connectors commonly have Distributed Filing Systems (Hadoop/Windows Distributed File Systems, Network/Google/MapR File Systems, Server Message Blocks, etc.), but these frameworks only accommodate basic SQL transformations. 
  • Transformations run only when new data is loaded to the destination, only selects the tables that triggered it, and only transform at the end of the sync, which can severely decrease inference speeds. Underfitting, which by definition occurs when data is unable to establish a relationship between variables is a common issue for practitioners as a result. 
  • Connectors have also proven inefficient when processes are scheduled sequentially, as they are not designed for tracking or logging the data through each workflow stage. Without proper tracking data quality inevitably will suffer and strain operational resources, affecting your overall business value by reducing efficiency and preventing your ability to scale properly. These sorts of data connectors can still be too labor-intensive to properly manage data workflows, decreasing the speed of production deployment.

With the additional data operation needs in MLOps like sessioning, merging data tables, and data experimenting, there is definite value to orchestrating not just the data source connection but the operations on the data prior to inferencing. Wallaroo believes orchestrating and centralizing these processes streamlines MLOps leading to faster time to value.  Using out-of-the-box data connectors for common data sources with easy add-on capabilities also gives practitioners the levers to manage sequencing between dependent tasks in an automated manner. The evolved data connector with these abilities offers seamless business operations continuity to easily scale up any organization’s  ML initiatives. 

Data Flow Orchestration for Better MLOps

Integrating new tools into a business’s process is often accompanied by a learning curve, but with the right interface, the integration experience can be painless. The ideal deployment solution has the capability not only to manage data connections but also to orchestrate the flows through inference pipelines. Add to that an intuitive experience that ensures  ML pipelines are set up to provide analytics-ready data that is the lifeblood of MLOps. Centralized configuration and orchestration of data operations on the same platform that delivers critical ML insights drastically minimizes errors and reduces resource costs. 

Wallaroo is designed for maximum flexibility around your existing ML toolkit in contrast to competitors that require enterprises to change to fit in their box. Our platform strives to go beyond connecting your data to provide complete pipeline orchestration that facilitates scalability and lightning-fast deployment, so YOU can focus on the predictions tailored to your business needs.

If you’re interested in exploring a constantly improving ML deployment solution, dedicated to simplifying the connection to your data and improving your MLOps, contact one of our specialists to learn more about all that Wallaroo has to offer.