Grand Ballroom EFGH
Enterprises today have a plethora of information that needs to be harnessed for business insights. Over the years, Enterprises have made investments in a variety of storage solutions, relational databases, warehouses, NoSQL stores, Big Data analytics platforms, Data Lakes, Cloud Stores, etc. As we enter the era of Machine Learning (ML), it is important to understand how to bring these silos together to discover, build, and deploy ML models in production.
This tutorial covers the technical concepts and architectural models required to operationalize and architect your Enterprise Data Fabric for ML initiatives. The tutorial is divided into the following sections:
- A Data Engineering perspective on the end-to-end ML workflow in-production
- Taxonomy of requirements & landscape of available building blocks for the Data Fabric
- Putting it together: Defining the Data Fabric architecture with reference examples
The tutorial assumes a basic knowledge of popular Big Data and Analytics solutions. We assume no ML background—our focus will be on operational concepts rather than the internal mathematical formulations of ML algorithms. The tutorial is designed for Storage architects, Data Engineers, and Engineering Managers interested in learning designing of Data Fabrics.
- Different architectures for Data Stores (Relational, MPP, NoSQL, Event Stores, In-memory grids, etc.)
- Different Analytic programming models and Frameworks (Batch, Interactive, Stream)
- Example Cloud computing platforms for Data Management
- Workflow for Machine Learning models in production
- Blue-print of a Data Fabric
- Examples reference architectures of Data Fabric deployments