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IBM Data Fabric and Data Science

BP Academy, IBM Innovation Center Ljubljana, room 1b, 9:00-13:00

Date: Friday, May 27 from 9:00 to 13:00 Central European Time

IBM Data Fabric and Data Science

A data fabric is an architectural approach to simplify data access in an organization to facilitate self-service data consumption. This architecture is agnostic to data environments, processes, utility and geography, all while integrating end-to-end data-management capabilities. A data fabric automates data discovery, governance and consumption, enabling enterprises to use data to maximize their value chain. With a data fabric, enterprises elevate the value of their data by providing the right data, at the right time, regardless of where it resides.

A data fabric could be logically divided into four capabilities:

•       Knowledge, insights and semantics

•       Provides a data marketplace and shopping experience

•       Automatically enriches discovered data with knowledge and semantics, allowing consumers to find and understand the data

•       Unified governance and compliance

•       Allows governance of metadata and policy enforcement ,  and Automatically applies policies in accordance with rules

•       automates data asset classification and curation

•       Intelligent integration

•       Accelerates a data engineer’s tasks through automated pipeline creation across data sources

•       Enables data access over any data with enforcement of data protection policies

•       Automatically determines best fit execution through optimized workload distribution and self-tuning

•       Orchestration and lifecycle

•       Enables the composition, testing, operation and monitoring of data pipelines

•       Infuses AI capabilities in the data lifecycle to automate tasks, selftune, and detect source data changes

Building ModelOps to deliver trusted AI, with Watson Studio.

·      What is ModelOps?

ModelOps (or MLOps) is a set of practices connecting data preparation, model creation, deployment, and monitoring.

·      Building ModelOps to deliver trusted AI
Our trust in technology relies on understanding how it works. We need to understand why AI makes the decisions it does. We’re developing tools to make AI more explainable, fair, robust, private, and transparent.

·      IBM Watson Studio
BM Watson® Studio empowers data scientists, developers and analysts to build, run and manage AI models, and optimize decisions anywhere on IBM Cloud Pak® for Data.

·      Demonstration
We will be using Watson Studio, to showcase how to prepare data to create, deploy and monitor a model, by using different integrated tools.

Speakers: Klemen Kobilica and Erik Ternav

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