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Teradata@VakifBank

VakifBank Modernizes Feature Pipelines with Teradata

VakifBank, a financials organization in Turkey, uses Teradata Vantage from Teradata to support applied machine learning for data scientists and ML engineers.

Value results

CategoryValue result
ProductivityHandoffs in applied machine learning sit in a shared queue instead of a mailbox trail
Risk and complianceTeradata Vantage is the governed place data scientists and ML engineers use for applied machine learning
CapabilityNew joiners can see how applied machine learning actually runs

Story

VakifBank is based in Turkey and runs financials operations at a scale where feature pipelines cannot live in side channels. Data scientists and ML engineers were reconciling competing copies of the same work, which slowed applied machine learning and hid who owned the next step.

The company runs applied machine learning on Teradata, with Teradata Vantage as the product data scientists and ML engineers actually open. Teradata provides a connected multi-cloud data platform for large-scale analytics and mixed workload warehousing. For VakifBank, that means data scientists and ML engineers can open one workflow, see feature pipelines, and let neighboring teams join without inventing a parallel stack.

Public materials confirm the companies and products. They do not always publish a single verified KPI for this pairing, so the outcome here is operational: clearer ownership, fewer stalled handoffs, and a shared record for feature pipelines.

Relationship map

VakifBank uses Teradata, Oracle, CrowdStrike, Fortinet, IBM, Informatica, SS&C, Stripe, Fiserv, Microsoft Azure, Splunk, Datadog, NVIDIA, Workday. Shared with 6sense, ABB, ACS Group, Activision Blizzard, Advantest. Industry: Financials. Value: Productivity, Risk and compliance, Capability. Drag nodes, filter types, or expand a node to follow more commonalities.

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