Remove blogs product-discovery-challenges
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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

Requirements drift is a challenge in any development project (when the desired outcomes are changed, sometimes without notifying the development team), but the latter three are more apropos to data-intensive product development activities (which certainly describes AI projects).

Strategy 289
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Ontotext Marketing Gets a Boost from Knowledge Graph Powered LLMs

Ontotext

Motivated by our marketing team’s aim to simplify content discovery on our website, we initiated the Ontotext Knowledge Graph (OTKG) project. We envisioned harnessing the power of our products to elevate our entire content publishing process, thereby facilitating in-depth knowledge exploration.

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Successfully conduct a proof of concept in Amazon Redshift

AWS Big Data

For a POC on Amazon Redshift, we recommend a three-phase process of discovery, implementation, and evaluation. Discovery phase The discovery phase is considered the most essential among the three phases and the longest. Challenges In this section, we discuss the major challenges that you may encounter while planning your POC.

Testing 96
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Discovery to delivery: Transform the shopper’s journey

IBM Big Data Hub

.” A promise of accuracy in product detail, product capabilities, quality, price and delivery. It becomes very challenging to uphold the promise when so much is changing in the processes that trigger demand and supply.

B2B 80
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Application modernization overview

IBM Big Data Hub

Typical benefits realized would range from increased agility, cost-effectiveness and competitiveness, while challenges include complexity and resource demands. The biggest challenge with this is the amount of investment needed and many CIOs/CTOs are hesitant to invest due to the cost and timelines involved in realizing value.

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Streams Replication Manager Prefixless Replication

Cloudera

Replication is a crucial capability in distributed systems to address challenges related to fault tolerance, high availability, load balancing, scalability, data locality, network efficiency, and data durability. release) Remote topic discovery SRM needs to be able to know which topics are replicas and what are their respective source topics.

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The Future of LLMs and Spatial Computing with @mad.marchy: TDI 17

Data Science 101

In the near future I think the most use we’ll get out of it will be in research and discovery. DHBu1… There’s also a lot of challenges though, like privacy and security. Imagine walking into a store and getting a notification for a sale along with AR directions to the product. What type of development do you currently do?