Mindtree offers Omnichannel Analytics for Consumer Goods manufacturers. Learn how to leverage, multi-retailer, POS data to deliver business outcomes that improve the bottom line.
Understanding the DSR Market looks at the differences between a team and enterprise solution for handling multiple data sources in the consumer goods industry.
POSmart Webinar will cover:What an Enterprise Demand Signal Repository Can Do With POS Data. Why A Point Solution May Not Be Enough. How To Drive Revenue From POS Data.
Consumer Goods Companies need to overcome the challenges of integrating multiple data sources. Learn how to deal with disparate, misaligned and missing data. Gain insights that fuel an Enterprise from a single source of truth.
Most companies have data in various sources. Often, they do nothing but store the data because it takes too much time to make sense of it all. Taking control of the data is a process, but once the building blocks are in place a true Demand Signal Management Process will support an enterprise with reliable business insights.
SmartSuite is an enterprise foundation that integrates and harmonizes POS and Syndicated Data with your internal master data to support business intelligence needs of all departments for consumer goods manufacturers.
On this 30 min. Webinar you will hear from Relational Solutions co-founder, Janet Dorenkott as she discusses: Harmonizing POS data with master data to support
• Assortment Optimization
• OOS & Potential OOS Issues
• Post Event Analytics
• Event Alignments & more
Addressing the Omnichannel dilemma should be a top priority for CPG manufacturers. The omnichannel brings with it, a wide array of new challenges. It's time for retailers and manufacturers to get OmniSmart about attracting, converting and delighting customers!
Trade Spend tops the budget for most CPG Companies and most of them are making big mistakes! Learn what tops the list of Trade Spend Mistakes you want to avoid.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Mindtree offers Omnichannel Analytics for Consumer Goods manufacturers. Learn how to leverage, multi-retailer, POS data to deliver business outcomes that improve the bottom line.
Understanding the DSR Market looks at the differences between a team and enterprise solution for handling multiple data sources in the consumer goods industry.
POSmart Webinar will cover:What an Enterprise Demand Signal Repository Can Do With POS Data. Why A Point Solution May Not Be Enough. How To Drive Revenue From POS Data.
Consumer Goods Companies need to overcome the challenges of integrating multiple data sources. Learn how to deal with disparate, misaligned and missing data. Gain insights that fuel an Enterprise from a single source of truth.
Most companies have data in various sources. Often, they do nothing but store the data because it takes too much time to make sense of it all. Taking control of the data is a process, but once the building blocks are in place a true Demand Signal Management Process will support an enterprise with reliable business insights.
SmartSuite is an enterprise foundation that integrates and harmonizes POS and Syndicated Data with your internal master data to support business intelligence needs of all departments for consumer goods manufacturers.
On this 30 min. Webinar you will hear from Relational Solutions co-founder, Janet Dorenkott as she discusses: Harmonizing POS data with master data to support
• Assortment Optimization
• OOS & Potential OOS Issues
• Post Event Analytics
• Event Alignments & more
Addressing the Omnichannel dilemma should be a top priority for CPG manufacturers. The omnichannel brings with it, a wide array of new challenges. It's time for retailers and manufacturers to get OmniSmart about attracting, converting and delighting customers!
Trade Spend tops the budget for most CPG Companies and most of them are making big mistakes! Learn what tops the list of Trade Spend Mistakes you want to avoid.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation