Remove products maps
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Analyzing Your Excel Spreadsheets with NotebookLM

KDnuggets

. # Step 1: Preparing and Exporting Excel Spreadsheets Lets consider a quarterly business report with data on sales, expenses, profit, and customer satisfaction scores across different regions and product categories. What product showed the greatest profitability increase? Therefore, the average customer satisfaction score is 86.25% ".

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10 Python One-Liners for JSON Parsing and Processing

KDnuggets

product_names = [item[name] for item in products] This list comprehension iterates through each dictionary in the products list and extracts the name field. This one-liner efficiently plucks specific values from nested structures, making it perfect for creating summary lists or preparing data for further analysis.

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Graph RAG vs RAG: Which One Is Truly Smarter for AI Retrieval?

Data Science Dojo

In this comprehensive guide, we’ll explore the technical foundations, architectures, use cases, and best practices of graph rag versus traditional RAG, helping you understand which approach is best for your enterprise AI, research, or product development needs. Scientific Research: Discovering connections between genes, diseases, and drugs.

AI
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Muvera: Making multi-vector retrieval as fast as single-vector search

Hacker News

Learn more about our Publications Learn more Publications Resources We make products, tools, and datasets available to everyone with the goal of building a more collaborative ecosystem. Heres a simplified breakdown of how MUVERA works: FDE generation : MUVERA employs mappings to convert query and document multi-vector sets into FDEs.

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Understanding User Needs and Satisfying Them

Speaker: Scott Sehlhorst

We know we want to create products which our customers find to be valuable. Whether we label it as customer-centric or product-led depends on how long we've been doing product management. Attendance of this webinar will earn one PDH toward your NPDP certification for the Product Development and Management Association.

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Orchestrate generative AI workflows with Amazon Bedrock and AWS Step Functions

AWS Machine Learning Blog

Orchestrating parallel tasks using the map functionality Arrays are fundamental data structures in programming, consisting of ordered collections of elements. Step Functions provides two different mapping strategies for iterating through arrays: inline mapping and distributed mapping, each with its own advantages and use cases.

AWS
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Map Earth’s vegetation in under 20 minutes with Amazon SageMaker

AWS Machine Learning Blog

In this post, we demonstrate the power of SageMaker geospatial capabilities by mapping the world’s vegetation in under 20 minutes. For our vegetation mapping project, we’ve selected Sentinel-2 for its global coverage and update frequency. This example of vegetation mapping is just the beginning for running planetary-scale ML.

ML
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How to Package and Price Embedded Analytics

Just by embedding analytics, application owners can charge 24% more for their product. This framework explains how application enhancements can extend your product offerings. How much value could you add? Brought to you by Logi Analytics.

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Driving Business Impact for PMs

Speaker: Jon Harmer, Product Manager for Google Cloud

This session will provide you with a comprehensive set of tools to help you develop impactful products by shifting from output-based thinking to outcome-based thinking. Grow your user empathy skills: Better understand users and the problem space they are working in through Journey Maps that are customized for Product Managers.

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How to Find and Test Assumptions in Product Development

Assumptions mapping is the process of identifying and testing your riskiest ideas. Watch this webinar with Laura Klein, product manager and author of Build Better Products, to learn how to spot the unconscious assumptions which you’re basing decisions on and guidelines for validating (or invalidating) your ideas.