Vincent Amedekah

Quartr API datasets are now available via Snowflake. Access structured earnings data – live audio, real-time and historical transcripts, filings, slide decks, and event summaries. Pull Quartr data directly into your existing Snowflake workflows and augment your datasets. No new pipelines, no separate API integration. Build with data that drives decisions.

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Snowflake Intelligence: From Data to Decisions - Atrium's AI: AI projects fail when data quality is poor and siloed. At its core, the Snowflake platform solves this by giving access to unified insights with ...

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Imagine invoking a machine learning model with SQL. Ben Tengelsen dives into Snowflake's Document AI, highlighting its ease of use and the power it brings to extracting information from documents.

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🧠 Databricks vs. Keewano: The Future of AI Analytics for Product Teams Not all AI analytics platforms are created equal. While Databricks powers enterprise data infrastructure, Keewano is purpose-built for fast, causal insights in games and mobile apps—with 600× faster access and always-on AI agents that deliver answers, not just dashboards. 🚀 Compare setup effort, cost, speed, and how each handles real-time product optimization at scale. 🔍 Read the full breakdown: https://lnkd.in/dxcNCNKB #Keewano #Databricks #AIAnalytics #ProductIntelligence #GameAnalytics #LLM #AIAgents #Retention #Churn #DataPlatform #TechComparison #BuildBetterGames

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# Reproducibility In your experience, how would you make this environment decision framework more thorough Use uv if: ⚡ Blazing speed is required (10-100x faster than pip/conda) 🐍 Python-centric workflow with Julia as secondary 🪶 Minimal overhead preferred (single Rust binary) 🔄 Rapid prototyping and environment switching 📱 Modern developer tooling (lockfiles, monorepos) 🔥 Latest Python ecosystem (3.12+, newest packages) 💾 Storage constrained (much smaller than conda) Use Mamba if: 🚀 Speed is critical for daily development 🔄 Iterating frequently on environment setup 👥 Team prefers familiar tools (many scientists know conda) 📦 Mixing languages (Python + Julia + Java + R + Java + Quarto) 💻 Local development primarily 🧬 Bioinformatics/data science workflows (conda-forge ecosystem) 📟 Specific CUDA version required for PyTorch-GPU, TensorFlow-GPU, JAX-GPU. Use DevContainer / Docker if: 🔬 Long-term reproducibility is paramount (publications, dissertations) 🚀 Need cutting-edge packages (latest SciML developments) 🤝 Exact collaboration requirements ⚡ Performance critical work (no conda overhead) 🔒 Security/isolation is important 🏢 Enterprise/institutional requirements Lukas Mosser #dataScience #machineLearning #spe #SciML #docker #devcontainers #python #uv #pytorch #tensorflow

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New research from Databricks. It's about training enterprise search agents via RL. KARL introduces a multi-task RL approach where agents are trained across heterogeneous search behaviors, constraint-driven entity search, cross-document synthesis, and tabular reasoning. It generalizes substantially better than those optimized for any single benchmark. KARL is Pareto-optimal on both cost-quality and latency-quality trade-offs compared to Claude 4.6 and GPT 5.2. With sufficient test-time compute, it surpasses the strongest closed models while being more cost efficient.

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Traditional infrastructure silos genomics, imaging, and clinical data across disconnected platforms, slowing precision medicine research. The TileDB and Databricks integration combines specialized scientific data management with large-scale analytics through synchronized catalogs and distributed compute. Researchers can access multimodal datasets from either platform while maintaining governance and enabling advanced analytics at scale. 👉 https://hubs.la/Q041Y6wF0

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The AI Forward Deployed Engineer is here. Amplify your development work with AI labor, and reach your next breakthrough faster than ever. At DevCon 3, watch as Palantir Software Engineer Ankit Shankar unveils the AI Forward Deployed Engineer — an agentic system that can build data transformations, create and edit your ontology, create functions, build entire applications, and much more.

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Snowflake’s ability to handle diverse data workloads makes it a stronger choice over BigQuery for complex data environments. Our latest whitepaper provides an in-depth comparison of these leading data cloud platforms, exploring crucial factors such as pricing models, performance, scalability, and multi-cloud flexibility. Download the whitepaper to make an informed decision: https://lnkd.in/gQsnmMq2 #BigQuery #Snowflake #CloudData #DataMigration #Kasmo

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If your datasets are poorly labeled, you’re inviting a host of issues like noise, bias, and inconsistencies that can seriously hurt your model's accuracy and reliability when it’s out in the wild. That's why data labeling has evolved from being just a preliminary step into a vital part of engineering and operations in the AI lifecycle.

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