John Antezana, MBA

Discover how advanced LLM operations are transforming data management in Snowflake! This case study dives into innovative techniques and real-world success stories. 📖 Read it here: https://hubs.ly/Q02Lr1Xk0 Stay ahead in data science with tips from industry experts! #DataScience #LLM #Snowflake #TechInnovation #BigData #AI

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Josh Treat, Director of Advanced Analytics at Array and 2024 Relativity AI Visionary, shares how he assists clients in navigating Continuous Active Learning (CAL) workflows in our latest blog! Josh outlines key steps for implementing CAL and addresses common challenges in adopting AI-driven processes. Discover his insights and innovative approach to legal technology here: https://lnkd.in/gyzbZUrj #eDiscovery #LegalTech #AI #LegalInnovation #TrustArray

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Overcoming Challenges in Real-World ML Solutions Machine learning has immense potential, but real-world projects often come with unexpected hurdles. Unlike controlled environments, real-world data and algorithms have imperfections that introduce challenges at various stages. Here’s a breakdown of common challenges in ML development and practical strategies to tackle them: 1. Securing and Gathering Data The Challenge: Data collection is often constrained by availability, privacy concerns, and regulatory restrictions. The Solution: • Use synthetic data or publicly available datasets. • Follow strict data governance policies for compliance. • Apply techniques like differential privacy to maintain anonymity. 2. Representing Real-World Scenarios The Challenge: A non-representative training set leads to poor performance in real-world deployments. The Solution: • Ensure balanced sampling to represent all subgroups. • Monitor post-deployment performance and retrain models with updated data. 3. Dealing with Low-Quality Data The Challenge: Missing values, outliers, and mislabeling can create unreliable models. The Solution: • Prioritize data cleaning with imputation methods and outlier detection. • Automate quality checks with validation pipelines. • Use active learning to improve labeling accuracy. 4. Avoiding Underfitting The Challenge: Underfitting occurs when a model is too simplistic to capture key patterns. The Solution: • Increase model complexity with additional parameters or layers. • Focus on robust feature engineering to better represent the data. • Experiment with different algorithms suited to the task. 5. Preventing Overfitting The Challenge: Overfitting happens when models perform well on training data but fail on unseen data. The Solution: • Use regularization techniques and reduce complexity with dropout. • Evaluate performance with cross-validation. • Simplify the model to better generalize. 6. Meeting Infrastructure Needs The Challenge: Training and deploying models often require computationally intensive resources. The Solution: • Leverage cloud platforms like AWS, Google Cloud, or Azure. • Use tools like Kubernetes for scalable deployments. • Optimize models with lighter architectures to minimize resource usage. The Bottom Line Real-world ML development is challenging but rewarding. Success comes from anticipating potential issues, applying proven solutions, and staying adaptable. At Dallas Data Science Academy, our hands-on training ensures you’re prepared to handle these challenges and build impactful ML solutions in any industry. Register for two FREE sessions here - https://lnkd.in/gw-zNYhf

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Check out this InsideAnalysis podcast with our CEO, Justin Borgman! Eric Kavanagh and Justin talked about analytic architecture, the #Icehouse, and how Starburst gives data teams the freedom to be curious. Listen to the whole episode here: https://okt.to/TJ8bMo #analyticarchitecture #bigdataanalytics #apacheiceberg #trinodb

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✨ Master AI in Snowflake: Enhance Your DBT Workflow ✨ Our Senior Data Engineer, Jaime Andres Salas, guides you in integrating LLMs with Snowflake Cortex into your DBT transformations. Key points: 🔹 Sentiment analysis with AI 🔹 Join sentiment data with reviews 🔹 Create recommendations for feedback https://hubs.ly/Q02BX1sW0 👉 Read the full article #AI #Snowflake #DBT #DataScience #LLM

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You know a Semantic Layer would be hugely valuable, but how do you actually build such a thing? Pro tip: Crafting a Semantic Layer is about building iterative velocity alongside accuracy, so that when your stakeholders ask about Revenue MoM grouped by Attribution Channel, you can answer instead of adding a ticket to the backlog. Start with these four steps: 1. Identify a Data Product that is impactful: Find something that is in heavy use and high value, but fairly narrow scope. Don’t start with a broad executive dashboard that shows metrics from across the company because you’re looking to optimize for migrating the smallest amount of modeling for the highest amount of impact that you can. For example, a good starting place would be a dashboard focused on Customer Acquisition Cost (CAC) that relies on a narrow set of metrics and underlying tables that are nonetheless critical for your company. 2. Catalog the models and their columns that service the Data Product, both in dbt and the BI tool, including rollups, metrics tables, and marts that support those. Pay special attention to aggregations as these will constitute metrics. 3. Melt the frozen rollups in your dbt project, as well as variations modeled in your BI tool, into Semantic Layer code. 4. Create a parallel version of your data product that points to Semantic Layer artifacts, audit, and then publish. Creating in parallel takes the pressure off, allowing you to fix any issues and publish gracefully. You’ll keep the existing Data Product as-is while swapping the clone to be supplied with data from the Semantic Layer. Dig deeper into the step-by-step process of how to ship a Semantic Layer in pieces at our link in the comments.

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We've just dropped some amazing new additions to the Snowflake Marketplace! 🌟 Dive into a treasure trove of data, apps, and AI products from hundreds of awesome providers. Whether you're looking to explore, try, or buy, we've got you covered for all your innovative business needs. Check them out now and level up your data game! 🎊 UndercastAI - Date/Time Dimensions for Data Models https://lnkd.in/g5ucxPWx  Version^ - Detailed insights into the deprecation status of Python modules https://lnkd.in/grcQQaCQ  DataRadar - Data Reliability & Cost Optimization https://lnkd.in/gK5BMQWQ

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Catch-up on September episodes, we talked data warehousing, brand marketing measurement, revenue-driving workflows and incrementality vs MTA 👇 138: Erin Foxworthy: Snowflake's Industry Lead on the future of data warehousing, from APIs to data sharing and a unified data layer https://lnkd.in/gx-rKViD 137: Liam Moroney: Rethinking measurement by balancing pipeline, brand, and long-term value in a nonlinear world https://lnkd.in/ghUGXWVC 136: Benoit Leggieri: Livestorm’s Head of Growth on crafting revenue driving workflows with Customer.io https://lnkd.in/gvBpfRfB 135: Pranav Piyush: Why multi-touch attribution is broken and what you should do instead https://lnkd.in/g2WSjTRh

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Want to learn more about how to get started using Snowflake Cortex LLM Functions? Check out this post from Jon Coulter detailing how to use these tools to quickly classify data, empowering Data Engineers with Data Science tools! https://lnkd.in/gMTFT5xQ

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Dive deep into your data's potential with Infometry Inc's INFOfISCUS Sales Analytics! Built on Snowflake Native App architecture, it seamlessly ingests, transforms, and analyzes data from ERPs and CRMs. - Infofiscus Sales Analytics: Effortless Sales Insights - No-code, all-gain: Get started in minutes, no coding required! - Seamless integration: Works with your existing CRM, ERP, and forecasting apps. - Real-time insights: Make data-driven decisions faster with near real-time data. - Deeper understanding: Gain powerful analytics across Sales, Marketing, Finance & Supply Chain. - Effortless setup: Find & install the app directly from the Snowflake Marketplace. Ready to Redefine your approach to data analytics with INFOfISCUS Sales Analytics? Let this No-Code Snowflake Native App be your catalyst for digital success. Visit the Snowflake Marketplace - https://lnkd.in/dtNaUsV6 Visit our website - https://lnkd.in/esKHgdg9 . . . #INFOfISCUS #SalesAnalytics #DataPotential #Snowflake #NoCode #Infometry #DataTransformation #RealTimeInsights #SalesInsights #CRM #ERP #BusinessIntelligence #DataDriven #DigitalSuccess #DataAnalytics #SupplyChainAnalytics #MarketingAnalytics #FinanceAnalytics #SnowflakeMarketplace #DataIntegration #EffortlessSetup #TechInnovation

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With the GA release of Cassandra 5 now live, it’s a great time to check out Jon Haddad's recent deep dive webinar. Jon covers all the key new features, including: 🔹BTI Storage format,  🔒Data Masking,  🔍 Vector Search,  ➕ More! He also demonstrates how to explore these updates using his own open-source tools in under an hour. 👉 Watch the recorded webinar here  https://lnkd.in/eThwxhyT #ApacheCassandra #Cassandra5 #AxonOps #TechWebinar #WebinarReplay

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If you're experiencing slowness with Looker Studio when connecting to your data in BigQuery, you're not alone! This week we're sharing 3 optimizations you can implement today to speed up your Looker Studio dashboards. https://lnkd.in/gp6gXncQ Google #BigQuery #DataOptimization #LookerStudio #DashboardSpeed

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Heading to #DataCloudSummit? Stop by booth 1225 where we’ll be talking all things AI, customer marketing, and how to drive ROI through the roof. We’ll also be hosting a discussion with Zillowaround marketing, advertising, and how Snowflake is powering both. Get all the details here.

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Walk through a pipeline forecasting workbook developed by Mike Gerardi, Sigma’s VP of Sales, and Jake Hannan, Sigma’s Head of Data and Demo Engineering. See how it’s reshaped their workflows and why it’s a must-have for any sales leader looking to stay ahead. This #SigmaSolution cuts down hours of busy work, provides instant insights, and allows sales teams to focus on what really matters — closing deals and hitting targets! 🏅 Read how you can seamlessly bridge the gap between sales and data with Sigma today! https://bit.ly/406dY96

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Thoughts on this? >> Salesforce AI Research Introduces AGUVIS: A Unified Pure Vision Framework Transforming Autonomous GUI Interaction Across Platforms >> Comment below! >>> lqventures.com #mhealth #AI #healthtech #industry40 #IoT

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Practitioners, worried about the nightmare of bad data? Anomalo’s data quality platform is now available on Google Cloud Marketplace, making it easier than ever to monitor your data without writing code or setting thresholds. Simplified Procurement: Buy through your Cloud Billing account—no new vendor setup needed. Plus, it counts towards your committed GCP spend. Unmatched Data Quality: Anomalo uses advanced AI to detect data issues early, saving you from the disastrous consequences of bad data, especially in AI and ML models. Secure & Scalable: Monitor any BigQuery or AlloyDB table with a few clicks, keeping your data secure within your VPC. Prevent Big Data Disasters: Trust Anomalo to ensure your data is accurate and reliable, helping you avoid costly mistakes and maintain stakeholder trust. Ready to protect your data and breathe easier? Read the blog to get started with Anomalo on Google Cloud Marketplace today https://bit.ly/3RyLwrA #DataQuality #AI #GCP #Anomalo #DataMonitoring #CloudComputing #BigData #MachineLearning #DataSecurity

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