Real-World Lessons From 50+ Agentic Orchestration Projects, Gemini Cloud Assist for Spark, NetoAI’s TSLAM: First Open-Source Telecom LLM, ARGUS Recommender

Your Weekly Dose of Data & ML -Connecting Challenges to Breakthroughs

Welcome toDataPro #148, your trusted guide through the fast-moving world of data science, machine learning, and AI infrastructure. Every week, we connect the toughest problems researchers and engineers face with the solutions shaping the next wave of innovation.

This edition covers breakthroughs where AI directly tackles long-standing pain points:

Domain-specific LLMs:NetoAI’sTSLAM, trained on AWSTrainium, becomes the first open-source telecom LLM, cutting costs and boosting accuracy by 37%.

Also inside: aColab-readyBioinformatics AI AgentwithBiopython,Baseten’s225% inference efficiency gains,FineVision’s24M multimodal dataset, andnew methods inDeepSpeed,LangExtract, Random Forest tuning, and Flink CMK encryption.

AtDataPro, we believe keeping up with data and AIisn’tabout chasing hype,it’sabout understanding how problems get solved, and how those solutions expandwhat’spossible.

Cheers,

Merlyn Shelley

Growth Lead, Packt

Google AI Introduces Personal Health Agent (PHA): A Multi-Agent Framework that Enables Personalized Interactions to Address Individual Health Needs.Google introduced thePersonal Health Agent (PHA), a multi-agent framework built on Gemini 2.0 that integrates data science, domainexpertise, and health coaching via an orchestrator. Evaluated on 10 benchmarks with 7,000+ annotations and 1,100 expert hours, PHA outperformed baseline models in accuracy, personalization, and trust. Though still research, it sets a blueprint for modular, agentic health AI capable of reasoning across multimodal data.

🔸Extracting Structured Data with LangExtract: A Deep Dive into LLM-Orchestrated Workflows:LangExtractis a workflow library forLLM-based structured extractionthat fixes schema drift and missing facts via prompt orchestration, chunking, and optional parallel or multi-pass extraction. It fine-tunes prompts per model, manages token limits, and streams results as generator outputs. A hands-on demo ingestsTechXploreRSS, filters articles, runs few-shot extractions (e.g., sectors, metrics, values, regions), and aggregates results intodataframes. Best practices: rich examples, 2+ extraction passes, and tunedmax_workers.

🔸Zero-Inflated Data: A Comparison of Regression Models:Zero-inflated data occurs when a dataset has far more zeros than expected, such as bike usage where most people report zero days. Standard Poisson regression struggles with this, so specialized models work better. TheZero-Inflated Poisson (ZIP)model handles excess zeros by combining a Bernoulli zero model with a Poisson count model, whilehurdle modelsfirst predict zero vs. non-zero and then model only the positives. In practice, both outperform Poisson or linear regression, with hurdle models offering a faster, solid fit and ZIP excelling when the data truly follows a zero-inflated pattern.