process
process on Beyond Market Intelligence: a running collection of 8 stories we have gathered and hand-picked because they are worth your time. Every post here touches on process in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around process, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
7 Common Python Mistakes to Avoid in AI Workflows
A clean execution in AI workflows shouldn’t be mistaken for success. While a successful run confirms the process completed, it reveals nothing about data integrity, model learning, or the reliability of saved results. To ensure robust and trustworthy AI pipelines, avoid these 7 common Python mistakes. Understanding these pitfalls is critical for data scientists, as highlighted in our recent piece, "5 AI Skills That Will Keep Data Scientists Relevant in 2027." Explore these insights and build confidence in your AI journey.
Is Agentic AI Just Automation?
The rise of "Agentic AI" has sparked considerable excitement, but a critical question remains: is it truly transformative, or simply sophisticated automation? Many current agents operate as complex flowcharts, limiting their adaptability and problem-solving capabilities. This post explores why this architecture falls short and outlines a more effective approach to building genuinely intelligent agents. Delve deeper into maximizing coding agent performance with our guide, "How to Effectively Solve 100+ Tasks with Claude Code," for practical strategies.
Constraining Output Space for SLM Narrow Automation Optimization
Optimizing narrow automation for Semantic Layer Models (SLMs) unlocks significant productivity gains. This series begins by exploring a crucial technique: constraining the output space, rather than solely relying on parsing generated text. By limiting potential outputs, we achieve greater efficiency and reliability in automated workflows. This initial article will detail how to implement this approach effectively. For broader context on navigating the evolving AI landscape, see our article, "New EU Guidelines For AI Labelling," for essential insights into regulatory considerations.
Discovered Materials is playing AI whack-a-mole to hunt cooler chips
Discovered Materials is pioneering a novel approach to chip development, essentially playing “AI whack-a-mole” to uncover superior materials for more efficient semiconductors. The company recently secured $9 million in funding to accelerate this search for groundbreaking compounds. This innovative strategy addresses a critical bottleneck in chip performance, moving beyond traditional material science. As Situational Awareness demonstrated with their $400M investment in Source Foundry, the pursuit of advanced chip technology remains a high-priority area for strategic investors.
Jeff Dean and other top AI researchers are leaving Google to launch their own startup
A seismic shift is underway in the AI landscape. Jeff Dean, the legendary Google executive, alongside other prominent AI researchers, is departing to launch a new startup focused on accelerating scientific discovery through artificial intelligence. This ambitious venture signals a progressive push beyond traditional computational methods, aiming to transform how research is conducted and breakthroughs are achieved. For deeper insights into the evolving intersection of AI and the physical world, explore our coverage of "TechCrunch Disrupt 2026’s Real World AI Stage."
How do you decide whether a data science problem really needs machine learning?
Deciding when to leverage machine learning versus a simpler analytical approach is a critical step in any data science project. Often, the allure of complex models overshadows the value of robust, interpretable methods. Factors like data volume, the complexity of relationships, and the need for explainability should guide your decision. If clear patterns emerge through traditional analysis, building a machine learning model may be unnecessary.
The AI Was the Easy Part: What Is a Forward-Deployed Engineer in a Supply Chain?
The rise of AI often overshadows the human expertise driving its practical application. "The AI Was the Easy Part" explores a critical, often unseen role: the Forward-Deployed Engineer. We detail what truly defines this position—beyond the technical skills—through a real-world supply chain project. Discover how these engineers bridge the gap between sophisticated AI models and tangible business outcomes. For a deeper dive into the engineering layers underpinning AI applications, see our article, "Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On."
How I Mastered Data Structures and Algorithms for ML (In 6 Weeks)
Ace your coding interviews and unlock advanced machine learning capabilities by mastering data structures and algorithms. This post details a focused, six-week strategy—the specific questions, techniques, and process—used to achieve proficiency. Learn how to move beyond foundational knowledge and build a robust skillset essential for ML roles. For a deeper dive into ensuring data quality within complex systems, explore "Building Trustworthy Production RAG Systems Through Continuous Evaluation" for practical guidance on catching potential errors.