The first ML value chain landscape shaped by data scientists — and its top 5 insights (venturebeat.com)

TheSequence community of over 144,000 data scientists and ML engineers has collaboratively created the first ML value chain landscape shaped directly by practitioners, offering a comprehensive map of the applied machine learning lifecycle—from data collection and labeling to model training, deployment, and monitoring. Unlike traditional AI/ML landscapes built by analysts or VCs, this practitioner-driven approach integrates hands-on insights gathered through extensive surveys and analysis of existing market solutions, reflecting real-world challenges and preferences. Key findings reveal that nearly half of ML professionals face significant struggles with data processing and model monitoring, with the latter notably underserved by current tools that lack optimization and demand heavy manual work. Data processing emerges as the largest, most complex stage, where users yearn for more unified, scalable, and user-friendly solutions that seamlessly integrate with diverse platforms. Moreover, a persistent bottleneck exists in the poor interoperability between different lifecycle stages—both horizontally across tools and vertically among specialists with varied expertise—highlighting the fragmented nature of ML infrastructure today. While no existing solution fully covers the entire ML pipeline, platforms like Vertex AI, Scale AI, and Abacus.AI approach comprehensive coverage, indicating gradual progress toward a more cohesive ecosystem. This pioneering ML practitioner-driven landscape underscores the urgent need for enhanced software compatibility and strategic coordination across ML workflows. As the industry evolves rapidly, this community-informed framework sets the stage for developing more holistic, efficient tools that better address the practical realities faced by ML professionals worldwide.