Got Data Science Platform, Visualization, and MLOps Tools, yet struggling with scaling AI? Feature Engineering holds the key to faster development! Data science, analytics, and BI leaders in disparate industries such as financial services, retail, and manufacturing have been spending heavily on AI tools, upgrading data infrastructure, augmenting BI with ML. Your organization may already have an AI Center of Excellence (CoE) to support LoB’s where the teams are building predictive applications that can predict churn, detect fraud, and forecast inventory. Yet, for the vast majority of enterprise customers, the AI development has been slow, AI initiatives have not scaled according to expectations. What can you do to scale AI development, accelerate adoption and propel innovation? You need to step back, analyze the data science process and focus on three core buckets in the development workflow - Data Preparation, Feature Engineering, and Machine Learning. More specifically, answer three critical questions: Who…
Concerned about accelerating AI workflows, addressing multiple use cases, and scaling ML initiatives? Here are the rules to help you succeed. In this two-part blog series, we review the ten rules that will ensure success with your first AI/ML project paving the way for many more. In the first part, we discussed the alignment of business objectives with use cases, getting a head start on data preparation and the mechanics of feature engineering. We also talked about understanding AutoML tools’ capabilities and ensuring the right modeling approach while balancing model accuracy and interpretability. This second part will discuss why visibility to the ML processes and results are critical, the importance of data science education, real-time analytics, infrastructure compatibility, and ML operationalization. Here are the five best practices: Ensure the ML project has visibility. ML initiatives fail because they operate with a silo mentality, like a secret science experiment that…
Harvard Business Review recently published interesting research findings from Accenture. The research shed light on how 1500 C-suite executives across 16 industries in 12 countries think about AI. The survey concluded that while most C-suite executives recognize the need to integrate AI capabilities, many fail to move beyond the PoC stage. According to the research, three out of four executives believe they risk going out of business entirely if they don’t scale AI. The authors espoused a radical idea of killing the PoC and jumping straight to scale. But how do you move beyond AI experiments? What tools do you need to successfully implement AI at scale? According to Accenture research, companies that are succeeding with AI are doing three critical things: 1)Pivoting from PoCs to pilots, 2) Committing to action, and 3) Ensuring the right team is in the right place from the very start. At dotData, we believe…
