Interpretability and Explainability with Aruna Chakkirala

• Her early inspiration while growing up in Goa with limited exposure to career options. Her Father’s intellectual influence despite personal hardships and shift in focus to technology.
• Personal tragedy sparked a resolve to become financially independent and learn deeply.
• Inspirational quote that shaped her mindset: “Even if your dreams haven’t come true, be grateful that so haven’t your nightmares.”
• Her first role at a startup with Hands-on work with networking protocols (LDAP, VPN, DNS). Learning using only RFCs and O'Reilly books—no StackOverflow! Importance of building deep expertise for long-term success.
• Experiences with Troubleshooting and System Thinking; Transitioned from reactive fixes to logical, structured problem-solving. Her depth of understanding helped in debugging and system optimization.
• Career move to Yahoo where she led Service Engineering for mobile and ads across global data centers got early exposure to big data and machine learning through ad recommendation systems and built "performance and scale muscle" through working at massive scale.
• Challenges of Scale and Performance Then vs. Now: Problems remain the same, but data volumes and complexity have exploded. How modern tools (like AI/ML) can help identify relevance and anomalies in large data sets.
• Design with Scale in Mind - Importance of flipping the design approach: think scale-first, not POC-first. Encourage starting with a big-picture view, even when building a small prototype. Highlights multiple scaling dimensions—data, compute, network, security.
• Getting Into ML and Data Science with early spark from MOOCs, TensorFlow experiments, and statistics; Transition into data science role at Infoblox, a cybersecurity firm with focus areas on DNS security, anomaly detection, threat intelligence.
• Building real-world ML model applications like supervised models for threat detection and storage forecasting; developing graph models to analyze DNS traffic patterns for anomalies and key challenges of managing and processing massive volumes of security data.
• Data stack and what it takes to build data lakes that support ML with emphasis on understanding the end-to-end AI pipeline
• Shifts from “under the hood” ML to front-and-center GenAI & Barriers: Data readiness, ROI, explainability, regulatory compliance.
• Explainability in AI and importance of interpreting model decisions, especially in regulated industries.
• How Explainability Works -Trade-offs between interpretable models (e.g., decision trees) and complex ones (e.g., deep learning); Techniques for local and global model understanding.
• Aruna’s Book on Interpretability and Explainability in AI Using Python (by Aruna C).
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