Abhishek Jha | Freelancer & ethical hacker

Personal Reflections & Tech Insights

Blogs

Aug 12, 2026 6 min read

Orchestrating LLM Workflows in Serverless

Building real-world LLM applications often means chaining multiple prompts, conditional logic, and retries. Serverless functions need orchestration to manage this state and complexity.

Aug 10, 2026 6 min read

Taming AI Microservices with a Service Mesh

AI workloads bring new complexity to microservices. A service mesh can help manage traffic, observability, and security for these demanding systems.

Aug 09, 2026 6 min read

Catching Silent Failures in ML Pipelines

Your ML pipeline might be green, but your model could be failing silently in production due to data issues. Data observability helps you catch these problems.

Aug 07, 2026 6 min read

Why Your Production AI Needs Explanations

Deploying black-box AI models without understanding their decisions can lead to serious issues. Explainable AI (XAI) is critical for debugging, trust, and compliance.

Aug 06, 2026 5 min read

Monitoring AI in production: Beyond the usual metrics

Observing AI/ML systems in production goes beyond traditional infrastructure metrics. It needs deep insights into data quality, model behavior, and performance to catch silent failures.

Aug 05, 2026 5 min read

Kubernetes for ML: Orchestrating Production Models

Deploying machine learning models to production brings unique challenges. Kubernetes offers powerful tools for managing these complex workflows, but it's not a silver bullet.

Jul 27, 2026 2 min read

Event-Driven AI Pipelines with Serverless Architecture

An event-driven AI pipeline with serverless architecture is a powerful combination that can improve scalability, reliability, and cost-effectiveness.

Jul 26, 2026 5 min read

Federated Learning: AI without centralizing all the data

Federated Learning offers a way to train AI models on distributed, sensitive data without ever centralizing the raw information, addressing critical privacy and scaling challenges.

Jul 25, 2026 5 min read

Feature Stores Bridge ML Training and Serving

Feature stores solve the messy problem of managing, transforming, and serving features consistently for machine learning models, bridging the gap between training and production inference.

Blogs

Abhishek's AI Clone