LLMs & Models
12 articles
Monitoring AI: Prometheus and Grafana for Observability
Increase AI model reliability with Prometheus and Grafana for observability and evaluation, streamline model performance monitoring
Evaluating LLMs with Structured Output and Semantic Similarity
Evaluate LLMs with structured output and semantic similarity for production-grade AI engineering, learn how to avoid common pitfalls
Efficient Fine-Tuning with QLoRA for LLMs
QLoRA enables efficient fine-tuning of large language models, reducing computational costs and memory usage.
LoRA Fine-Tuning for LLMs: Unlock Efficient Adaptation
Discover how LoRA fine-tuning enhances LLMs with efficient adaptation, improving performance without extensive retraining.
Integrating AI with Salesforce using Python
Integrate AI models with Salesforce CRM systems using Python for enhanced customer insights and automation, learn how
Optimizing AI Model Inference with Intel OpenVINO
Optimize AI model inference with Intel OpenVINO and model pruning techniques for better performance
Serving LLM Predictions with RESTful API using Flask and Docker
Serve Large Language Model predictions via RESTful API using Flask and Docker, streamlining model deployment and integration.
Kubeflow for AI Model Deployment on Kubernetes
Automate AI model deployment and management with Kubeflow on Kubernetes. Learn how to streamline your workflow
Deploying AI Models to Edge Devices with TensorFlow Lite
Deploy AI models to edge devices with TensorFlow Lite and Raspberry Pi for efficient inference, including model optimization and Raspberry Pi setup
Building Explainable AI with SHAP and LIME for Model Interpretability
Learn to build explainable AI systems with SHAP and LIME for model interpretability, improving transparency and trust in AI models.
Real-Time Data Processing with Apache Kafka and Spark
Implement real-time data processing for AI model training with Apache Kafka and Spark, streamlining your workflow
Automating LLM Testing with Pytest and Hypothesis
Automate LLM testing and validation with Pytest and Hypothesis for robust AI models