App Monitoring & APM

Achieve full-stack observability and AI-powered monitoring.

Overview

Comprehensive observability for microservices and monoliths, combining logs, metrics, traces, and AI-driven anomaly detection to meet strict SLAs.

State-of-the-Art Methods and Architectures

Instrumentation

OpenTelemetry SDKs for auto-telemetry.

Ingestion

Fluentd/Logstash → ElasticSearch or Splunk.

Visualization

Grafana dashboards, New Relic One.

AI Ops

Detect anomalies using unsupervised learning on time-series metrics.

Market Landscape & Forecasts

>99%

SLOs Met

<200ms

Latency Target

<1%

Error Rate

Implementation Guide

Define SLOs

Error rate <1%, p99 latency <200 ms.

Alerting

Set multi-channel alerts (Slack, PagerDuty).

Blameless Postmortems

Document incidents and RCA.

Continuous Improvement

Quarterly reviews of alert fatigue and dashboard utility.

Technical Deep Dive

Data Preparation

Collect domain-specific text (e.g., medical records, legal documents). Clean and format data into JSONL.

Adapter Insertion

Insert LoRA/QLoRA adapters into the base model.

Training

Run training with domain data, using a learning rate schedule and early stopping. Monitor loss and validation metrics.

Evaluation

Use ROUGE, accuracy, or custom metrics. Compare outputs to base model.

Sample Code

from transformers import AutoModelForCausalLM, TrainingArguments, Trainer model = AutoModelForCausalLM.from_pretrained('llama-7b') # Insert LoRA adapters... # Prepare data... trainer = Trainer(model=model, args=TrainingArguments(...), train_dataset=...) trainer.train()

Why Fine-Tuning?

No APM

- Blind to issues - Slow incident response - High downtime

With APM

- Proactive monitoring - Fast incident response - High uptime

FAQ

Industry Voices

"APM is essential for modern cloud apps."

DevOps Weekly

Project Timeline