Performance Engineering Mastery Program – Live Training
(From Load Testing to Full-Stack Performance Engineering, Observability & Cloud Performance · Root Cause Analysis & AI JMeter, LoadRunner, JVM Tuning, Dynatrace, AppDynamics, Oracle AWR, Prometheus, Grafana, Kubernetes, AWS, Jenkins CI/CD))
The Performance Engineering Mastery Program is a comprehensive, hands-on training program designed to help professionals master performance testing, engineering, optimization, scalability, and application reliability. Learn how to design effective performance strategies, create realistic workloads, execute different types of performance tests, and identify performance bottlenecks across modern applications.
This program covers Load, Stress, Spike, Endurance, and Scalability Testing, along with advanced JMeter scripting, API performance testing, database performance analysis, monitoring, metrics analysis, and root cause identification. Participants will gain practical knowledge of CPU, memory, network, application, and database performance while learning how to analyze test results and optimize applications for better performance.
Through real-world scenarios, hands-on exercises, and practical projects, you will learn to integrate performance testing into CI/CD pipelines, cloud, DevOps, and modern application environments. By the end of the program, you will be equipped to move beyond traditional performance testing and confidently work as a Performance Engineer, helping organizations build fast, scalable, reliable, and high-performing applications.
About The Instructor:
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Visvashwarr is an experienced Performance Engineering, Performance Testing, and Application Performance professional and trainer with 10+ years of industry experience in performance testing, engineering, automation, monitoring, scalability, and production systems. She brings practical industry expertise in designing performance strategies, executing load tests, identifying bottlenecks, analyzing system behavior, and optimizing applications for high performance and reliability. She has hands-on expertise across Apache JMeter, performance testing methodologies, load and stress testing, API performance testing, workload modeling, scripting, correlation, parameterization, monitoring, database performance, application performance monitoring, CI/CD integration, cloud environments, and performance analysis. Her training approach focuses on connecting performance testing with real-world engineering practices, covering key areas such as bottleneck identification, root cause analysis, capacity planning, scalability, optimization, monitoring, and performance tuning. With 10+ years of professional experience and training experience with 200+ students, Visvashwarr delivers practical, hands-on sessions using real-world projects, performance scenarios, and industry best practices. Her learner-focused approach helps professionals develop industry-ready skills for careers in Performance Testing, Performance Engineering, SDET, QA Automation, DevOps, and Application Performance Engineering. |
Live Sessions Price:
For LIVE sessions – Offer price after discount is 200 USD 159 119 USD Or USD15000 INR 13000 INR 9900 Rupees.
OR
Free Demo Session:
8th September @ 7:00 AM – 8:00 AM (IST) (Indian Timings)
7th September @ 9:30 PM – 10:30 PM (EST) (U.S Timings)
8th September @ 2:30 AM – 3:30 AM (BST) (UK Timings)
Class Schedule:
For Participants in India: Monday to Friday @ 7:00 AM – 8:00 AM (IST)
For Participants in the US: Sunday to Thursday @ 9:30 PM – 10:30 PM (EST)
For Participants in the UK: Monday to Friday @ 2:30 AM – 3:30 AM (BST)
What student’s have to say about Trainer :
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⭐ The course provides a very structured approach to MLOps and AI Platform Engineering. I especially liked the hands-on work with MLflow, CI/CD, Kubernetes, and production AI systems. – Rahul Sharma ⭐ Excellent training! The modules on AI architecture, model serving, observability, and Kubernetes were explained clearly with practical examples.– Priya ⭐ I joined this program to strengthen my AI Platform Engineering skills. The LLM serving, RAG, GPU infrastructure, and AI gateway modules were extremely useful and practical.– Michael Anderson ⭐ One of the most comprehensive AI Platform Engineering programs I have taken. It covers MLOps, LLMOps, Kubernetes, RAG, observability, security, agentic AI, and production architecture. The capstone project made the learning experience especially valuable.– Sneha ⭐ Very practical and well-organized training. The instructor connects AI concepts with real production challenges. The hands-on capstone, troubleshooting, cost optimization, and system-design sessions were especially helpful. – Ayaan Khan |
What will I Learn by end of this course?
- Build a strong foundation in Performance Engineering concepts, principles, and methodologies for modern applications and systems.
- Design and execute performance, load, stress, spike, endurance, and scalability tests to identify application performance bottlenecks.
- Gain hands-on experience with leading performance testing tools such as JMeter, LoadRunner, and other modern performance testing technologies.
- Analyze key performance metrics including response time, throughput, latency, error rate, CPU, memory, disk, and network utilization.
- Monitor and troubleshoot application performance using APM and observability tools, logs, dashboards, and system metrics.
- Identify and analyze performance bottlenecks across applications, databases, APIs, servers, and infrastructure.
- Perform capacity planning, workload modelling, performance tuning, and scalability analysis for enterprise applications.
- Integrate performance testing into CI/CD pipelines and implement continuous performance testing practices.
- Work with cloud-based and distributed applications, containers, microservices, and modern architectures from a performance perspective.
- Gain practical experience through real-time projects, performance analysis, troubleshooting scenarios, and hands-on exercises to become job-ready as a Performance Engineer.
Salient Features:
- 25 Hours of Live Training with real-time project demonstrations
- 1 Year Access to All Recorded Sessions for continuous learning and revision
- Industry-Recognized Course Completion Certificate upon successful completionWho can enroll in this course?
- Performance Test Engineers looking to strengthen their skills in advanced performance testing, analysis, and engineering practices.
- Manual Testers & Automation Test Engineers who want to transition into Performance Testing and Performance Engineering roles.
- QA Engineers & Software Testers interested in learning load testing, stress testing, scalability testing, and performance analysis.
- Developers & Software Engineers who want to understand application performance, identify bottlenecks, and improve system scalability.
- DevOps & Cloud Engineers interested in performance monitoring, infrastructure analysis, capacity planning, and cloud performance optimization.
- SRE & Monitoring Engineers looking to enhance their knowledge of application performance, observability, monitoring, and reliability.
- Database Administrators & Backend Engineers who want to analyze and optimize database, API, and system performance.
IT Professionals & Experienced Developers preparing for careers in Performance Testing, Performance Engineering, Load Testing, and Application Performance Monitoring (APM).
Course syllabus:
Module 1: Foundations & Performance Strategy
- What is Performance Engineering?
- Functional vs. Performance Testing mindset
- 7 Types of Performance Testing
- Shift-Left Performance Engineering Strategy
- Real-world case study: Knight Capital $440M loss
- NFRs & Test Strategy
- Understanding vague requirements
- Converting requirements into measurable SLA parameters
- Writing NFR documents
- Using AI for NFR drafting
- Test Planning, Environments & Data
- Performance test planning
- Environment requirements
- Test data strategy
- Workload modeling fundamentals
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Module 2: JMeter, LoadRunner & HAR Analysis
Sessions 4–6 | 3 Hours
4. JMeter & LoadRunner: First Script- Record and execute your first load test in JMeter
- LoadRunner VuGen, Controller & Analysis
- HTTP, SOAP, REST, AJAX & RDP protocols
- HAR File Analysis
- Capture and analyze HAR files
- Identify JMeter performance misses
- Chrome DevTools for client-side performance diagnostics
- Performance Mental Model
- Performance Layers Map
- Browser → Network → App → JVM → Database → Cloud
- Linux performance utilities: Nmon, sar, iostat, Htop, Top
Module 3: Monitoring & Observability
Sessions 7–10 | 4 Hours
7. Prometheus: Metrics & PromQL
- Prometheus setup and exporters
- P95 response-time queries using PromQL
- Alerting rules and thresholds
- Grafana: Live Dashboards
- Build KPI dashboards from scratch
- CPU, Memory, P95 & Error Rate
- NFR threshold visualization
- Real-time performance monitoring
- Kibana & ELK: Log Analysis
- Load testing with seeded errors
- Finding errors in Kibana
- Log correlation techniques
- APM Tools: Dynatrace & AppDynamics
- Hands-on Dynatrace and AppDynamics
- Automatic instrumentation
- Live flow maps
- AI-driven root-cause analysis
Module 4: JVM Internals & Performance Tuning
Sessions 11–13 | 3 Hours
11. JVM Architecture
- JVM memory model
- Heap structure
- Garbage Collection deep dive
- GC log analysis
- High heap usage, memory leaks & GC overhead
- Heap Dumps, Thread Dumps & Deadlocks
- Analyze heap and thread dumps
- VisualVM and YourKit
- Identify deadlocks
- Thread locks and multithreading issues
- JVM Tuning & Heap Sizing
- GC tuning parameters
- Heap sizing strategy
- UI page slowness
- Transaction response-time degradation
- Load balancing and High Availability
Module 5: Database Performance Engineering
14. Oracle AWR & Wait Event Analysis
- AWR report deep dive
- Identify top wait events
- Python-based AWR scrubbing automation
- Database parameter assessment
15.SQL Tuning & Execution Plans
- SQL query tuning
- Explain plan analysis
- Indexing strategy
- ETL batch-job optimization
- Workload-based database tuning
16. DB Performance in Microservices
Connection pooling
Oracle RMS/RPM/ADF performance
MQ messaging performance
Database performance under distributed load
Module 6: Cloud & Microservices Performance
17. Cloud Performance Engineering – AWS
- Oracle Cloud distributed load testing
- Auto-scaling validation
- Cost vs. performance trade-offs
- Kubernetes & HPA Testing
- Kubernetes performance strategy
- HPA (Horizontal Pod Autoscaler) testing
- Docker container performance
- Shell automation scripts
- Microservices & Distributed Tracing
- SOAP & REST API performance testing
- SIEM application performance
- Distributed tracing tools
- Distributed tracing patterns
Live Sessions Price:
For LIVE sessions – Offer price after discount is 200 USD 159 119 USD Or USD15000 INR 13000 INR 9900 Rupees.
FAQ –Performance Engineering:
1️⃣ Is this Training Useful For Freasher?
Absolutely! This training is designed to help freshers kick-start their career in performance engineering. The curriculum starts from fundamentals and builds up to enterprise-level skills.
2️⃣ Is this Training Useful For Manual/Functi0nal Testers?
Yes. This training will help you transition from functional testing to performance engineering — one of the most in-demand and well-paid specialisations in QA and engineering.
3️⃣ Do u need prior programming or testing knowledge?
Basic understanding of software testing is helpful but not mandatory. The trainer starts from the fundamentals and gradually progresses to advanced topics.
4️⃣ Will get recording the session?
Yes. Every session is recorded and shared after the class. You will have 1 year access to all videos.
5️⃣ What if i miss the session?
Don’t worry. You’ll receive the recording of every session, which you can watch at your convenience. Recordings are available for 1 year.
6️⃣ What is the total duration and schedule?
The course is 25 hours across 25 sessions — 1 hour per session, daily. India: Mon–Fri, 7:00 AM–8:00 AM IST | USA: Sun–Thu, 9:30 PM–10:30 PM EST | UK: Mon–Fri, 2:30 AM–3:30 AM BST.
7️⃣ Does the course cover LLMOps, RAG, and Agentic AI?
Yes. The advanced modules cover LLM serving, vLLM, Ollama, inference optimization, RAG architecture, embeddings, vector databases, LLM observability, cost monitoring, Agentic AI, tool calling, memory, orchestration, multi-agent systems, retries, timeouts, and failure recovery.
8️⃣ Is this training theory-based or hand -on?
This training is fully hands-on and practical. Every tool — JMeter, Prometheus, Grafana, Dynatrace, JVisualVM, Jenkins — is used live in class with real scenarios. No death-by-PowerPoint.
9️⃣ Do you provide a certificate?
Yes. A course completion certificate from Isha Training Solutions (ISO 9001:2015 certified) is provided upon completing the program and capstone project.
🔟 Is this suitable for experienced professinals?
Absolutely. The curriculum is carefully designed to add value at every level — whether you’re just entering performance testing or are an experienced engineer looking to master JVM tuning, APM tools, cloud performance, and AI analysis.
Sample Course Completion Certificate:
Your course completion certificate looks like this……
