The program's strategic backbone is the first module, which clarifies the birth of the AI video-production era and the 2026 ecosystem landscape. OpenAI Sora demo (February 2024) → Sora 2 launch (December 2024) → Google Veo 2 + Veo 3 (2025 native audio) → Runway Gen-4 (2025) → Kling 2.0 + Hailuo + Pika 2.2 + Luma 2 race; comparative landscape of open-source (HunyuanVideo + Wan 2.1 + LTX-Video + Mochi 1) + avatar-focused (HeyGen + Synthesia) ecosystem. Turkish market sector opportunities: e-commerce ad video (for Trendyol + Hepsiburada + Amazon TR sellers), social media viral content (TikTok 20M + Instagram Reels 40M + YouTube Shorts 30M Turkish users), AI influencer + virtual character + short film + music video market are detailed. Cost + quality + lip-sync + length +… **[Read the full article →](https://sukruyusufkaya.com/en/training/yapay-zeka-video-uretimi-egitimi)**]]> Tue, 19 May 2026 20:27:05 GMT https://sukruyusufkaya.com/en/training/voice-ai-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/voice-ai-muhendisligi-egitimi This training is designed to teach end to end — in Turkish — the voice-AI discipline that was the paradigm-opening agent layer of the 2024-2026 period. With OpenAI's GPT-4o Realtime API launch in October 2024, Anthropic Claude Voice and Google Gemini 2.5 Live API arriving in 2025, Sesame Maya opening the conversational-presence paradigm, Hume EVI 3's empathic voice interface, Cartesia Sonic 2's sub-100ms TTS, ElevenLabs' ultra-natural TTS + Conversational AI platform in 32 languages, Vapi (YC W24) + Retell AI (YC S23) voice-agent orchestrators, LiveKit Agents + Pipecat open-source frameworks, and Moshi (Kyutai), F5-TTS, Higgs Audio v2 open-source alternatives — the voice-AI ecosystem became a production-grade discipline. Voice AI automation offers critical advantage for the Turkish banking (BDDK IVR), healthcare (SBSGM emergency-call triage), e-commerce (Trendyol/Hepsiburada call center), and public-services (444 hotlines) sectors — yet a training that addresses this discipline end to end in Turkish is virtually nonexistent. This program is designed to fill that gap as Turkey's most comprehensive production-grade voice-AI reference training.

The program's strategic backbone is the first module, which clarifies the rationale for the transition from the classical 3-stage pipeline (STT → LLM text → TTS) approach to the native real-time speech-to-speech (S2S) LLM paradigm. In the classical pipeline, latency budget is high (STT 200ms + LLM 500ms + TTS 200ms = 900ms TTFB) and emotion + prosody information is lost; native S2S LLMs (GPT-4o Realtime, Gemini 2.5 Live, Claude Voice, Sesame Maya, Moshi) provide <500ms TTFB + emotion preservation + interruption handling. The 2026 ecosystem map is comparatively presented: commercial S2S (OpenAI Realtime, Claude Voice, Gemini Live), specialized voice (Sesame Maya, Hume EVI 3, ElevenLabs Conversational), open-source (Moshi, F5-TTS, Higgs Audio v2). Turkish market use cases: banking BDDK IVR automation + KVKK-compliant… **[Read the full article →](https://sukruyusufkaya.com/en/training/voice-ai-muhendisligi-egitimi)**]]> Tue, 19 May 2026 19:04:33 GMT https://sukruyusufkaya.com/en/training/ai-code-review-sistemi-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/ai-code-review-sistemi-muhendisligi-egitimi This training is a 3-day advanced program designed for Senior Backend Developers, DevOps Engineers, Tech Leads, Engineering Managers, and AI Engineers who want to transform enterprise software teams' pull-request review process with an AI-driven approach and increase developer productivity. With GitHub Copilot Reviews' launch in 2023, CodeRabbit's emergence from the YC W24 batch in 2024 reaching 30K+ GitHub repos + 1,500+ enterprise customers, Greptile's codebase-aware AI review approach, Qodo's (formerly Codium AI) product family (Gen + Merge + Cover), Bito Code Review Agent, Cursor BugBot, GitLab Duo Code Review, Sweep AI autonomous PR bot, and the Diamond ecosystem, the 2024-2026 period was the era when AI code review integrated into enterprise software-development processes. In Turkey, a training that addresses this discipline in Turkish + end to end + production-grade is virtually nonexistent — existing content either stays at short CodeRabbit tutorials or freezes at simple OpenAI API prompt demos. This program is designed to fill that gap as Turkey's most comprehensive production-grade AI code-review reference training.

The program's strategic backbone is the first module, which clarifies the rationale for the transition from the classical static-analysis approach (SonarQube, ESLint, Pylint, golangci-lint) to modern AI-driven code-review platforms. Classical linters stay at the syntactic level; SonarQube + Snyk + Semgrep offer semantic analysis but their rule-based + cross-file context is insufficient; AI code review, with semantic + intent + context-aware advantage, can understand what the developer 'really wants to do' and produce comments. 2026 ecosystem map: CodeRabbit (YC W24, 30K+ repos + 1,500+ enterprises), Greptile (YC S23, codebase-aware), Qodo (test + review hybrid), Bito + Sweep + Diamond + Cursor BugBot, GitHub Copilot Reviews + GitLab Duo Code Review platform-native solutions. ROI calculation: 30-50% PR cycle time reduction, 20-40%… **[Read the full article →](https://sukruyusufkaya.com/en/training/ai-code-review-sistemi-muhendisligi-egitimi)**]]> Tue, 19 May 2026 18:33:46 GMT https://sukruyusufkaya.com/en/training/vllm-internals-custom-backend-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/vllm-internals-custom-backend-muhendisligi-egitimi This training is designed to teach end to end — in Turkish — the internal architecture, algorithmic foundations, and production deployment discipline of vLLM, which has become the de facto inference-engine standard of the 2024-2026 period. The journey that began with the PagedAttention paper UC Berkeley Sky Computing Lab presented at SOSP in September 2023 has been transformed into a production-grade platform with 30K+ GitHub stars, 2025 incubation under LF AI & Data Foundation, vLLM v1 redesign (March 2025), NVIDIA Dynamo collaboration (March 2025), and the Neural Magic + Anyscale + Red Hat ecosystem. In Turkey, a training that addresses this discipline from source-code level to production Kubernetes deployment end to end is virtually nonexistent — existing content either stays at short vLLM tutorials or freezes at OpenAI-compatible server usage demos. This program is designed to fill that gap as Turkey's most comprehensive production-grade vLLM internals reference training.

The program's strategic backbone is the first module, which clarifies vLLM's birth and rise, why it became the inference-engine standard, and the 2026 ecosystem landscape. The Kwon 2023 PagedAttention paper (SOSP) from UC Berkeley Sky Lab; 2024 production spread; LF AI & Data Foundation 2025 incubation; vLLM v1 redesign (March 2025 — sync-to-async architecture transition, 1.7x throughput); NVIDIA Dynamo collaboration; Neural Magic acquisition (by Red Hat in 2024). Inference engine comparison: vLLM vs SGLang (CMU + Stanford, radix attention), vs TensorRT-LLM (NVIDIA-only, fastest NVIDIA-native), vs Hugging Face TGI (simple + production-ready), vs LMDeploy (Shanghai AI Lab, TurboMind kernel). 2026 inference landscape: multi-vendor inference (NVIDIA H100/B200, AMD MI300X/MI355X, AWS Trainium 2, Apple Silicon), unique requirements of reasoning-model + agent + long-context serving, open-source vs commercial inference (Anyscale, Together AI, Fireworks).

The second module covers… **[Read the full article →](https://sukruyusufkaya.com/en/training/vllm-internals-custom-backend-muhendisligi-egitimi)**]]> Tue, 19 May 2026 18:17:33 GMT https://sukruyusufkaya.com/en/training/ai-red-teaming-adversarial-robustness-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/ai-red-teaming-adversarial-robustness-muhendisligi-egitimi This training is designed to teach end to end — in Turkish — AI red teaming + adversarial robustness engineering, the discipline of systematically testing and hardening enterprise generative-AI and LLM products against attack vectors. Developments defining the 2024-2026 period: EU AI Act entering into force in May 2024 + Article 15 robustness/cybersecurity mandate + Article 50 transparency, KVKK Generative AI Guide (2024), ISO/IEC 42001:2023 AI Management System certification, NIST AI RMF 1.1 (2024), the publication of Microsoft AI Red Team methodology, the UK AI Safety Institute (AISI) framework, the maturation of NVIDIA Garak and Microsoft PyRIT open-source red-team tools, the OWASP LLM Top 10 v2.0 (2025) update, and the maturation of the MITRE ATLAS framework. In Turkey, a training that addresses this discipline in Turkish + end to end + production-grade is virtually nonexistent — existing content either stays at OWASP slides or freezes at the shallow jailbreak-demo level. This program is designed to fill that gap as Turkey's most comprehensive production-grade AI red teaming reference training.

The program's strategic backbone is the first module, which clarifies how AI red teaming differs from classical penetration testing and maps the 2026 threat landscape. Classical pen testing was designed for deterministic systems; AI systems are non-deterministic + open to semantic attack surface + natural-language jailbreak — modern AI security cannot be built without grasping this difference. Anthropic constitutional AI + ARC Evals + Responsible Scaling Policy, OpenAI Preparedness Framework + system card red-team reports, Microsoft AI Red Team + UK AISI Inspect Framework methodologies are comparatively covered. Compliance mandates: EU AI Act Article 15 (robustness + cybersecurity), KVKK Generative AI Guide (2024), ISO/IEC 42001:2023 audit requirements, banking BDDK + healthcare SBSGM + financial SPK + audit KGK sectoral AI security frameworks are detailed.… **[Read the full article →](https://sukruyusufkaya.com/en/training/ai-red-teaming-adversarial-robustness-muhendisligi-egitimi)**]]> Tue, 19 May 2026 17:39:53 GMT https://sukruyusufkaya.com/en/training/browser-agent-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/browser-agent-muhendisligi-egitimi This training is designed to teach end to end — in Turkish — the paradigm-opening agent layer of the 2024-2026 period: the browser-agent discipline. The October 2024 launch of Anthropic Claude Computer Use, the January 2025 arrival of OpenAI Operator + Computer Use API, and the contributions of Google Project Mariner and Microsoft Magentic-One opened a new frontier of AI engineering: the browser-agent discipline. New-generation autonomous browser agents — vision-language-model-based, adaptive, controllable by natural-language prompts — replaced the script-based, brittle, high-maintenance approach of classical RPA solutions (UiPath, Automation Anywhere). In Turkey, a training that addresses this discipline end to end starting from Playwright foundations and reaching the Browser Use / Stagehand / Anthropic Computer Use / OpenAI Operator / Skyvern / Magentic-One stack is virtually nonexistent — existing content either stays at short Playwright tutorials or freezes at shallow demo level. This program is designed to fill that gap as Turkey's most comprehensive production-grade browser-agent reference training.

The program's strategic backbone is the first module, which frames the birth and momentum of the browser-agent era. Anthropic Claude Computer Use's October 2024 launch — Claude Sonnet 3.5 / 4.6 reading screenshots and producing mouse + keyboard actions — opened the paradigm; OpenAI Operator's January 2025 ChatGPT Pro tier launch spread the consumer-facing autonomous-agent vision; the OpenAI Computer Use API gave developers access to this paradigm; Google Project Mariner + Microsoft Magentic-One deepened the research area; Adept ACT-2 and other solutions joined the race. Difference from classical RPA: UiPath / Automation Anywhere is scripted (manual updates on every UI change), brittle (the pipeline collapses the moment a CSS selector breaks), high maintenance; AI browser agents are vision-aware (adapt by reading screenshots), reasoning-driven (make… **[Read the full article →](https://sukruyusufkaya.com/en/training/browser-agent-muhendisligi-egitimi)**]]> Tue, 19 May 2026 16:28:54 GMT https://sukruyusufkaya.com/en/training/ai-agent-memory-sistemleri-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/ai-agent-memory-sistemleri-muhendisligi-egitimi This training is designed to teach end to end — in Turkish — the agent-memory discipline that forms the foundational building block of the stateful AI agent paradigm: an agent that remembers its user, learns from conversation history, and sustains long-term context, going beyond classical stateless LLM API calls. The 2024-2026 period witnessed the birth of the agent-memory ecosystem: Letta (formerly MemGPT, Berkeley 2023, virtual context pagination), Mem0 (YC W24, hybrid memory layer, 25K+ GitHub stars), Zep (Series A in 2024, temporal knowledge graph), Graphiti (Zep's open-source graph engine), Cognee (GraphRAG + memory hybrid), LangChain LangMem (native memory primitives), OpenAI Memory (ChatGPT cross-conversation), Claude Projects + Anthropic Memory Tool API (2025), Google Gemini Memory (2025). In Turkey, a training that addresses this discipline end to end at the cognitive taxonomy + framework comparison + retrieval strategy + lifecycle management + production-eval triangle is virtually nonexistent — existing content either stays at short single-tool tutorials or freezes in academic papers. This program is designed to fill that gap as Turkey's most comprehensive production-grade agent-memory reference training.

The program's strategic backbone is the first module, which clarifies the rationale for the transition from the stateless LLM API to the stateful agent paradigm and the rapidly rising 2024-2026 agent-memory ecosystem. The classical LLM API treats every call independently and must stay within the context window limit (8K-1M tokens); the needs of agent products (personal assistant, customer-support bot, sales CRM AI, personal tutor, personal trainer AI) include dimensions that don't fit in the context window — persistent identity, long-term user knowledge, episodic recall, multi-session continuity. The evidence-based answer to 'is just enlarging the context window enough?' is no — even Gemini 2.5 Pro's 10M-token context does not eliminate the… **[Read the full article →](https://sukruyusufkaya.com/en/training/ai-agent-memory-sistemleri-muhendisligi-egitimi)**]]> Tue, 19 May 2026 15:45:43 GMT https://sukruyusufkaya.com/en/training/ai-observability-llm-monitoring-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/ai-observability-llm-monitoring-muhendisligi-egitimi This training is designed to address end to end — in Turkish — AI observability: the discipline of placing generative-AI and LLM applications under observation in production, measuring them, evaluating them, and ensuring their operational sustainability. The 2024-2026 period witnessed the birth and standard-setting race of LLM observability platforms (Langfuse, Arize Phoenix, Helicone, W&B Weave, Braintrust, LangSmith); in the same period, the vendor-agnostic trace standard took shape with OpenTelemetry GenAI Semantic Conventions. In Turkey, a training that addresses this discipline end to end at the math + tool stack + production experience + KVKK compliance triangle is virtually nonexistent — existing content either stays at short single-tool tutorials or freezes from the APM perspective. This program is designed to fill that gap as Turkey's most comprehensive production-grade AI observability reference training.

The program's strategic backbone is the first module, which clarifies how LLM observability differs from the classical APM (Application Performance Monitoring) approach. Details why classical APM solutions like Datadog, New Relic, Dynatrace fall short on LLM applications, and the LLM-specific observability needs like semantic output (non-deterministic, semantic output), hallucination, prompt drift, cost explosion, token-level cost attribution, RAG retrieval quality, and agent tool-selection accuracy. The 4-pillar framework in generative-AI observability (trace + eval + cost + quality drift) is established. The 2026 ecosystem map compares Langfuse (open-source, 13K+ GitHub stars), Arize Phoenix + AX (ML observability tradition), Helicone (proxy-based, YC W23), W&B Weave + Braintrust (eval-first), and LangSmith (LangChain native). The decision framework: open-source vs SaaS vs enterprise hybrid; self-hosted Langfuse vs Helicone vs Phoenix; and selection from the KVKK + EU AI Act + GDPR compliance perspective is presented.

The second… **[Read the full article →](https://sukruyusufkaya.com/en/training/ai-observability-llm-monitoring-muhendisligi-egitimi)**]]> Tue, 19 May 2026 15:34:36 GMT https://sukruyusufkaya.com/en/training/llm-quantization-muhendisligi-egitimi https://sukruyusufkaya.com/en/training/llm-quantization-muhendisligi-egitimi This training is designed to address end to end — with math + algorithms + production stack — the quantization discipline that forms the economic foundation of modern LLM inference. As of 2026, serving a 70B-parameter LLM in FP16 won't fit even on a single H100 (140GB > 80GB); in contrast, with 4-bit quantization the same model can run on a single RTX 4090 (24GB) at 10x lower cost. This dramatic difference has made quantization one of the priorities of production AI engineering. In Turkey, a training that addresses this discipline end to end — from Frantar's GPTQ derivation to the mathematical construction of Lin's AWQ scaling factor, from the SmoothQuant outlier-migration formulation to AQLM additive codebooks, from Hopper FP8 Tensor Cores to Blackwell B200 NVFP4 / MXFP4, from KIVI 2-bit KV cache to reasoning-model long-trace serving — is virtually nonexistent; existing content either stays at shallow tool tutorials or freezes at academic-paper summaries. This program is designed to fill that gap as Turkey's most comprehensive production-grade LLM quantization reference training.

The program's strategic backbone is the first module, which clarifies the cost-quality-throughput trade-off across the quantization spectrum (FP32 → BF16/FP16 → FP8 → INT8 → NF4/INT4 → FP4 → AQLM 1-2 bit). A 70B model's memory footprint is 140GB in FP16, 70GB in INT8, 35GB in INT4/NF4, 17.5GB in NVFP4, and 4GB in AQLM 2-bit; this difference produces not only memory but also a 2-8x throughput gain. Hopper H100/H200's FP8 (E4M3 + E5M2) native Tensor Cores and Blackwell B200/GB200's NVFP4 + MXFP4 Transformer Engine v2 support form the hardware foundation of the 2024-2026 industry transformation; AMD MI325X/MI355X FP8/FP4, Intel Gaudi 3, Google TPU v6/v7 (Trillium) joined this race as well. Decision framework: for production cost optimization, the $0.30/M output token vs $3/M comparison, the quality regression budget (is 0.5% MMLU loss tolerable?), and which bit-width is the right… **[Read the full article →](https://sukruyusufkaya.com/en/training/llm-quantization-muhendisligi-egitimi)**]]> Tue, 19 May 2026 15:18:29 GMT