Sıla Ermut
Sıla ist Branchenanalystin bei AIMultiple und spezialisiert auf E-Mail-Marketing und Vertriebsvideos.
Forschungsschwerpunkte
Sılas Forschungsschwerpunkte umfassen E-Mail-Marketing, E-Commerce-Marketingkampagnen und Marketingautomatisierung. Sie ist außerdem Teil des AIMultiple-Projekts zur E-Mail-Zustellbarkeits-Benchmark-Analyse. In Zusammenarbeit mit dem Technologie-Team von AIMultiple entwickelt und implementiert sie Benchmarks zur E-Mail-Zustellbarkeit.Berufserfahrung
Sıla arbeitete zuvor als Personalvermittlerin und war in Projektmanagement- und Beratungsunternehmen tätig.Ausbildung
Sie hält:- Bachelor of Arts-Abschluss in Internationalen Beziehungen von der Bilkent-Universität.
- Master of Science-Abschluss in Sozialpsychologie von der Başkent-Universität.
Neueste Artikel von Sıla
Federated Learning: 7 Anwendungsfälle & Beispiele
According to recent McKinsey analyses, the most pressing risks of AI adoption include model hallucinations, data provenance and authenticity, regulatory non-compliance, and AI supply chain vulnerabilities. Federated learning (FL) has emerged as a foundational technique for organizations seeking to mitigate these risks.
10+ Beispiele für große Sprachmodelle & Benchmark
We have used open-source benchmarks to compare top proprietary and open-source large language model examples. You can choose your use case to find the right model. Comparison of the most popular large language models We have developed a model scoring system based on three key metrics: user preference, coding, and reliability.
Die 15 wichtigsten Anwendungsfälle und Beispiele für KI in der Logistik
Persistent inefficiencies, rising operational costs, and ongoing supply chain disruptions continue to challenge logistics functions globally. These pressures are straining traditional systems, reducing service reliability, and limiting organizations’ ability to scale. In response, companies are increasingly turning to artificial intelligence to enhance end-to-end visibility, strengthen resilience, and optimize core functions.
Zeitreihen-Fundamentmodelle: Anwendungsfälle & Vorteile
Time series foundation models (TSFMs) build on advances in foundation models from natural language processing and vision. Using transformer-based architectures and large-scale training data, they achieve zero-shot performance and adapt across sectors such as finance, retail, energy, and healthcare.
Große Weltmodelle: Anwendungsfälle & Beispiele
Despite advances in large language models, artificial intelligence remains limited in its ability to understand and interact with the physical world due to the constraints of text-based representations. Large world models address this gap by integrating multimodal data to reason about actions, model real-world dynamics, and predict environmental changes.
LLM Observability-Tools: Weights & Biases, Langsmith
LLM-based applications are becoming more capable and increasingly complex, making their behavior harder to interpret. Each model output results from prompts, tool interactions, retrieval steps, and probabilistic reasoning that cannot be directly inspected. LLM observability addresses this challenge by providing continuous visibility into how models operate in real-world conditions.
Top 5 KI-Dienste zur Steigerung der Geschäftseffizienz
AI adoption is rapidly increasing. Around 98% of companies are experimenting with AI, reflecting its growing accessibility and potential to improve operations. Yet only 26% have advanced beyond trials to achieve measurable business value, showing that many are still building the capabilities needed to scale AI effectively.
Text-to-Video-Generator-Benchmark
A text-to-video generator is an AI system that turns written prompts into short videos by generating visuals, motion, and sometimes audio directly from natural language.
Tools zur Erkennung von KI-Halluzinationen: W&B Weave & Comet
We benchmarked three hallucination detection tools: Weights & Biases (W&B) Weave HallucinationFree Scorer, Arize Phoenix HallucinationEvaluator, and Comet Opik Hallucination Metric, across 100 test cases. Each tool was evaluated on accuracy, precision, recall, and latency to provide a fair comparison of their real-world performance.
57 Datensätze für ML- & AI-Modelle
Data is required to leverage or build generative AI or conversational AI solutions. You can use existing datasets available on the market or hire a data collection service. We identified 57 datasets to train and evaluate machine learning and AI models.
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