Preciziranje Training in Belgrade

This instructor-led, live training in Belgrade (online or onsite) is designed for advanced-level defense AI engineers and military technology developers who wish to fine-tune deep learning models for use in autonomous vehicles, drones, and surveillance systems while adhering to strict security and reliability standards.

By the conclusion of this training, participants will be able to:

• Fine-tune computer vision and sensor fusion models for surveillance and targeting tasks.
• Adapt autonomous AI systems to changing environments and mission profiles.
• Implement robust validation and fail-safe mechanisms in model pipelines.
• Ensure alignment with defense-specific compliance, safety, and security standards.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level legal tech engineers and AI developers who wish to fine-tune language models for tasks like contract analysis, clause extraction, and automated legal research in legal service environments.

By the end of this training, participants will be able to:

• Prepare and clean legal documents for fine-tuning NLP models.
• Apply fine-tuning strategies to improve model accuracy on legal tasks.
• Deploy models to assist with contract review, classification, and research.
• Ensure compliance, auditability, and traceability of AI outputs in legal contexts.

Ova obuka sa instruktom, koja se odvija uživo na lokaciji < (online ili na licu mesta), namenjena je medicinskim developerima veštačke inteligencije i naučnicima na podacima srednjeg do naprednog nivoa znanja, koji žele da usavršavaju modele za kliničku dijagnostiku, predviđanje bolesti i prognozu ishoda kod pacijenata, koristeći strukturirane i nestrukturisane medicinske podatke.

Na kraju ove obuke, učesnici će moći da:

• Usavršavaju modele veštačke inteligencije na zdravstvenim skupovima podataka, uključujući EMR, snimke i podatke vremenskih serija.
• Primenjuju transfer learning, adaptaciju na domen i kompresiju modela u medicinskom kontekstu.
• Rešavaju pitanja privatnosti, pristrasnosti (bias) i usklađenosti sa propisima u razvoju modela.
• Implementiraju i prate usavršene modele u stvarnom zdravstvenom okruženju.

Ovaj obuci koju vodi predavač u Belgrade (online ili licno) namenjena je naprednim naučnicima za podatke i AI inženjerima u finansijskom sektoru koji žele da prilagode modele za primene kao što su kreditno ocenjivanje, detekcija prevare i modeliranje rizika, koristeći specifične finansijske podatke domene.

Do kraja ove obuke, učesnici će moći da:

• Fine-tune-uju AI modele na finansijskim skupovima podataka radi poboljšane predikcije prevare i rizika.
• Primene tehnike kao što su transfer learning (prenos znanja), LoRA i regularizacija radi poboljšanja efikasnosti modela.
• Integrišu finansijska razmatranja usaglašenosti (compliance) u tok rada AI modeliranja.
• Razviju (deploy) prilagođene modele za produkcijsku upotrebu na platformama finansijskih usluga.

This instructor-led, live training in Belgrade (online or onsite) is aimed at advanced-level AI maintenance engineers and MLOps professionals who wish to implement robust continual learning pipelines and effective update strategies for deployed, fine-tuned models.

By the end of this training, participants will be able to:

• Design and implement continual learning workflows for deployed models.
• Mitigate catastrophic forgetting through proper training and memory management.
• Automate monitoring and update triggers based on model drift or data changes.
• Integrate model update strategies into existing CI/CD and MLOps pipelines.

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate-level embedded AI developers and edge computing specialists aiming to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices.

Upon completion of this training, participants will be capable of:

• Identifying and adapting pre-trained models appropriate for edge deployment.
• Utilizing quantization, pruning, and other compression methods to minimize model size and reduce latency.
• Fine-tuning models through transfer learning to achieve task-specific performance.
• Deploying optimized models on actual edge hardware platforms.

This instructor-led, live training in Belgrade (online or onsite) is aimed at advanced-level computer vision engineers and AI developers who wish to fine-tune VLMs such as CLIP and Flamingo to improve performance on industry-specific visual-text tasks.

By the end of this training, participants will be able to:

• Understand the architecture and pretraining methods of vision-language models.
• Fine-tune VLMs for classification, retrieval, captioning, or multimodal QA.
• Prepare datasets and apply PEFT strategies to reduce resource usage.
• Evaluate and deploy customized VLMs in production environments.

Ova instruktorska, uživo obuka u Belgrade (online ili na lokaciji klijenta) namenjena je inženjerima mašinskog učenja srednjeg nivoa i profesionalcima za usklađenost sa AI standardima koji žele da identifikuju, procene i smanje rizike po sigurnost i pristrasnost u jezičkim modelima nakon fine-tuninga.

Nakon završetka ove obuke, učesnici će biti u mogućnosti da:

• Razumeju etički i regulatorni kontekst za bezbedne AI sisteme.
• Identifikuju i procene uobičajene oblike pristrasnosti u modelima nakon fine-tuninga.
• Primenjuju tehnike ublažavanja pristrasnosti tokom i nakon obuke modela.
• Dizajniraju i audiraju modele radi obezbeđenja sigurnosti, transparentnosti i pravičnosti.

Ova obuka sa predavačem uživo u <lokalizaciji > (onlajn ili na lokaciji) namenjena je inženjerima NLP-a i timovima za upravljanje znanjem srednjeg nivoa koji žele da usavre RAG tokove radi poboljšanja performansi u slučajevima upotrebe kao što su odgovaranje na pitanja, pretraga u preduzećima i zbirke.

Krajem ove obuke, učesnici će moći da:

• Razumeju arhitekturu i tok rada RAG sistema.
• Usavre retriever i generator komponente za domen-specifične podatke.
• Procene performansu RAG-a i primene poboljšanja kroz PEFT tehnike.
• Deployuju optimizovane RAG sisteme za unutrašnju ili produkcijsku upotrebu.

This instructor-led, live training in Belgrade (online or onsite) is designed for intermediate-level ML practitioners and AI developers aiming to fine-tune and deploy open-weight models such as LLaMA, Mistral, and Qwen for specific business or internal applications.

By the end of this training, participants will be able to:

• Understand the open-source LLM ecosystem and key differences between models.
• Prepare datasets and fine-tuning configurations for models like LLaMA, Mistral, and Qwen.
• Execute fine-tuning pipelines using Hugging Face Transformers and PEFT.
• Evaluate, save, and deploy fine-tuned models in secure environments.

Ova obuka sa instruktorom uživo u Belgrade (online ili na lokaciji) namenjena je data scientistima i AI inženjerima srednjeg nivoa koji žele da prilagode velike jezičke modele povoljnije i efikasnije koristeći metode poput LoRA, Adapter Tuning-a i Prefix Tuning-a.

Na kraju ove obuke, učesnici će moći da:

• Razumeju teoriju iza pristupa efikasnog prilagođavanja parametara.
• Implementiraju LoRA, Adapter Tuning i Prefix Tuning koristeći Hugging Face PEFT.
• Uporedne perfomaanse i troškove PEFT metoda u odnosu na kompletno prilagođavanje.
• Implantiraju i skaliraju prilagođene LLM-ove sa smanjenim zahtevima za izračunavanje i skladištenjem.

This instructor-led, live training in Belgrade (online or onsite) targets intermediate to advanced machine learning engineers, AI developers, and data scientists seeking to learn how to utilize QLoRA for the efficient fine-tuning of large models tailored to specific tasks.

By the conclusion of this training, participants will be capable of:

• Understanding the theory behind QLoRA and quantization techniques for LLMs.
• Implementing QLoRA to fine-tune large language models for domain-specific applications.
• Optimizing fine-tuning performance on limited computational resources using quantization.
• Deploying and evaluating fine-tuned models efficiently in real-world applications.

This live, instructor-led training in Belgrade (online or onsite) targets advanced machine learning engineers and AI researchers aiming to utilize RLHF for the fine-tuning of large AI models to achieve enhanced performance, safety, and alignment.

By the end of this training, participants will be able to:

• Comprehend the theoretical underpinnings of RLHF and its significance in modern AI advancement.
• Construct reward models based on human input to guide reinforcement learning cycles.
• Refine large language models using RLHF techniques to align results with human expectations.
• Apply best practices for scaling RLHF processes in production-grade AI environments.

This live, instructor-led training module, conducted either online or on-site in Belgrade, is tailored for mid-level professionals looking to acquire practical skills in adapting AI models for essential financial operations.

Upon finishing the program, participants will be equipped to:

• Comprehend the foundational aspects of fine-tuning AI for financial use cases.
• Leverage pre-trained models to handle specific financial workflows.
• Apply specialized methods for fraud detection, risk analysis, and the generation of financial advice.
• Maintain strict compliance with regulations such as GDPR and SOX.
• Integrate comprehensive data security and ethical AI practices into financial systems.

This live, instructor-led training in Belgrade (online or onsite) is designed for advanced professionals who aim to sharpen their skills in diagnosing and solving fine-tuning challenges for machine learning models.

Upon completion of this training, participants will be able to:

• Identify problems such as overfitting, underfitting, and data imbalance.
• Apply strategies to enhance model convergence.
• Enhance fine-tuning pipelines for superior performance.
• Troubleshoot training processes using practical tools and techniques.

This instructor-led, live training in Belgrade (online or onsite) targets advanced professionals who wish to master techniques for optimizing large models for cost-effective fine-tuning in real-world scenarios.

By the end of this training, participants will be able to:

• Grasp the challenges associated with fine-tuning large models.
• Implement distributed training techniques for large models.
• Utilize model quantization and pruning for improved efficiency.
• Optimize hardware usage for fine-tuning tasks.
• Effectively deploy fine-tuned models in production environments.

Ovo instruktorsko, uživo obučavanje u Belgrade (online ili na lokaciji) namenjeno je profesionalima srednjeg nivoa koji žele da iskoriste snagu inženjeringa promptova i učenja u nekoliko koraka da optimizuju performanse LLM modela za primenu u stvarnom svetu.

Do kraja ovog treninga, učesnici će moći da:

• Razumeju principe inženjeringa promptova i učenja u nekoliko koraka.
• Dizajniraju efikasne promptove za različite NLP zadatke.
• Iskoriste tehnike few-shot prilagodbom LLM modela uz minimalne količine podataka.
• Optimizuju performanse LLM modela za praktične primene.

Ova obuka koju vodi instruktur, održava se uživo u Belgrade (online ili na lokaciji klijenta) i namenjena je profesionalima naprednog nivoa koji žele da savladaju tehniku prilagođavanja multimodalnih modela za inovativna rešenja veštačke inteligencije.

Krajem ove obuke, polaznici će moći da:

• razume arhitekturu multimodalnih modela poput CLIP i Flamingo.
• efikasno pripreme i predobrade multimodalne skupove podataka.
• prilagode multimodalne modele za specifične zadatke.
• optimizuju modele za primenu u stvarnom svetu i poboljšanje performansi.

Ovo obuka sa instruktora, uživo u Belgrade (online ili uživo), namenjena je istraživačima veštačke inteligencije naprednog nivoa, inženjerima mašinskog učenja i razvojnim programerima koji žele da fino-nalaze DeepSeek LLM modele kako bi kreirali specijalizovane AI aplikacije prilagođene određenim industrijskim domenima, poslovima ili potrebama.

Na kraju ove obuke, učesnici će moći da:

• Razumeju arhitekturu i mogućnosti modela DeepSeek, uključujući DeepSeek-R1 i DeepSeek-V3.
• Pripreme datasetske i preprave podatke za fino-nalađivanje.
• Fino-nalaze DeepSeek LLM za domen-specific aplikacije.
• Optimizuju i implementiraju fino-nalažene modele efikasno.

This instructor-led, live training in Belgrade (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently.

By the end of this training, participants will be able to:

• Understand the challenges of deploying fine-tuned models into production.
• Containerize and deploy models using tools like Docker and Kubernetes.
• Implement monitoring and logging for deployed models.
• Optimize models for latency and scalability in real-world scenarios.

Ovo vođeno, uživo obuku na <lokalizaciji> (online ili uživo) namenjeno je profesionalcima u mašinskom učenju naprednog nivoa koji žele da savladaju najnovije tehnike prenosa znanja i primene ih na kompleksne probleme iz stvarnog sveta.

Na kraju ove obuke, učesnici će moći da:

• Razumeju napredne koncepte i metodologije u prenosu znanja.
• Implementiraju tehnike prilagodbe specifičnim domenima za prethodno obučene modele.
• Primene kontinuirano učenje za upravljanje promenljivim zadacima i skupovima podataka.
• Savladaju fine-tuning za više zadataka radi poboljšanja performansi modela preko različitih zadataka.

This instructor-led training in Belgrade delves into advanced fine-tuning and prompt management within Vertex AI. It empowers intermediate and advanced practitioners to optimize Gemini models, implement stringent evaluation workflows, and deploy reliable generative AI solutions suited for real-world enterprise environments.

This instructor-led, live session in Belgrade (available online or on-site) is intended for beginner to intermediate machine learning professionals who aim to understand and apply transfer learning techniques to enhance efficiency and performance in AI projects.

By the conclusion of this training, participants will be able to:

• Understand the core principles and advantages of transfer learning.
• Explore popular pre-trained models and their respective applications.
• Execute fine-tuning of pre-trained models for bespoke tasks.
• Apply transfer learning solutions to real-world issues in NLP and computer vision.

Ovo instruktur-vodjeno, uživo obuke u Belgrade (online ili na lokaciji) namenjene su razvojcima i AI praktikama srednjeg nivoa koji žele da implementiraju strategije fino podešavanja velikih modela bez potrebe za značajnim računovskim resursima.

Nakon završetka ove obuke, učesnici će moći da:

• razumeju principe Low-Rank Adaptation (LoRA) tehnike.
• implementiraju LoRA radi efikasnog fino podešavanja velikih modela.
• optimizuju proces fino podešavanja za okruženja sa ograničenim resursima.
• procenjuju i razvrtaju modele podešene pomoću LoRA za praktičnu primenu.

Ova trainerska obuka koju vodi instruktora uživo u Belgrade (onlajn ili na lokaciji) namenjena je profesionalcima srednjeg i naprednog nivoa koji žele da prilagode predtreningovane modele za specifične zadatke i skupove podataka.

Na kraju ove obuke, učesnici će moći da:

• razumeju principe fino-tuniniga i njegove primene.
• prepričaju skupove podataka za fino-tuning predtreningovanih modela.
• provedu fino-tuning velikih jezičkih modela (LLM) za zadatake iz oblasti NLP.
• optimizuju performanse modela i rešavaju uobičajene izazove.

This instructor-led, live training in Belgrade (online or onsite) is aimed at intermediate-level professionals who wish to enhance their NLP projects through the effective fine-tuning of pre-trained language models.

By the end of this training, participants will be able to:

• Grasp the core concepts behind fine-tuning for NLP applications.
• Apply fine-tuning techniques to pre-trained models like GPT, BERT, and T5 for targeted NLP use cases.
• Adjust hyperparameters to boost model effectiveness.
• Assess and implement fine-tuned models in practical, real-world settings.