Multimodal AI Training in Patras

This instructor-led, live training in Patras (online or onsite) is aimed at intermediate-level to advanced-level AI researchers, developers, and data scientists who wish to leverage DeepSeek’s multimodal capabilities for cross-modal learning, AI automation, and advanced decision-making.

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

• Implement DeepSeek’s multimodal AI for text, image, and audio applications.
• Develop AI solutions that integrate multiple data types for richer insights.
• Optimize and fine-tune DeepSeek models for cross-modal learning.
• Apply multimodal AI techniques to real-world industry use cases.

This instructor-led, live training in Patras (online or onsite) is designed for intermediate to advanced AI developers, researchers, and multimedia engineers who aim to construct AI agents capable of understanding and generating multi-modal content.

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

• Create AI agents that process and integrate text, image, and speech data.
• Implement multi-modal models such as GPT-4 Vision and Whisper ASR.
• Optimize multi-modal AI pipelines for enhanced efficiency and accuracy.
• Deploy multi-modal AI agents in real-world applications.

This instructor-led, live training in Patras (online or onsite) is designed for advanced AI professionals looking to improve their prompt engineering skills for multimodal AI applications.

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

• Grasp the fundamentals of multimodal AI and its practical applications.
• Design and refine prompts for generating text, images, audio, and video.
• Leverage APIs for multimodal AI platforms like GPT-4, Gemini, and DeepSeek-Vision.
• Create AI-driven workflows that integrate multiple content formats.

This instructor-led, live training in Patras (online or onsite) is designed for advanced-level AI developers, machine learning engineers, and researchers who aim to construct custom multimodal AI models leveraging open-source frameworks.

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

• Grasp the core principles of multimodal learning and data fusion.
• Build multimodal models utilizing DeepSeek, OpenAI, Hugging Face, and PyTorch.
• Optimize and fine-tune models to effectively integrate text, image, and audio inputs.
• Deploy multimodal AI solutions in practical, real-world scenarios.

This instructor-led, live training in Patras (online or onsite) is aimed at intermediate-level to advanced-level industrial engineers, automation specialists, and AI developers who wish to apply multimodal AI for quality control, predictive maintenance, and robotics in smart factories.

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

• Understand the role of multimodal AI in industrial automation.
• Integrate sensor data, image recognition, and real-time monitoring for smart factories.
• Implement predictive maintenance using AI-driven data analysis.
• Apply computer vision for defect detection and quality assurance.

This instructor-led live training in Patras (online or onsite) is aimed at intermediate-level linguists, AI researchers, software developers, and business professionals who wish to leverage multimodal AI for real-time translation and language understanding.

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

• Understand the fundamentals of multimodal AI for language processing.
• Use AI models to process and translate speech, text, and images.
• Implement real-time translation using AI-powered APIs and frameworks.
• Integrate AI-driven translation into business applications.
• Analyze ethical considerations in AI-powered language processing.

This instructor-led, live training in Patras (online or onsite) is aimed at beginner-level to intermediate-level product designers, software engineers, and customer support professionals who wish to enhance virtual assistants with multimodal AI.

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

• Understand how multimodal AI enhances virtual assistants.
• Integrate speech, text, and image processing in AI-powered assistants.
• Build interactive conversational agents with voice and vision capabilities.
• Utilize APIs for speech recognition, NLP, and computer vision.
• Implement AI-driven automation for customer support and user interaction.

This instructor-led live training in Patras (online or onsite) targets intermediate to advanced healthcare professionals, medical researchers, and AI developers wishing to apply multimodal AI in medical diagnostics and healthcare applications.

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

• Grasp the role of multimodal AI in contemporary healthcare.
• Combine structured and unstructured medical data for AI-driven diagnostics.
• Utilize AI techniques to analyse medical images and electronic health records.
• Create predictive models for disease diagnosis and treatment recommendations.
• Implement speech recognition and natural language processing (NLP) for medical transcription and patient engagement.

Vertex AI offers robust tools for constructing multimodal LLM workflows, seamlessly integrating text, audio, and image data into a unified pipeline. With support for long context windows and Gemini API parameters, it facilitates advanced applications in planning, reasoning, and cross-modal intelligence.

This instructor-led, live training (available online or onsite) is designed for intermediate to advanced practitioners aiming to design, build, and optimize multimodal AI workflows within Vertex AI.

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

• Utilize Gemini models for handling multimodal inputs and outputs.
• Implement long-context workflows to facilitate complex reasoning.
• Design pipelines that effectively integrate text, audio, and image analysis.
• Optimize Gemini API parameters for improved performance and cost efficiency.

Course Format

• Interactive lectures and discussions.
• Practical labs focused on multimodal workflows.
• Project-based exercises targeting applied multimodal use cases.

Customization Options

• To request customized training for this course, please contact us to make arrangements.

This instructor-led, live training in Patras (online or onsite) targets intermediate-level finance professionals, data analysts, risk managers, and AI engineers looking to utilize multimodal AI for risk analysis and fraud detection.

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

• Grasp how multimodal AI is applied in financial risk management.
• Analyze structured and unstructured financial data to detect fraud.
• Implement AI models to identify anomalies and suspicious activities.
• Utilize NLP and computer vision for financial document analysis.
• Deploy AI-driven fraud detection models within real-world financial systems.

This instructor-led, live training in Patras (online or onsite) targets beginner to intermediate-level UI/UX designers, product managers, and AI researchers who want to elevate user experiences through multimodal AI-powered interfaces.

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

• Understand the fundamentals of multimodal AI and its impact on human-computer interaction.
• Design and prototype multimodal interfaces using AI-driven input methods.
• Implement speech recognition, gesture control, and eye-tracking technologies.
• Evaluate the effectiveness and usability of multimodal systems.

This instructor-led, live training in Patras (online or onsite) is designed for intermediate-level content creators, digital artists, and media professionals eager to discover how multimodal AI can be leveraged for various creative outputs.

Upon completing this training, participants will be able to:

• Utilize AI tools to elevate music and video production workflows.
• Produce original visual art and designs using AI technology.
• Develop engaging interactive multimedia experiences.
• Comprehend the influence of AI on the creative sector.

This instructor-led, live training in Patras (online or onsite) targets advanced robotics engineers and AI researchers looking to leverage Multimodal AI. The course focuses on integrating diverse sensory data to build more autonomous and efficient robots capable of seeing, hearing, and touching.

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

• Implement multimodal sensing within robotic systems.
• Develop AI algorithms for sensor fusion and decision-making processes.
• Build robots capable of executing complex tasks in dynamic environments.
• Address challenges related to real-time data processing and actuation.

This instructor-led, live training in Patras (online or onsite) targets intermediate UX/UI designers and front-end developers who wish to use Multimodal AI to design and implement user interfaces capable of understanding and processing various input types.

Upon completing this training, participants will be able to:

• Design multimodal interfaces that boost user engagement.
• Integrate voice and visual recognition into web and mobile applications.
• Leverage multimodal data to develop adaptive and responsive UIs.
• Understand the ethical implications of user data collection and processing.

This instructor-led, live training in Patras (online or onsite) is designed for intermediate-level AI researchers, data scientists, and machine learning engineers who wish to create intelligent systems capable of processing and interpreting multimodal data.

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

• Understand the principles of multimodal AI and its applications.
• Implement data fusion techniques to combine different types of data.
• Build and train models that can process visual, textual, and auditory information.
• Evaluate the performance of multimodal AI systems.
• Address ethical and privacy concerns related to multimodal data.