Multimodal AI Training in Tashkent

This instructor-led, live training in Tashkent (online or on-site) is designed for intermediate to advanced-level AI researchers, developers, and data scientists seeking to harness DeepSeek's multimodal capabilities for cross-modal learning, AI automation, and enhanced 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 to generate richer insights.
• Optimize and fine-tune DeepSeek models for effective cross-modal learning.
• Apply multimodal AI techniques to real-world industry use cases.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate to advanced AI developers, researchers, and multimedia engineers looking to build AI agents that can understand and generate 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 like GPT-4 Vision and Whisper ASR.
• Optimize multi-modal AI pipelines for better efficiency and accuracy.
• Deploy multi-modal AI agents in real-world applications.

This instructor-led, live training in Tashkent (available online or on-site) is designed for advanced-level AI professionals seeking to enhance their prompt engineering capabilities for multimodal AI applications.

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

• Understand the fundamentals of multimodal AI and its practical applications.
• Design and optimise prompts for generating text, images, audio, and video.
• Utilise APIs for multimodal AI platforms such as GPT-4, Gemini, and DeepSeek-Vision.
• Develop AI-driven workflows that integrate multiple content formats.

This instructor-led, live training in Tashkent (online or on-site) is designed for advanced-level AI developers, machine learning engineers, and researchers who aim to build custom multimodal AI models using open-source frameworks.

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

• Understand the fundamentals of multimodal learning and data fusion.
• Implement multimodal models using DeepSeek, OpenAI, Hugging Face, and PyTorch.
• Optimize and fine-tune models for text, image, and audio integration.
• Deploy multimodal AI models in real-world applications.

This instructor-led, live training in Tashkent (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 Tashkent (online or on-site) is designed for intermediate-level linguists, AI researchers, software developers, and business professionals who aim to leverage multimodal AI for real-time translation and language understanding.

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

• Grasp the fundamentals of multimodal AI in language processing.
• Utilise 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.
• Analyse ethical considerations in AI-powered language processing.

This instructor-led, live training in Tashkent (online or on-site) is designed for beginner to intermediate-level product designers, software engineers, and customer support professionals who aim to enrich 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 into AI-powered assistants.
• Build interactive conversational agents with voice and vision capabilities.
• Utilise APIs for speech recognition, NLP, and computer vision.
• Implement AI-driven automation for customer support and user interaction.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level to advanced-level healthcare professionals, medical researchers, and AI developers who wish to apply multimodal AI in medical diagnostics and healthcare applications.

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

• Understand the role of multimodal AI in modern healthcare.
• Integrate structured and unstructured medical data for AI-driven diagnostics.
• Apply AI techniques to analyse medical images and electronic health records.
• Develop predictive models for disease diagnosis and treatment recommendations.
• Implement speech and natural language processing (NLP) for medical transcription and patient interaction.

Vertex AI offers robust tools for creating multimodal LLM workflows that seamlessly integrate text, audio, and image data into a unified pipeline. Leveraging long context window capabilities and Gemini API parameters, it facilitates the development of sophisticated applications focused on 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 to handle multimodal inputs and outputs.
• Implement long-context workflows to tackle complex reasoning tasks.
• Design pipelines that effectively combine text, audio, and image analysis.
• Optimize Gemini API parameters to enhance performance and cost-efficiency.

Course Format

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

Course Customization Options

• To request a customized version of this training, please contact us to arrange.

This instructor-led, live training in Tashkent (online or onsite) is aimed at intermediate-level finance professionals, data analysts, risk managers, and AI engineers who wish to leverage multimodal AI for risk analysis and fraud detection.

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

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

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

By the end 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 Tashkent (online or onsite) is aimed at intermediate-level content creators, digital artists and media professionals who wish to learn how multimodal AI can be applied to various forms of content creation.

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

• Use AI tools to enhance music and video production.
• Generate unique visual art and designs with AI.
• Create interactive multimedia experiences.
• Understand the impact of AI on the creative industries.

This instructor-led, live training in Tashkent (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI for integrating various sensory data to create more autonomous and efficient robots that can see, hear, and touch.

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

• Implement multimodal sensing in robotic systems.
• Develop AI algorithms for sensor fusion and decision-making.
• Create robots that can perform complex tasks in dynamic environments.
• Address challenges in real-time data processing and actuation.

This instructor-led, live training in Tashkent (online or onsite) is designed for intermediate-level UX/UI designers and front-end developers who want to leverage Multimodal AI to create user interfaces capable of understanding and processing various types of input.

Upon completing this training, participants will be able to:

• Design multimodal interfaces that enhance user engagement.
• Incorporate voice and visual recognition into web and mobile applications.
• Use multimodal data to develop adaptive and responsive UIs.
• Grasp the ethical implications of user data collection and processing.

This instructor-led, live training in Tashkent (online or onsite) is aimed at 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 core principles of multimodal AI and its practical applications.
• Implement data fusion techniques to integrate different types of data.
• Build and train models capable of processing visual, textual, and auditory information.
• Evaluate the performance of multimodal AI systems.
• Address ethical and privacy concerns related to multimodal data.