Fine-Tuning Training in Los Angeles

This instructor-led, live training (conducted online or on-site) is aimed at advanced-level defense AI engineers and military technology developers who seek to refine deep learning models for use in autonomous vehicles, drones, and surveillance systems while adhering to stringent security and reliability standards. By the end of this training, participants will be able to: - Refine computer vision and sensor fusion models for surveillance and targeting tasks. - Adapt autonomous AI systems to dynamic environments and varying mission profiles. - Implement robust validation and fail-safe mechanisms in model pipelines. - Ensure alignment with defense-specific compliance, safety, and security standards for government.

This instructor-led, live training (online or onsite) is aimed at intermediate-level legal technology engineers and artificial intelligence developers who wish to fine-tune language models for tasks such as 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 natural language processing (NLP) models. - Apply fine-tuning strategies to enhance model accuracy on legal tasks. - Deploy models to support contract review, classification, and research. - Ensure compliance, auditability, and traceability of AI outputs in legal contexts for government.

This instructor-led, live training (online or onsite) is designed for intermediate to advanced medical AI developers and data scientists who aim to refine models for clinical diagnosis, disease prediction, and patient outcome forecasting using structured and unstructured medical data.

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

• Fine-tune AI models on healthcare datasets, including electronic medical records (EMRs), imaging, and time-series data.
• Apply techniques such as transfer learning, domain adaptation, and model compression in medical contexts.
• Address privacy concerns, bias mitigation, and regulatory compliance in the development of AI models for government and healthcare settings.
• Deploy and monitor fine-tuned models in real-world healthcare environments to ensure effective and ethical use.

This instructor-led, live training (online or onsite) is designed for advanced-level data scientists and AI engineers in the financial sector who aim to optimize models for applications such as credit scoring, fraud detection, and risk modeling using domain-specific financial data. By the end of this training, participants will be able to: - Refine AI models on financial datasets to enhance fraud and risk prediction. - Implement techniques such as transfer learning, LoRA, and regularization to improve model efficiency. - Incorporate financial compliance considerations into the AI modeling process for government and private sector use. - Deploy fine-tuned models for production in financial services platforms.

This instructor-led, live training in Los Angeles (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 effective 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 for government.

This instructor-led, live training (conducted online or onsite) is aimed at intermediate-level embedded AI developers and edge computing specialists who wish to fine-tune and optimize lightweight AI models for deployment on resource-constrained devices. By the end of this training, participants will be able to: - Select and adapt pre-trained models suitable for edge deployment. - Apply quantization, pruning, and other compression techniques to reduce model size and latency. - Fine-tune models using transfer learning to enhance task-specific performance. - Deploy optimized models on real edge hardware platforms, ensuring alignment with public sector workflows and governance standards for government.

This instructor-led, live training (online or onsite) is designed for advanced-level computer vision engineers and AI developers who aim to fine-tune vision-language models (VLMs) such as CLIP and Flamingo to enhance performance on industry-specific visual-text tasks. By the end of this training, participants will be able to: - Understand the architecture and pretraining methodologies of vision-language models. - Fine-tune VLMs for tasks including classification, retrieval, captioning, or multimodal question answering. - Prepare datasets and implement parameter-efficient fine-tuning (PEFT) strategies to optimize resource usage. - Evaluate and deploy customized VLMs in production environments, ensuring alignment with public sector workflows and governance standards for government applications.

This instructor-led, live training (available online or onsite) is designed for intermediate-level machine learning engineers and AI compliance professionals who aim to identify, evaluate, and mitigate safety risks and biases in fine-tuned language models. By the end of this training, participants will be able to: - Understand the ethical and regulatory context for safe AI systems for government. - Identify and evaluate common forms of bias in fine-tuned models. - Apply bias mitigation techniques during and after training. - Design and audit models for safety, transparency, and fairness.

This instructor-led, live training in Los Angeles (online or onsite) is aimed at intermediate-level NLP engineers and knowledge management teams who wish to fine-tune RAG pipelines to enhance performance in question answering, enterprise search, and summarization use cases for government. By the end of this training, participants will be able to: - Understand the architecture and workflow of RAG systems. - Fine-tune retriever and generator components for domain-specific data relevant to public sector needs. - Evaluate RAG performance and apply improvements through PEFT techniques. - Deploy optimized RAG systems for internal or production use within government agencies.

This instructor-led, live training (online or onsite) is designed for intermediate-level machine learning practitioners and artificial intelligence developers who aim to refine 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 ecosystem and differences among open-source large language models. - Prepare datasets and fine-tuning configurations suitable 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, ensuring compliance with standards for government use.

This instructor-led, live training (online or onsite) is designed for intermediate-level data scientists and AI engineers who aim to refine large language models more cost-effectively and efficiently using techniques such as LoRA, Adapter Tuning, and Prefix Tuning. By the end of this training, participants will be able to: - Understand the theoretical foundations of parameter-efficient fine-tuning methods. - Implement LoRA, Adapter Tuning, and Prefix Tuning using Hugging Face PEFT for government applications. - Evaluate the performance and cost trade-offs of PEFT methods compared to full fine-tuning. - Deploy and scale fine-tuned LLMs with minimized compute and storage requirements.

This instructor-led, live training (online or onsite) is aimed at intermediate to advanced machine learning engineers, AI developers, and data scientists who wish to learn how to use QLoRA to efficiently fine-tune large models for specific tasks and customizations. By the end of this training, participants will be able to: - Understand the theory behind QLoRA and quantization techniques for large language models. - Implement QLoRA in fine-tuning large language models for domain-specific applications. - Optimize fine-tuning performance on limited computational resources using quantization methods. - Deploy and evaluate fine-tuned models efficiently in real-world scenarios, ensuring alignment with public sector workflows and governance standards for government.

This instructor-led, live training (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to apply Reinforcement Learning from Human Feedback (RLHF) to fine-tune large AI models for superior performance, safety, and alignment. By the end of this training, participants will be able to: - Understand the theoretical foundations of RLHF and its importance in modern AI development. - Implement reward models based on human feedback to guide reinforcement learning processes. - Fine-tune large language models using RLHF techniques to align outputs with human preferences. - Apply best practices for scaling RLHF workflows to support production-grade AI systems for government.

This instructor-led, live training (online or onsite) is designed for intermediate-level professionals who wish to develop practical skills in customizing artificial intelligence models for critical financial tasks. By the end of this training, participants will be able to: - Understand the foundational principles of fine-tuning AI models for finance applications. - Utilize pre-trained models for domain-specific tasks within the financial sector. - Apply techniques for fraud detection, risk assessment, and the generation of financial advice. - Ensure compliance with financial regulations such as GDPR and SOX. - Implement robust data security and ethical AI practices in financial applications. This training is tailored to align with the needs and standards required for government agencies, ensuring that participants are well-equipped to handle sensitive financial information and adhere to regulatory requirements.

This instructor-led, live training in Los Angeles (online or onsite) is designed for advanced-level professionals who wish to enhance their skills in diagnosing and addressing fine-tuning challenges for machine learning models. By the end of this training, participants will be able to: - Identify issues such as overfitting, underfitting, and data imbalance. - Implement strategies to improve model convergence. - Optimize fine-tuning pipelines for enhanced performance. - Debug training processes using practical tools and techniques, ensuring effective solutions for government applications.

This instructor-led, live training (offered online or on-site) is designed for advanced-level professionals who aim to master techniques for optimizing large models for cost-effective fine-tuning in real-world applications. By the end of this training, participants will be able to: - Understand the challenges associated with fine-tuning large models. - Apply distributed training methods to enhance the performance of large models. - Utilize model quantization and pruning to improve efficiency. - Optimize hardware usage for fine-tuning tasks. - Effectively deploy fine-tuned models in production environments, ensuring alignment with public sector workflows and governance standards for government.

This instructor-led, live training (conducted online or onsite) is designed for intermediate-level professionals who aim to harness the capabilities of prompt engineering and few-shot learning to enhance the performance of Large Language Models (LLMs) for real-world applications. By the end of this training, participants will be able to: - Understand the foundational principles of prompt engineering and few-shot learning. - Develop effective prompts for a variety of natural language processing tasks. - Utilize few-shot techniques to adapt LLMs with minimal data requirements. - Optimize LLM performance to meet practical needs, ensuring alignment with public sector workflows and governance standards for government.

This instructor-led, live training in [location] (online or onsite) is aimed at advanced-level professionals who wish to master multimodal model fine-tuning for innovative AI solutions for government. By the end of this training, participants will be able to: - Understand the architecture of multimodal models such as CLIP and Flamingo. - Prepare and preprocess multimodal datasets effectively. - Fine-tune multimodal models for specific tasks. - Optimize models for real-world applications and performance.

This instructor-led, live training (available online or onsite) is designed for advanced-level AI researchers, machine learning engineers, and developers who aim to fine-tune DeepSeek LLM models to develop specialized AI applications tailored to specific industries, domains, or business needs. By the end of this training, participants will be able to: - Understand the architecture and capabilities of DeepSeek models, including DeepSeek-R1 and DeepSeek-V3. - Prepare datasets and preprocess data for fine-tuning. - Fine-tune DeepSeek LLM models for domain-specific applications. - Optimize and deploy fine-tuned models efficiently. This training is tailored to enhance the skills necessary for government agencies to leverage advanced AI technologies effectively, ensuring alignment with public sector workflows, governance, and accountability.

This instructor-led, live training (online or onsite) is aimed at advanced-level professionals who wish to deploy fine-tuned models reliably and efficiently for government applications. By the end of this training, participants will be able to: - Understand the challenges associated with deploying fine-tuned models into production environments. - Containerize and deploy models using tools such as Docker and Kubernetes. - Implement monitoring and logging mechanisms for deployed models to ensure ongoing performance and compliance. - Optimize models for latency and scalability in real-world scenarios, aligning with public sector workflows and governance requirements.

This instructor-led, live training (available online or onsite) is designed for advanced-level machine learning professionals who seek to master cutting-edge transfer learning techniques and apply them to complex real-world challenges. By the end of this training, participants will be able to: - Understand advanced concepts and methodologies in transfer learning. - Implement domain-specific adaptation techniques for pre-trained models. - Apply continual learning strategies to manage evolving tasks and datasets. - Master multi-task fine-tuning to improve model performance across various tasks. This training is tailored to enhance the skills of professionals working in technical roles, ensuring they are equipped with the knowledge necessary to advance their capabilities for government and private sector applications.

Vertex AI provides advanced tools for fine-tuning large models and managing prompts, enabling developers and data teams to enhance model accuracy, streamline iteration workflows, and ensure rigorous evaluation with built-in libraries and services. This instructor-led, live training (online or onsite) is designed for intermediate to advanced practitioners who aim to improve the performance and reliability of generative AI applications using supervised fine-tuning, prompt versioning, and evaluation services in Vertex AI for government. By the end of this training, participants will be able to: - Apply supervised fine-tuning techniques to Gemini models in Vertex AI. - Implement prompt management workflows, including versioning and testing. - Utilize evaluation libraries to benchmark and optimize AI performance. - Deploy and monitor enhanced models in production environments. **Format of the Course** - Interactive lecture and discussion. - Hands-on labs with Vertex AI fine-tuning and prompt tools. - Case studies of enterprise model optimization for government. **Course Customization Options** - To request a customized training for this course, please contact us to arrange.

This instructor-led, live training (online or onsite) is designed for government machine learning professionals at the beginner to intermediate level who wish to understand and apply transfer learning techniques to enhance efficiency and performance in AI projects for government. By the end of this training, participants will be able to: - Comprehend the fundamental concepts and advantages of transfer learning. - Examine widely used pre-trained models and their applications. - Conduct fine-tuning of pre-trained models for specific tasks. - Utilize transfer learning to address real-world challenges in natural language processing (NLP) and computer vision.

This instructor-led, live training (online or onsite) is designed for intermediate-level developers and AI practitioners who aim to implement fine-tuning strategies for large models with minimal computational resources. By the end of this training, participants will be able to: - Understand the principles of Low-Rank Adaptation (LoRA). - Implement LoRA to efficiently fine-tune large models. - Optimize fine-tuning processes for environments with limited resources. - Evaluate and deploy LoRA-tuned models for practical applications, ensuring alignment with public sector workflows and governance for government.

This instructor-led, live training in Los Angeles (online or onsite) is designed for intermediate to advanced professionals who seek to customize pre-trained models for specific tasks and datasets. By the end of this training, participants will be able to: - Comprehend the principles of fine-tuning and their applications. - Prepare datasets for the fine-tuning of pre-trained models. - Fine-tune large language models (LLMs) for natural language processing (NLP) tasks. - Enhance model performance and address common challenges. This training is tailored to meet the needs of professionals working in various sectors, including those for government, ensuring that participants are well-equipped to apply these skills in their respective fields.

This instructor-led, live training (available online or onsite) is designed for intermediate-level professionals who aim to enhance their natural language processing (NLP) projects through the effective fine-tuning of pre-trained language models. By the end of this training, participants will be able to: - Understand the core principles of fine-tuning for NLP tasks. - Fine-tune pre-trained models such as GPT, BERT, and T5 for specific NLP applications relevant to their work. - Optimize hyperparameters to achieve enhanced model performance. - Evaluate and deploy fine-tuned models in real-world scenarios, ensuring alignment with public sector workflows and governance standards for government.