EmbeddingOptimization

STARTS SOON ON MONDAY, MAR 31, 2025!

  Elevate your fine-tuning expertise with our immersive hands-on course designed for AI practitioners. Begin with the foundational concepts of transfer learning and pre-trained models, then dive into fine-tuning methodologies for transformers and other state-of-the-art architectures. Explore open-source libraries such as Hugging Face, LoRA, and PEFT for scalable and efficient fine-tuning. Master techniques like prompt tuning, adapter tuning, and hyperparameter optimization to tailor models for domain-specific tasks. Learn strategies for low-resource fine-tuning, including few-shot and zero-shot learning, and address overfitting with advanced regularization methods. Discover fine-tuning approaches for diverse modalities, including text, images, and multimodal data, while exploring domain-adaptation strategies for out-of-distribution datasets. Implement advanced training strategies like quantization-aware training, curriculum learning, and differential privacy. By the end of the course, you’ll have the practical knowledge to fine-tune models for real-world applications, ensuring optimal performance and efficiency tailored to your unique datasets.

STARTS SOON ON MONDAY, FEBRUARY 3, 2025!

  Unlock the power of operationalizing AI systems with our comprehensive MLOps and LLMOps course, designed for engineers and practitioners. Start with the principles of continuous integration, deployment (CI/CD), and version control tailored for machine learning workflows. Learn to automate data pipelines, model training, and monitoring using tools like MLflow, DVC, and Airflow. Gain expertise in serving and scaling large language models (LLMs) with techniques like sharding, quantization, and optimization. Explore infrastructure orchestration with Kubernetes and cloud platforms for deploying AI at scale. Delve into advanced observability, including drift detection, explainability, and model retraining triggers. Discover best practices for handling multi-modal pipelines and hybrid systems, integrating LLMs into retrieval-augmented architectures and streaming applications. Address ethical and regulatory considerations, ensuring compliance in real-world deployments. By the end of this course, you’ll be equipped to design, deploy, and manage robust MLOps and LLMOps pipelines, unlocking scalability, efficiency, and reliability for AI systems at any scale.