MultimodalAI

STARTS SOON ON SATURDAY, FEBRUARY 1, 2025!

  This is the eagerly awaited hackathon-style, coding-centered BootCamp centered around real-world, exciting projects. The goal is to make you profoundly confident and fluent in applying LLMs to solve an extensive range of real-world problems in vector embeddings, semantic AI search, retrieval-augment generation, multi-modal learning, video comprehension, adversarial attacks, computer vision, audio-processing, natural language processing, tabular data, anomaly and fraud detection, healthcare applications, and clever techniques of prompt engineering. You will work in teams of 4-6 engineers in an environment that closely reproduces the innovation and development that is the quintessence of the Silicon Valley spirit. Each team will get a meeting room with a state-of-the-art multimedia setup and a powerful AI server with RTX4090 for exclusive use during the boot camp. These will also be accessible remotely so that the teams can continue to work on them during the workdays. You will also have unrestricted access to a massive 4-GPU AI server at the admin-privilege level for the sixteen week duration of this boot camp on a time-shared basis. Besides this, you will also benefit from the 10-gigabit networking and the large server cluster to take your projects to actual production. SupportVectors will let you use the compute resources for an additional four weeks if you need to finish any remaining aspects of your projects.

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.