Dive Into the New Age of Generative AI
Embark on a transformative journey into the new age of Generative AI. Explore limitless possibilities with our cutting-edge solutions and unlock the power of artificial intelligence today
Generative AI Organization Structure
Successfully executing a Generative AI project requires a multidisciplinary team with skills beyond AI and machine learning. Essential areas include data engineering, model fine-tuning, user experience, and industry expertise. A diverse team helps navigate the complexities and translates AI capabilities into practical business solutions.
Shorthills AI Organizational Structure for AI Solutions
Solution Architects understand client requirements, deliver cost-effective POCs, select appropriate models for finetuning and scaling, and possess expertise in tooling, deployment, and ML Ops
Data Engineer and Annotation
The task of a data engineer and annotations involves extracting and transforming data, preparing it for evaluation, and presenting it for annotation. Additionally, they are responsible for preparing the data to be used in finetuning processes.
The role of a prompt engineer involves creating and implementing task prompts, showcasing expertise in logical reasoning, and swiftly evaluating outcomes. They excel at optimizing prompt design for optimal performance.
Tooling and API Expert
MLOps and Observability
Tooling and API experts possess expertise in APIs such as Langchain, Llama Index, and OpenAI. They excel in chaining prompts or creating agents, and demonstrate proficiency in utilizing GPT plugins to enhance their capabilities
Model finetuning involves optimizing the right model while efficiently managing memory and compute resources. It requires a deep understanding in technologies like DeepSpeed, Quantization, and other techniques for enhancing model performance.
MLOps and observability professionals have the responsibility of understanding and optimizing LLM metrics like perplexity, accuracy (Acc), BLEU, etc. They also focus on comprehending and measuring model drift, as well as ensuring proper versioning and maintenance of data.