Generative AI process ingests training code, labeled data and unlabeled data and the resulting foundation models can generate new content in form of text, code, images, audio, video, etc. The process of learning from existing content is called training and results in creation of statistical model. When given a prompt this statistical model generates new content. Organizations use generative AI models to train them on their proprietary corporate data and then use it in different ways to collaborate with knowledge workers to drive productivity and decision making capabilities.However, often companies struggle with building production grade pipelines due to cost, security, hardware and software requirements.
Reduce the time required to develop and deploy machine learning models, allowing businesses to achieve results faster. Pre-built models enable rapid implementation, cutting down on the development cycle and accelerating time-to-value.
Utilizing pre-fine-tuned models reduces the need for extensive development resources, saving on costs associated with hiring, training, and infrastructure.
These models are crafted with industry best practices and have been rigorously tested to ensure high performance and reliability. These pre-built and pre-fine tuned models, minimizes the risks associated with model development, providing a dependable foundation for business applications.
By using accelerators, businesses can focus more on strategic activities and innovation rather than getting bogged down in the technical complexities of model development. Ensure that the accelerators comply with your organization’s data security policies and regulatory requirements.
Multimodal RAG applications involving text, images, video, audio.
Security and Governance
Model training at inference time
Cost effective and use case specific model tuning by prompt engineering, prompt learning, LoRA, SFT/RLHF
Service: Evaluating accelerators for compliance with data security policies and regulatory requirements, and implementing necessary security measures. Benefit: Protects sensitive data and ensures that the models adhere to industry standards and regulations.
Automated model evaluation and regression testing
Rapid iteration over models, training and evaluation on accelerated infrastructure(cloud native, 3rd party hosting, on-prem hosting)
Hardware inference acceleration by model parallelism and tensor parallelism and software inference acceleration by model chunking and batching strategies
Model monitoring for performance, security
Deployment topologies of on-prem or cloud Kubernetes clusters or hybrid cloud to achieve cost/performance/accuracy
Roadmap architectures and execution strategies for GCP, AWS, Azure and Nvidia pipelines
Choosing us for your AI/ML model development means partnering with a team that is deeply committed to unlocking the full potential of your data.
