Generative AI Pipelines

Need for Generative AI pipelines

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.

Generative AI pipelines advantages

01.

Speed to Market

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.

02.

Cost Efficiency

Utilizing pre-fine-tuned models reduces the need for extensive development resources, saving on costs associated with hiring, training, and infrastructure.

03.

Proven Performance

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.

04.

Focus on Core Competencies

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.

Our services

Multimodal RAG applications

Multimodal RAG applications involving text, images, video, audio.

Security and Governance

Model training at inference time

Cost effective and use case specific model

Cost effective and use case specific model tuning by prompt engineering, prompt learning, LoRA, SFT/RLHF

Security and Compliance Assessment

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

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

Deployment topologies of on-prem or cloud Kubernetes clusters or hybrid cloud to achieve cost/performance/accuracy

Roadmap architectures and execution strategies

Roadmap architectures and execution strategies for GCP, AWS, Azure and Nvidia pipelines

Need for Generative AI pipelines