Recent innovations in generative AI technology have made people realize the potential of AI to accelerate productivity and give organizations a competitive edge. But there are challenges to adoption of generative AI technology in organizations.

Challenges Organizations face with generative AI adoption

Getting Started

The state of the art is evolving quickly. Every other day there are new AI products, technologies and companies coming out with newsflash about how they are the latest and best in the market.  With such overwhelming information overload, many are playing wait game.

Development

Building and integrating generative AI applications require multiple cutting-edge products and frameworks that require specialized expertise that many organizations are lacking.

Context

To effectively use generative AI applications, organizations need to find ways to make large language models ground to their data and customize their AI applications to their domain, which many organizations find to be a tough task.

Evaluation

It is hard to figure out which model to use and how to optimize it for a particular use case.

Operationalization

Concerns around privacy, security and grounding make operationalization of large language models a difficult task. Lack of experience makes this task especially challenging.

Operationalization of large language models (LLMOps) is anew process that organizations are trying to implement. LLMOps is similar to DevOps or MLOps that many organizations use to operationalize their applications and machine learning pipelines. But LLMOps brings new challenges.

Challenges of LLMOPs

Target Audience

LLMOps requires application developers and ML engineers working together, which is different from MLOps where ML engineers and Data scientists work together.

Shared Assets

LLMOps has assets like LLM, agents, plugins, prompts, chains and APIs, which is different from assets shared om MLOps like models, data, environments and features.

Metrics/Evaluations

The evaluation metrics for MMLOps are quality, harm, correctness, cost and latency etc. which is different from metrics used in MLOps like accuracy, Root Mean Squared Error, F1 score etc.

Types of ML Models

LLMOps involves working with pretrained large language models that is different from traditional machine learning where ML libraries are used to train ML models using data.

Azure AI Studio is a web based integrated development environment (IDE) that makes development and operationalization of generative AI applications using large language models easy to make organizations more productive.

Components of Azure AI Studio

Playground

Playground in Azure AI studio helps developers quickly test large language model deployment and grounding to data using a pre-built chat UI.

Deployment

Users can create REST API end point for a selection of large language models.

Prompt Flow

Prompt Flow is a development tool that makes development and architecting of generative AI applications using large language models easy and productive with visual flows and IntelliSense.

Evaluation

Azure AI studio has built in flow evaluation tools to test relevant metrics like groundedness, relevance, coherence, fluency, GPT similarity and F1 score.

Fine Tuning

Fine Tuning allows users to create new deployment of large language models that are fine tuned to the provided ground data.

Custom Neural Voice

Users can select and customize the AI voice they want to use for their app.

Custom Speech

Users can customize how speech detection and translation works.

Content Filter

Users can customize generative AI input and output for sensitive topics like Violence, Hate, Sexual and Self-harm etc.

You can watch my full walkthrough of Azure AI Studio on my YouTube channel.