#genaiops

GenAI Ops

As generative AI workloads go from proof-of-concept to production, Google Cloud clients are witnessing tangible business benefits from their AI investments. Numerous clients have collaborated with Google Cloud Consulting to implement AI in significant and beneficial ways. To help its clinical study teams find crucial information and produce documents more quickly, Bristol Myers Squibb, for instance, developed a new AI-powered interface. Palo Alto Networks, on the other hand, introduced a number of new AI tools that use Gemini to improve user experience in its copilots and increase security practitioners’ productivity.

Implementing these workloads in production calls for extensive knowledge of big language model architectures, fast engineering, evaluation, and generative AI systems design, among other topics. Now, with the introduction of a new service offering called GenAI Ops, Google is bringing Google’s proficiency in these fields to their clients on a large scale. This new offering will assist businesses in developing their generation AI prototypes into production-grade solutions and will be supplied by Google Cloud Consulting or through their extensive partner ecosystem. It will also give support in critical areas such as security, model tuning and feedback, and optimisation.

GenAI Operations: GenAI Constant Adjustment and Input

With the assistance of Google’s GenAI specialists, elevate your generative AI (GenAI) offering to new heights. By including a continual tuning and feedback pipeline into the system design, their team will enhance the output from your model. This can involve creating automated data pipelines, fine-tuning triggers, and putting in place systems for gathering and incorporating downstream feedback. Prototypes, MVPs, and production-ready models are all covered by this offer.

GenAI Ops: Optimising GenAI Models

With the assistance of Google’s GenAI specialists, elevate your generative AI (GenAI) offering to new heights. To position your team for long-term success, Google’s team will optimise your model output or AI infrastructure, implement the optimised service, and finish a knowledge transfer. This deal is valid for prototypes, MVPs, and production-ready models. It can be customised to meet your unique requirements in terms of model selection, timely engineering, agent routing, integrating the newest APIs, enhancing GenAI methods, enhancing system architecture, cutting latency, or cutting expenses.

Google Cloud now provides users with an open and optimised technology stack for developing AI in addition to a wide range of services to assist users at every stage of their AI transformations, from discovery to production, with the introduction of GenAI Ops.

The procedures needed to prepare AI applications for production are walked clients through in the new GenAI Ops services offering. Among them are:

Engineering, designing, and optimising prompts is crucial to ensuring that models can produce high-quality results and gaining the trust of users. With the use of retrieval augmented generation (RAG), chain of thought, and best practices for fast engineering, Google Cloud Consulting may assist clients in developing solutions that enhance the functionality of their existing AI applications and model outputs.

Crucially, distinct models are frequently appropriate for various use scenarios, and each of these models could call for a unique prompting structure. Google’s knowledgeable teams will assist clients in matching the appropriate model to the appropriate use case as well as the appropriate prompting strategy to the appropriate model.

Performance and system assessment: In order to successfully implement AI in the workplace, models and applications must be continuously evaluated and given feedback. This services offering assists clients in developing mechanisms for automated evaluation metrics utilising technologies such as AutoSxS and GenAI Eval, human evaluation, as well as hybrid techniques, and in designing and implementing an assessment framework customised for their applications.

Model optimisation and ongoing tuning: Gen AI applications and models still need ongoing tweaking and optimisation even when a framework for performance and system evaluation is established. Gen AI Ops offers managed services and solutions for model tuning and optimisation based on benchmarking and user input. To ensure that applications execute as efficiently as possible, this entails enhancing system design and model selection, cutting costs and latency, and utilising the most recent APIs and tools available to orchestrate and create AI agents utilising LangChain or do-it-yourself orchestrators.

Monitoring and observability: Ensuring that AI applications are ready for production requires having a strong monitoring system in place. In order to continuously monitor the performance and operations of their generation AI applications on a wide range of criteria, such as model accuracy and hallucinations, latency, throughput, hardware utilisation, model drift, traffic, and costs, Google Cloud Consulting may assist customers in building observability solutions.

Testing and business integration: The performance and integration of a customer’s applications and models with their business processes in real-world settings is crucial. Customers can get assistance from Google Cloud Consulting with the meticulous planning needed to accomplish this, such as creating a safe and scalable environment on Google Cloud, creating APIs to effectively manage interactions with different models, and putting their models through rigorous unit, integration, and load testing to assess performance under various scenarios.

Educate and empower client teams

Customers that want to see success with their cloud deployments must prioritise training and team enablement in addition to the technical and business planning procedures needed to put AI applications into production. Google Cloud provides a variety of trainings, practical labs, bootcamps, and coursework through the Google Cloud Skills Boost Platform to help teams become more proficient in generative AI and ensuring that client teams are able to create, implement, utilise, and oversee innovative AI applications.

Hype of Generative AI

Generative AI is not just a fleeting trend; it's atransformative force that's been captivating global interest. Comparable in significance to the dawn of the internet, its influence extends across various domains, altering the way we search, communicate, and leverage data. From enhancing business processes to serving as an academic guide or a tool for crafting articulate emails, its applications are vast. Developers have even begun to favor it over traditional resources for coding assistance. The term Retrieval Augmented Generation (RAG), introduced by Meta in 2020 (1), is now familiar in the corporate world. However, the deployment of such technologies at an enterprise level often encounters hurdles like task-specificity, accuracy, and the need for robust controls.

Why enterprises struggle with Industrializing Generative AI

Despite the enthusiasm, enterprises are grappling with the practicalities of adopting Generative AI.

According to survey by MLInsider,

62% of AI professionals continue to say it is difficult to execute successful AI projects. The larger the company, the more difficult it is to execute a successful AI project.

Lack of expertise, budget, and finding AI talent are the top challenges organizations are facing when it comes to executing ML programs.

Only 25% of organizations have deployed Generative AI models to production in the past year.

Of those who have deployed Generative AI models in the past year, several benefits have been realized. About half said they have seen improved customer experiences (58%) and improved efficiency (53%).

In summary, Generative AI offers massive opportunities to enterprise but due to skills, requirements for enterprise security and governance, they are still behind in the adoption curve.

Industrialization of Generative AI applications

The quest for enterprise-grade Generative AI applications is now easier, thanks to SaaS-based model APIs and packages like Langchain and Llama Index. Yet, scaling these initiatives across an enterprise remains challenging. Historical trends show that companies thrive when utilizing a centralized platform that promotes reusability and governance, a practice seen in the formation of AI and ML platform teams.

Enterprises should think about Gen AI platforms with the above four layered cake,

Infrastructure - Most companies have a primary cloud infrastructure and typically utilize Gen AI building blocks offered by the cloud.

Capabilities - These are set of foundational building block services offered by cloud native (e.g. Opensearch, Azure OpenAI) or 3rd party SAAS products(e.g. Milvus Vector search)

Reusable services - Central Gen AI teams typically have to build a RAG (Retrieval Augmented Generation), Fine Tuning or Model Hub Services that can be readily consumed with enterprise guard-rails

Use cases - Using the reusable services, use cases can be deployed and integrated with a variety of applications such as Customer support bot, summarizing customer reviews and more.

Many Data, ML and AI vendors are snapping these capabilities on top of their existing platform. ML Platforms that start with supervised labels and depend on model building & deployment aspect of MLOps, Generative AI platforms begin with a pre-trained Open source model(e.g. Llama2) or proprietary SAAS model(GPT4), focuses on capabilities to contextualize Large Language models and deploy capabilities to enable smarts in applications such as Copilots or Agents. Hence we propose a radically different approach to fulfill the promise of industrialized Gen AI that focuses on LLMOps development loop ( Connect to Model Hub -> Contextualize Model for Data -> Human Evaluation )

Introducing Generative AI Platform for all

Karini AI presents "Generative AI platform", designed to revolutionize enterprise operations by integrating proprietary data with advanced language models, effectively creating a digital co-pilot for every user. Karini simplifies the process, offering intuitive Gen AI templates that allow rapid application development. The platform offers an array of data processing tools and adheres to LLMOps practices for deploying Models, Data, and Copilots. It also provides customization options and incorporates continuous feedback mechanisms to enhance the quality of RAG implementations.

Conclusion

Karini AI accelerates experimentation, expedite market delivery, and bridge the generative AI adoption gap, enabling businesses to harness the full potential of this groundbreaking technology.

The business landscape is in perpetual flux, demanding constant adaptation and evolution. Organizations must keep pace with change and strategically outmaneuver it to thrive. In this dynamic environment, embracing disruptive technologies like Generative AI becomes not just an option but a necessity.

Beyond Analysis, Lies Creation: A New Frontier of AI

Unlike traditional machine learning, which focuses on analysis and classification, Generative AI ventures into creation. Imagine it as an inexhaustible wellspring of AI-powered creativity, capable of generating entirely new content – text, images, music, or even code. Think of it as AI with imagination, ready to unlock possibilities previously confined to the human mind.

Demystifying the Engine: LLMs, NLP, and the Collaborative Powerhouse

This transformative potential hinges on a collaborative interplay of crucial components.Large Language Models (LLMs) form the backbone of many Generative AI systems, particularly those dealing with text. These AI entities are trained on massive datasets, absorbing the intricacies and nuances of human language. This empowers them to generate realistic and coherent text, translate languages, and craft diverse creative content.

Natural Language Processing (NLP) plays a crucial role in this process. By enabling computers to understand and interpret human language, NLP allows Generative AI models to decipher our instructions and translate them into actionable insights, ultimately guiding the desired output.

Generative AI, LLMs, NLP, and machine learning are not isolated entities but rather interlocking pieces of a much larger puzzle. The process begins with feeding massive amounts of data into LLMs. Machine learning algorithms then analyze this data, unearthing complex patterns and structures. NLP techniques come into play next, enabling the system to glean the context and meaning embedded within user instructions and data inputs. Finally, armed with this comprehensive understanding, the Generative AI model generates new data that aligns with the identified patterns and the intent behind the user input.

The Imperative for Action: Embracing the Generative Future

While Generative AI is still in its early stages, its potential is undeniable. Businesses that seize this opportunity and become early adopters stand to gain a significant first-mover advantage, propelling them to the forefront of their industries and delaying; however, they must catch up as Generative AI disrupts existing processes and redefines market dynamics.

Real-World Examples of the Generative AI Advantage:

Marketing & Advertising: Personalized content creation with 30% higher click-through rates and targeted messaging with 20% increased engagement as seen in companies like Unilever and Netflix.

Research & Development: Accelerating drug discovery and pioneering material science innovations as implemented by Pfizer and Siemens.

Customer Service & Support: Implementing automated chatbots with 25% reduced wait times and personalized product recommendations leading to increased customer satisfaction and sales exemplified by Hilton Hotels and Amazon.

Your Roadmap to Leveraging Generative AI

Embarking on the Generative AI journey requires meticulous planning and strategic execution. The first step involves identifying specific use cases within your organization. Where can Generative AI streamline existing processes or unlock entirely new opportunities? Focusing on targeted areas with the potential for high impact is crucial for maximizing the return on investment.

Experimentation through pilot projects offers an invaluable opportunity to gain firsthand experience, identify potential challenges, and cultivate internal support for wider adoption within the organization. Lastly, selecting the appropriate Generative AI tools requires thoroughly evaluating various platforms, ensuring they seamlessly integrate with existing infrastructure and align with specific business needs and resource constraints.

Identify targeted use cases:

Where can Generative AI improve existing processes or create new opportunities?

Focus on areas with high-impact potential for maximum ROI.

Embrace experimentation:

Run pilot projects to gain experience, identify challenges, and build internal support.

Select the right tools:

Evaluate available platforms for seamless integration with existing infrastructure and alignment with business needs and resources.

Introducing Karini AI: Your Generative AI Ally

At Karini AI, we understand the challenges and complexities of operationalizing Generative AI applications. We are committed to partnering with organizations globally to overcome these hurdles and propel them into the forefront of this transformative technology.

Simplified process: We demystify technical complexities and jargon, making Generative AI accessible to everyone.

Unlocking data potential: We empower you to extract value from your data and foster an environment for creative exploration.

Iterative learning: Our platform allows you to experiment, learn, and refine your AI applications, ensuring successful implementation.

Responsible innovation: Our solutions prioritize security and ethical considerations, guaranteeing responsible and trustworthy applications.

Collaborative expertise: We provide the tools and knowledge you need to navigate the Generative AI landscape with confidence.

Karini AI's platform is engineered to demystify Generative AI, transforming it from a complex, technical endeavor into an accessible, user-friendly revolution that anyone can join. It's designed not just to unlock but to unleash the potential of your data, fostering an ecosystem where imagination and innovation aren't just encouraged but expected.

With our platform, you'll navigate through the Generative AI process with ease—from ideation and experimentation to development and deployment. The journey is iterative, allowing for continuous learning and refinement, culminating in robust applications tailored to your organization's needs.

At the heart of our platform is a commitment to security and ethics. We guide you in implementing robust safeguards that ensure your Generative AI applications are not only innovative but also responsible. By fostering a collaborative environment equipped with advanced tools and expertise, Karini AI empowers you to harness the transformative potential of Generative AI and lead the charge in the new frontier of digital innovation.

In an era where data is the new gold, businesses have grappled with the challenge of data silos - isolated reservoirs of information accessible only to specific organizational factions.

This compartmentalization of data is the antithesis of what we term 'healthy' data: information that's universally comprehensible and accessible, fueling informed decision-making across an enterprise. For decades, enterprises have endeavored to dismantle these silos, only to inadvertently erect new ones dictated by the need for efficient data flows and technological limitations.

However, the landscape is radically transforming, thanks to Generative AI (Gen AI) and its groundbreaking capabilities.

The Transformational Shift with Gen AI:

The advent of Gen AI heralds an unprecedented shift in data management and accessibility. With the advent of Retrieval Augmented Generation (RAG) and its integration into infinitely expandable vector data stores, the once-unthinkable is now a tangible reality. Karini.ai stands at the forefront of this revolution, harnessing Gen AI to bridge the gaps between disparate data stores, file repositories, and databases, turning unconnectable into a seamlessly interconnected web of knowledge.

The Dawn of a New Data Era:

For the first time in the annals of corporate history, every line of business has the key to unlock the treasures within all available data, regardless of its domicile. The power of Large Language Models (LLMs) further revolutionizes this landscape, enabling users to query complex data pools through intuitive, natural language. The beauty of this innovation lies not just in its technical prowess but in its adherence to the intricate tapestry of governance and compliance that underpins the corporate world.

Case Studies: The Infinite Horizon of Use Cases:

Karini.ai, armed with Gen AI, is not just transforming businesses; it's redefining them. From marketing insights derived from an ocean of consumer data to predictive maintenance in manufacturing powered by real-time IoT data - the use cases are as limitless as the human imagination. In finance, risk assessment models become more nuanced and robust, drawing from a richer, more diverse set of data points. Patient care personalization reaches new heights in healthcare as medical histories and research data converge to offer bespoke treatment plans.

Karini.ai: Your Navigator in the Gen AI Odyssey:

Navigating the vast seas of data with Gen AI is a venture fraught with challenges, from ensuring data integrity to maintaining privacy and compliance. Karini.ai does not just provide the tools for this journey; it offers the compass and the map. With our expertise, your enterprise can chart its unique course through this brave new world of unified data. We provide the guardrails to ensure your voyage is innovative, secure, compliant, and aligned with your corporate ethos and objectives.

Conclusion: A Call to Pioneer the Future:

The amalgamation of siloed data through Gen AI is not just an operational upgrade; it's a paradigm shift in how businesses perceive and utilize information. It's an invitation to pioneer a future where data is not just a resource but a beacon that guides every strategic decision, every innovation, and every customer interaction. Karini.ai is your partner in this transformative journey, fortified with robust governance and a deep understanding of your business landscape, bringing your business the prowess of Gen AI.

(करिणी) - We are with you on your entire journey…

About Karini AI:

Fueled by innovation, we're making the dream of robust Generative AI systems a reality. No longer confined to specialists, Karini.ai empowers non-experts to participate actively in building/testing/deploying Generative AI applications. As the world's first GenAIOps platform, we've democratized GenAI, empowering people to bring their ideas to life – all in one evolutionary platform. 

Contact:

Jerome Mendell

(404) 891-0255

[email protected]

When creating a RAG (Retrieval Augmented Generation) system, you infuse a Large Language Model (LLM) with fresh, current knowledge. The goal is to make the LLM's responses to queries more factual and reduce instances that might produce incorrect or "hallucinated '' information.

A RAG system is a sophisticated blend of generative AI's creativity and a search engine's precision. It operates through several critical components working harmoniously to deliver accurate and relevant responses.

Retrieval: This component acts first, scouring a vast database to find information that matches the query. It uses advanced algorithms to ensure the data it fetches is relevant and current.

Augmentation: This engine weaves the found data into the query following retrieval. This enriched context allows for more informed and precise responses.

Generation: This engine crafts the response with the context now broadened by external data. It relies on a powerful language model to generate answers that are accurate and tailored to the enhanced input.

We can further break down this process into the following stages:

Data Indexing: The RAG journey begins by creating an index where data is collected and organized. This index is crucial as it guides the retrieval engine to the necessary information.

Input Query Processing: When a user poses a question, the system processes this input, setting the stage for the retrieval engine to begin its search.

Search and Ranking: The engine sifts through the indexed data, ranking the findings based on how closely they match the user's query.

Prompt Augmentation: Next, we weave the top-ranked pieces of information into the initial query. This enriched prompt provides a deeper context for crafting the final response.

Response Generation: With the augmented prompt in hand, the generation engine crafts a well-informed and contextually relevant response.

Evaluation: Regular evaluations compare its effectiveness to other methods and assess any adjustments to ensure the RAG system performs at its best. This step measures the accuracy, reliability, and response time, ensuring the system's quality remains high.

RAG Enhancements:

To enhance the effectiveness and precision of your RAG system, we recommend the following best practices:

Quality of Indexed Data: The first step in boosting a RAG system's performance is to improve the data it uses. This means carefully selecting and preparing the data before it's added to the system. Remove any duplicates, irrelevant documents, or inaccuracies. Regularly update documents to keep the system current. Clean data leads to more accurate responses from your RAG.

Optimize Index Structure: Adjusting the size of the data chunks your RAG system retrieves is crucial. Finding the perfect balance between too small and too large can significantly impact the relevance and completeness of the information provided. Experimentation and testing are vital to determining the ideal chunk size.

Incorporate Metadata: Adding metadata to your indexed data can drastically improve search relevance and structure. Use metadata like dates for sorting or specific sections in scientific papers to refine search results. Metadata adds a layer of precision atop your standard vector search.

Mixed Retrieval Methods: Combine vector search with keyword search to capture both advantages. This hybrid approach ensures you get semantically relevant results while catching important keywords.

ReRank Results: After retrieving a set of documents, reorder them to highlight the most relevant ones. With Rerank, we can improve your models by re-organizing your results based on certain parameters. There are many re-ranker models and techniques that you can utilize to optimize your search results.

Prompt Compression: Post-process the retrieved contexts by eliminating noise and emphasizing essential information, reducing the overall context length. Techniques such as Selective Context and LLMLingua can prioritize the most relevant elements.

Hypothetical Document Embedding (HyDE): Generate a hypothetical answer to a query and use it to find actual documents with similar content. This innovative approach demonstrates improved retrieval performance across various tasks.

Query Rewrite and Expansion: Before processing a query, have an LLM rewrite it to express the user's intent better, enhancing the match with relevant documents. This step can significantly refine the search process.

By implementing these strategies, businesses can significantly improve the functionality and accuracy of their RAG systems, leading to more effective and efficient outcomes.

Using Karini AI’s purpose-built platform for GenAIOps, you can build production-grade, efficient RAG systems within minutes.

About Karini AI:

Fueled by innovation, we're making the dream of robust Generative AI systems a reality. No longer confined to specialists, Karini.ai empowers non-experts to participate actively in building/testing/deploying Generative AI applications. As the world's first GenAIOps platform, we've democratized GenAI, empowering people to bring their ideas to life – all in one evolutionary platform. 

Contact:

Jerome Mendell

(404) 891-0255

[email protected]