#kubernetesservices

[simple-logic]

#PollTime Which Cloud Platform is Known for Kubernetes Services?

a) Azure 🔵 b) AWS ☁️ c) Google Cloud 🌍 d) Oracle Cloud 🌐

Cast your #vote Comment Below

[govindhtech]

Intel Webinar: Experienced Assistance To Implement LLMs

How Prediction Guard Uses Intel Gaudi 2 AI Accelerators to Provide Reliable AI.

Intel webinar

Large language models (LLMs) and generative AI are two areas where the growing use of open-source tools and software at the corporate level makes it necessary to talk about the key tactics and technologies needed to build safe, scalable, and effective LLMs for business applications. In this Intel webinar, Rahul Unnikrishnan Nair, Engineering Lead at Intel Liftoff for Startups, and Daniel Whitenack, Ph.D., creator of Prediction Guard, lead us through the important topics of implementing LLMs utilizing open models, protecting data privacy, and preserving high accuracy.

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Intel AI webinar

Important Conditions for Enterprise LLM Adoption

Three essential elements are identified in the Intel webinar for an enterprise LLM adoption to be successful: using open models, protecting data privacy, and retaining high accuracy. Enterprises may have more control and customization using open models like Mistral and Llama 3, which allow them to obtain model weights and access inference code. In contrast, closed models lack insight into underlying processes and are accessible via APIs.

Businesses that handle sensitive data like PHI and PII must secure data privacy. HIPAA compliance is typically essential in these scenarios. High accuracy is also crucial, necessitating strong procedures to compare the LLM outputs with ground truth data in order to reduce problems like as hallucinations, in which the output generates erroneous or misleading information even while it is grammatically and coherently accurate.

Obstacles in Closed Models

Closed models like those offered by Cohere and OpenAI have a number of drawbacks. Businesses may be biased and make mistakes because they are unable to observe how their inputs and outputs are handled. In the absence of transparency, consumers could experience latency variations and moderation failures without knowing why they occur. Prompt injection attacks can provide serious security threats because they may use closed models to expose confidential information. These problems highlight how crucial it is to use open models in corporate applications.

Prediction Guard

The Method Used by Prediction Guard

The platform from Prediction Guard tackles these issues by combining performance enhancements, strong security measures, and safe hosting. To ensure security, models are hosted in private settings inside the Intel Tiber Developer Cloud. To improve speed and save costs, Intel Gaudi 2 AI accelerators are used. Before PII reaches the LLM, input filters are employed to disguise or substitute it and prevent prompt injections. By comparing LLM outputs to ground truth data, output validators guarantee the factual consistency of the data.

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During the optimization phase, which lasted from September 2023 to April 2024, load balancing over many Gaudi 2 machines, improving prompt processing performance by bucketing and padding similar-sized prompts, and switching to the TGI Gaudi framework for easier model server administration were all done.

Prediction Guard moved to Kubernetes-based architecture in Intel Tiber Developer Cloud during the current growth phase (April 2024 to the present), merging CPU and Gaudi node groups. Implemented include deployment automation, performance and uptime monitoring, and integration with Cloudflare for DDoS protection and CDN services.

Performance and Financial Gains

There were notable gains when switching to Gaudi 2. Compared to earlier GPU systems, Prediction Guard accomplished a 10x decrease in computation costs and a 2x gain in throughput for corporate applications. Prediction Guard’s sub-200ms time-to-first-token latency reduction puts it at the top of the industry performance rankings. These advantages were obtained without performance loss, demonstrating Gaudi 2’s scalability and cost-effectiveness.

Technical Analysis and Suggestions

The presenters stressed that having access to an LLM API alone is not enough for a strong corporate AI solution. Thorough validation against ground truth data is necessary to guarantee the outputs’ correctness and reliability. Data management is a crucial factor in AI system design as integrating sensitive data requires robust privacy and security safeguards. Prediction Guard offers other developers a blueprint for optimizing Gaudi 2 consumption via a staged approach. The secret to a successful deployment is to validate core functionality first, then gradually scale and optimize depending on performance data and user input.

Additional Information on Technical Execution

In order to optimize memory and compute utilization, handling static forms during the first migration phase required setting up model servers to manage varying prompt lengths by padding them to specified sizes. By processing a window of requests in bulk via dynamic batching, the system was able to increase throughput and decrease delay.

In order to properly handle traffic and prevent bottlenecks, load balancing among numerous Gaudi 2 servers was deployed during the optimization process. Performance was further improved by streamlining the processing of input prompts by grouping them into buckets according to size and padding within each bucket. Changing to the TGI Gaudi framework made managing model servers easier.

Scalable and robust deployment was made possible during the scaling phase by the implementation of an Intel Kubernetes Service (IKS) cluster that integrates CPU and Gaudi node groups. High availability and performance were guaranteed by automating deployment procedures and putting monitoring systems in place. Model serving efficiency was maximized by setting up inference parameters and controlling key-value caches.

Useful Implementation Advice

It is advised that developers and businesses wishing to use comparable AI solutions begin with open models in order to maintain control and customization options. It is crucial to make sure that sensitive data is handled safely and in accordance with applicable regulations. Successful deployment also requires taking a staged approach to optimization, beginning with fundamental features and progressively improving performance depending on measurements and feedback. Finally, optimizing and integrating processes may be streamlined by using frameworks like TGI Gaudi and Optimum Habana.

In summary

Webinar Intel

Prediction Guard’s all-encompassing strategy, developed in partnership with Intel, exemplifies how businesses may implement scalable, effective, and safe AI solutions. Prediction Guard offers a strong foundation for corporate AI adoption by using Intel Gaudi 2 and Intel Tiber Developer Cloud to handle important issues related to control, personalization, data protection, and accuracy. The Intel webinar‘s technical insights and useful suggestions provide developers and businesses with invaluable direction for negotiating the challenges associated with LLM adoption.

Read more on govindhtech.com

[johnthetechenthusiast]

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[octalitsolution]

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[codeonedigest]

Kubernetes Service Tutorial for Beginners and Devops Students | Kubernetes Service Explained

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Kubernetes service can be defined as a logical set of pods. It can be defined as an abstraction on the top of the pod which provides a single IP address and DNS name by which pods can be accessed. With Service, it is very easy to manage load balancing configuration. It helps pods to scale very easily. Kubernetes selectors allows us to select Kubernetes resources based on the value of labels and…

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[johnthetechenthusiast]

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[johnthetechenthusiast]

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