Mastering Serverless Model Deployment: Best Practices and Cost Considerations

Serverless model deployment is a cloud computing model where the cloud provider manages the infrastructure and automatically allocates resources as needed. Best practices for deployment and inference in a serverless scenario include containerization, CI/CD pipelines, version control, monitoring, load balancing, caching, and rate limiting. ML OPS require special skills for model deployment in a serverless mode, and checks should be set up for input and output validation, error handling, and security.

Serverless Model Deployment

Serverless model deployment is a cloud computing model where the cloud provider manages the infrastructure and automatically allocates resources as needed. In this model, the user only pays for the actual usage of the resources, rather than paying for a fixed amount of resources.

Best Practices for Deployment and Inference for Serverless Scenario

When deploying a model in a serverless scenario, it is important to follow some best practices:

  • Use a containerized approach to package the model and its dependencies.
  • Use a continuous integration and continuous deployment (CI/CD) pipeline to automate the deployment process.
  • Use a version control system to manage the code and configuration files.
  • Use a monitoring system to track the performance and usage of the model.

For inference in a serverless scenario, it is important to:

  • Use a load balancer to distribute the requests across multiple instances of the model.
  • Use a caching system to reduce the latency of the requests.
  • Use a rate limiter to prevent overload of the model.

Specific Type of Issues, Cost Consideration, Solutions

One of the main issues with serverless model deployment is cold start latency, which is the time it takes to initialize a new instance of the model. This can be mitigated by using a warm start approach, where a pool of instances is kept warm and ready to handle requests.

Cost consideration is also important in serverless model deployment, as the user only pays for the actual usage of the resources. However, it is important to monitor the usage and optimize the resources to avoid unnecessary costs.

One solution to reduce costs is to use a serverless framework that automatically scales the resources based on the usage. Another solution is to use a hybrid approach, where some parts of the model are deployed in a serverless scenario and others are deployed in a traditional server-based scenario.

Special Skills ML OPS Require for Model Deployment in Serverless Mode and What Checks Should be Set up for Inference

ML OPS require special skills for model deployment in a serverless mode, such as:

  • Knowledge of containerization and orchestration tools.
  • Knowledge of cloud computing platforms and services.
  • Knowledge of CI/CD pipelines and version control systems.
  • Knowledge of monitoring and logging tools.

For inference, some checks that should be set up include:

  • Input validation to ensure that the input data is in the correct format and within the expected range.
  • Output validation to ensure that the output data is in the correct format and within the expected range.
  • Error handling to handle unexpected errors and prevent the model from crashing.
  • Security checks to prevent unauthorized access to the model and data.

Dataknobs Blog

10 Use Cases Built

10 Use Cases Built By Dataknobs

Dataknobs has developed a wide range of products and solutions powered by Generative AI (GenAI), Agent AI, and traditional AI to address diverse industry needs. These solutions span finance, healthcare, real estate, e-commerce, and more. Click on to see in-depth look at these use cases - Stocks Earning Call Analysis, Ecommerce Analysis with GenAI, Financial Planner AI Assistant, Kreatebots, Kreate Websites, Kreate CMS, Travel Agent Website, Real Estate Agent etc.

AI Agent for Business Analysis

Analyze reports, dashboard and determine To-do

DataKnobs has built an AI Agent for structured data analysis that extracts meaningful insights from diverse datasets such as e-commerce metrics, sales/revenue reports, and sports scorecards. The agent ingests structured data from sources like CSV files, SQL databases, and APIs, automatically detecting schemas and relationships while standardizing formats. Using statistical analysis, anomaly detection, and AI-driven forecasting, it identifies trends, correlations, and outliers, providing insights such as sales fluctuations, revenue leaks, and performance metrics.

AI Agent Tutorial

Agent AI Tutorial

Here are slides and AI Agent Tutorial. Agentic AI refers to AI systems that can autonomously perceive, reason, and take actions to achieve specific goals without constant human intervention. These AI agents use techniques like reinforcement learning, planning, and memory to adapt and make decisions in dynamic environments. They are commonly used in automation, robotics, virtual assistants, and decision-making systems.

Build Dataproducts

How Dataknobs help in building data products

Building data products using Generative AI (GenAI) and Agentic AI enhances automation, intelligence, and adaptability in data-driven applications. GenAI can generate structured and unstructured data, automate content creation, enrich datasets, and synthesize insights from large volumes of information. This helps in scenarios such as automated report generation, anomaly detection, and predictive modeling.

KreateHub

Create New knowledge with Prompt library

At its core, KreateHub is designed to enable creation of new data and the generation of insights from existing datasets. It acts as a bridge between raw data and meaningful outcomes, providing the tools necessary for organizations to experiment, analyze, and optimize their data processes.

Build Budget Plan for GenAI

CIO Guide to create GenAI Budget for 2025

CIOs and CTOs can apply GenAI in IT Systems. The guide here describe scenarios and solutions for IT system, tech stack, GenAI cost and how to allocate budget. Once CIO and CTO can apply this to IT system, it can be extended for business use cases across company.

RAG For Unstructred and Structred Data

RAG Use Cases and Implementation

Here are several value propositions for Retrieval-Augmented Generation (RAG) across different contexts: Unstructred Data, Structred Data, Guardrails.

Why knobs matter

Knobs are levers using which you manage output

See Drivetrain appproach for building data product, AI product. It has 4 steps and levers are key to success. Knobs are abstract mechanism on input that you can control.

Our Products

KreateBots

Setup chatbots in minutes
  • Pre built front end that you can configure
  • Pre built Admin App to manage chatbot
  • Prompt management UI
  • Personalization app
  • Built in chat history
  • Feedback Loop
  • Available on - GCP,Azure,AWS.
  • Add RAG with using few lines of Code.
  • Add FAQ generation to chatbot

    • KreateWebsites

    LLM and AI based Website generation
  • AI powered websites to domainte search
  • Premium Hosting - Azure, GCP,AWS
  • AI web designer
  • Agent to generate website
  • SEO powered by LLM
  • Content management system for GenAI
  • Buy as Saas Application or managed services
  • Available on Azure Marketplace too.

    • Kreate CMS

    CMS GenAI
  • CMS for GenAI
  • Lineage for GenAI and Human created content
  • Track GenAI and Human Edited content
  • Trace pages that use content
  • Ability to delete GenAI content

    • Generate Slides

    Effortless slides creation
  • Give prompt to generate slides
  • Convert slides into webpages
  • Add SEO to slides webpages

    • Content Compass

    Your story writing is automated
  • Generate articles
  • Generate images
  • Generate related articles and images
  • Get suggestion what to write next