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    AI Platform Setup and Prototype Build

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    Amazon SageMaker is a fully managed machine learning service, that allows data scientists and ML engineers to build and productionalize end-to-end ML pipelines. Through this offer, Adastra will establish a robust SageMaker environment and implement an initial prototype to support your business-specific use case.
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    AI Platform Setup and Prototype Build

     Info

    Overview

    Approach

    SageMaker Platform Setup

    • Establishment of a SageMaker environment to enable development of a prototype AI/ML model, leveraging SageMaker instances
    • Setting up connectivity to data store
    • Leveraging SageMaker Model Registry to cataog and manage model versioning
    • Leveraging SageMaker Endpoints for model hosting and production
    • Enabling additional features such as data labelling, preprocessing, model training, evaluation, and production performance to support a target use case

    Prototype Model Build

    • Discovery to scope out business problem, objective function, error metrics, potential independent predictors, and a target response for the buildout of an AI/ML model prototype
    • Consolidation of data from up to 3 data sources, denormalization into a datastore to enable predictive modeling
    • Iterative prototype model built to obtain desired accuracy, including feature augmentation, feature engineering, and model development cycles
    • Recommendations for production

    Activities

    • Setup of the SageMaker environment
    • Data consolidation
    • Data assessment
    • Feature augmentation
    • Feature engineering
    • Feature importance mapping and assessment
    • Iterative model development
    • Model selection and hyperparameter optimization
    • Results summarization

    Deliverables

    • Scripts for data consolidation, feature engineering, model development, and assessment
    • Scripts for model development and assessment
    • Packaged pipelines for prototype model
    • Result spreadsheets and visualizations demonstrating model accuracies
    • Relevant support visuals, depending on the use case
    • Technical workflow summary

    Outcomes

    • Functional SageMaker environment for machine learning model development
    • Understanding of any potential gaps for the final model buildout
    • An accurate determination of the investment and anticipated accuracy for a full solution buildout
    • Ability to plan for required architecture for implementation

    Highlights

    • Enable an environment that supports end-to-end AI/ML model development, from data preparation, model development, training/tuning, deployment, and management
    • Build of an AI prototype to support a specific business use case, with a baseline accuracy and recommendations for production

    Details

    Delivery method

    Pricing

    Custom pricing options

    Pricing is based on your specific requirements and eligibility. To get a custom quote for your needs, request a private offer.

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    Support

    Vendor support

    Adastra offers a myriad of solutions from Cloud Migration and Analytics to Data Science and Governance as an Advanced Consulting Partner of AWS, including but not limited to:

    Data Discovery & Analytics Data Quality Artificial Intelligence Machine Learning Data Lake Build Data Engineering

    Learn more about Adastra:

    https://www.adastracorp.com/ 

    Contact us today for a review of your requirements

    awsmarketplacesales@adastragrp.com