FASTTRACK

ML Model

Build Custom AI Models That Create Competitive Advantage

Our ML Model FastTrack provides a de-risked, accelerated path to building and deploying custom machine learning solutions that solve your most complex business challenges.

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OVERVIEW

When Off-the-Shelf Models Aren't Enough

Your most critical business challenges—forecasting demand, predicting churn, or building recommendation systems—can't be solved with generic tools. Relying on the wrong model, or no model at all, leads to inaccurate predictions, leaves the value of your proprietary data untapped, and risks costly missteps.

Our ML Model Builder FastTrack provides a structured path to a custom machine learning solution that gives you a true competitive edge.

Custom Intelligence for a Competitive Edge

Solve Your Most Complex Business Challenges

Build custom models to tackle your most unique problems, from advanced fraud detection to dynamic customer churn prediction.

Gain a Competitive Advantage with Custom AI

Fine-tune models on your proprietary data to create a unique and defensible AI capability that your competitors cannot replicate.

A De-Risked Path to Custom Machine Learning

Our proven process, starting with a focused Proof-of-Concept, de-risks development and ensures a successful outcome with a clear ROI.

OUR APPROACH

Expertise That Prevents Missteps

Data science is a complex field. Our greatest accelerator is our deep expertise. Our team of AWS-certified experts quickly determines the right approach for your use case, de-risking the entire process and proving feasibility before scaling to a full production system.

Phase 1

Proof of Concept

Environment Setup: Our team will set up SageMaker Studio in your AWS account where we will conduct model development work
Discovery & Requirements: Our data scientists work with your team to understand goals for model outcomes and do a detailed walk through of your data
Dataset Preparation: We perform data cleaning, transformation, and feature engineering to create an initial training dataset
Model Development & Iteration: We build, train, and evaluate multiple models to find the best-performing approach
POC Results Checkpoint: Our team presents results and findings from the POC
Solution Architecture Workshop: We collaboratively make a plan for next steps for the model

ASSESSMENT WORKSHOPS

If the POC met expectations:

Goal for the Workshop: How do we get this model into production
Discuss any further model improvements that need to be made
Plan for how model will be consumed and how model will be deployed
Learn about MLOps and create a customized MLOps strategy for your organization
Discuss how to approach ground truth and monitoring – how will we know if the model is working?

If the POC uncovers opportunities for improvement:

Goal for the Workshop: To analyze key learnings from the POC and build a strategic plan for moving forward.
Leverage POC insights to identify the key factors for building a more successful model.
Review data quality for opportunities that could improve results: Is there additional key data to incorporate?
How can data quality be enhanced?
Determine the optimal path forward: Should we proceed with an enhanced second phase of development, or strategically redirect our efforts to achieve the best results?

Phase 2

Production Deployment

Model Iteration and Final Training: If required, our data scientists will conduct further model iteration and run finalized model training
Inference Packaging: Our data scientists author custom inference code, package dependencies, and register initial model version with required artifacts
Model Testing and Benchmarking: Conduct finalized model testing with holdout dataset and additional recent production datasets to verify model results; conduct benchmarking to collect stats on model performance, and select inference instance type
IAC Template buildout, CICD integration/buildout: Create IAC templates and CICD pipelines for inference infrastructure and code assets
Configure Data Capture: (Optional) configure endpoint data capture for model monitoring workflows
Final Deployment & Integration Support: We carry out production model deployment, and help your team integrate what we built with your existing application
Configure Cloudwatch Logging & Monitoring, and Data Drift & Model Monitoring: We set up monitoring to ensure system health, prepare for ongoing support, and establish a process for retraining the model.

Tangible Business Outcomes

The ML Model FastTrack provides a clear verdict on your model's viability and a foundational proof-of-concept, delivering detailed performance results and an actionable roadmap to production complete with a customized MLOps strategy.

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Ready to unlock the power of your proprietary data?

Work with an ML partner who understands model building on AWS, your goals and the agile processes required to bring value to your business faster.

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