AI & Machine Learning Implementation

Pro

Ari Harrison

Starting at

10,000

About this service

Summary

I offer a custom AI and machine learning implementation service that builds, trains, and deploys predictive models for your business using Hex’s computational infrastructure, delivering actionable insights without the need for in-house data scientists. What makes me unique is my streamlined, end-to-end process—combining fixed-tier pricing with optional ongoing support—ensuring you get a tailored, high-impact solution that’s both affordable and sustainable, all powered by Hex’s scalable technology.

Process

Process Summary: AI & Machine Learning Implementation

The process is structured to transform your raw data assets into a powerful, predictive tool tailored to your business objectives. It involves several key phases—data preparation, model development, deployment, and post-deployment support—each designed to maximize value while minimizing complexity for your team. Here’s how it works:

1. Discovery and Goal Setting

Objective: Understand your business needs and define the predictive outcomes you want to achieve (e.g., sales forecasting, customer churn prediction, operational efficiency).

Steps:

Conduct an initial consultation (virtual or written) to identify your key data assets, business challenges, and desired outcomes.

Assess the scope of your data (e.g., volume, variety, quality) and how it aligns with potential machine learning use cases.

Agree on the primary use case for the custom model to ensure focus and clarity.

Duration: 1-2 days.

Outcome: A clear project brief that outlines the predictive goal and confirms data availability.

2. Data Collection and Preprocessing

Objective: Prepare your data assets for effective model training.

Steps:

Collect and consolidate your data from specified sources (e.g., databases, spreadsheets, APIs).

Clean the data by removing inconsistencies, handling missing values, and standardizing formats.

Perform feature engineering to extract meaningful variables (e.g., transforming raw sales data into time-based trends or customer behavior metrics).

Validate data quality and ensure it’s sufficient for training a robust model.

Tools: Hex’s computational infrastructure for scalable data processing, supplemented by Python libraries (e.g., Pandas, NumPy).

Duration: 3-7 days, depending on data complexity and volume.

Outcome: A preprocessed dataset ready for modeling, documented in the "Data Integration and Preprocessing Report."

3. Model Design and Training

Objective: Build and train a custom machine learning model optimized for your use case.

Steps:

Select an appropriate algorithm based on your goal (e.g., regression for forecasting, classification for churn prediction, clustering for segmentation).

Split the preprocessed data into training and testing sets to evaluate performance.

Train the model on Hex’s infrastructure, leveraging its computational power to handle large datasets or complex algorithms efficiently.

Tune hyperparameters (e.g., learning rate, depth of trees) to optimize accuracy and prevent overfitting.

Test the model against held-out data to measure performance metrics like accuracy, precision, or mean squared error.

Tools: Hex’s ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) integrated into its infrastructure.

Duration: 5-10 days, depending on model complexity and data size.

Outcome: A trained, validated machine learning model ready for deployment.

4. Model Deployment

Objective: Integrate the model into your business environment for practical use.

Steps:

Deploy the model on Hex’s infrastructure, configured as an API endpoint, cloud dashboard, or direct integration into your systems (based on your preference).

Conduct integration testing to ensure the model delivers predictions seamlessly within your workflow.

Provide initial predictions or outputs for review, allowing you to see the model in action.

Incorporate up to 2 rounds of revisions based on your feedback (e.g., adjusting thresholds, refining outputs).

Tools: Hex’s deployment tools, API frameworks (e.g., FastAPI), and your existing systems.

Duration: 3-5 days.

Outcome: A fully deployed "Custom Machine Learning Model" accessible to your team.

5. Documentation and Knowledge Transfer

Objective: Equip your team to understand and maintain the solution independently.

Steps:

Compile the "Data Integration and Preprocessing Report" to explain data preparation steps and insights.

Create the "Model Performance and Maintenance Guide," detailing how to interpret predictions, monitor performance, and retrain the model if needed.

Conduct a 1-hour virtual walkthrough to demonstrate the model’s use and answer questions, with a recording provided for future reference.

Offer 1 round of revisions to each document based on your feedback.

Tools: Documentation in PDF format, video conferencing for the walkthrough.

Duration: 2-4 days.

Outcome: Comprehensive deliverables ("Report" and "Guide") and a trained team ready to leverage the model.

6. Post-Deployment Support

Objective: Ensure long-term success and adaptability of the solution.

Steps:

Provide contact details for troubleshooting or questions post-deployment.

Offer recommendations in the maintenance guide for when and how to retrain the model (e.g., after significant data updates).

Monitor initial usage (if agreed) to suggest minor adjustments during the first 30 days.

Duration: Ongoing, with active support for 30 days post-deployment.

Outcome: A sustainable, value-generating AI solution with minimal dependency on external expertise.

Key Features of the Process

Scalability: Hex’s infrastructure allows handling of large datasets and complex models without performance bottlenecks.

Customization: Every step is tailored to your specific data and business goals, ensuring relevance and impact.

Transparency: Detailed documentation and a walkthrough keep you informed without requiring ML expertise.

Efficiency: By leveraging pre-built infrastructure and my expertise, you avoid the overhead of hiring specialized staff.

Timeline

Total duration: 2-4 weeks, depending on data complexity, model requirements, and feedback cycles.

Milestones: Data preprocessing completed (Week 1), model trained and tested (Week 2), deployment and documentation finalized (Weeks 3-4).

Final Deliverables Recap

Custom Machine Learning Model: Deployed and ready to predict.

Data Integration and Preprocessing Report: Insight into your data’s role.

Model Performance and Maintenance Guide: Tools for ongoing success.

This process ensures your business unlocks predictive power from its data with minimal friction, all while leveraging Hex’s robust infrastructure. Let me know if you’d like a deeper dive into any phase!

FAQs

What do I get with the fixed-tier packages?
Each fixed-tier package (Starter at $10,000, Pro at $18,000, or custom Enterprise) includes a custom machine learning model built and deployed on Hex’s computational infrastructure, tailored to your business needs. You’ll also receive a Data Integration and Preprocessing Report (detailing how your data was prepared) and a Model Performance and Maintenance Guide (to help you use and maintain the model). The Pro tier adds support for complex data and extra revisions, while Enterprise offers bespoke solutions—contact us for details!
How does the process work, and how long does it take?
The process starts with a discovery call to define your goals and assess your data. Next, I preprocess your data, design and train a custom ML model, deploy it into your workflow, and provide documentation and a walkthrough. Depending on data complexity and your use case, it typically takes 2-4 weeks from start to deployment. You’ll have opportunities for feedback and up to 2 revisions (more with Pro) to ensure the model meets your needs.
Why would I need the optional monthly support?
The optional $1,500/month support keeps your model performing at its best as your data or business evolves. It includes monitoring, performance optimization, quarterly retraining with new data, and priority troubleshooting. While the fixed-tier model works out of the box, monthly support ensures long-term accuracy and value—perfect if your data changes frequently (e.g., sales trends, customer behavior).
Do I need data scientists or technical expertise on my team?
No! My service is designed to deliver predictive power without requiring you to hire specialized data scientists or ML engineers. I handle everything—data prep, model building, deployment, and documentation—using Hex’s infrastructure. The deliverables include clear guides and a walkthrough, so your team can use the model with minimal technical know-how.
What kind of results can I expect from the model?
Results depend on your use case and data, but the goal is to unlock actionable predictions—like forecasting sales, reducing churn, or optimizing operations. For example, a retail client might predict demand to cut inventory costs, while a SaaS company could identify at-risk customers. During discovery, we’ll align the model with your goals, and the Performance Guide will show metrics (e.g., accuracy) to quantify its impact. With monthly support, those results stay sharp over time.

What's included

Custom Machine Learning Model
A fully developed, custom-built machine learning model designed to address your specific business needs. This model will be trained on your data assets to provide predictive capabilities, such as forecasting trends, optimizing operations, or identifying patterns, depending on your goals. The model will be deployed using Hex's computational infrastructure for seamless integration into your workflow. Format: Deployed model accessible via API or integrated into your existing systems (e.g., cloud-based dashboard or software pipeline), accompanied by a technical documentation file (PDF). Quantity: 1 model tailored to your primary use case. Revisions: Up to 2 rounds of revisions to fine-tune model performance based on initial testing and feedback. Additional Details: Includes a setup guide and basic performance metrics (e.g., accuracy, precision, recall) to ensure transparency and usability.
Data Integration and Preprocessing Report
A comprehensive report detailing how your data assets were prepared and integrated into the machine learning model. This includes data cleaning, feature engineering, and any transformations applied to maximize predictive value. The report ensures you understand how your data drives the model’s outcomes. Format: PDF document (10-20 pages, depending on data complexity). Quantity: 1 report. Revisions: 1 round of revisions to address any clarifications or additional insights you request. Additional Details: Includes visualizations (e.g., charts or graphs) of key data features and a summary of data quality improvements.
Model Performance and Maintenance Guide
A user-friendly guide outlining the model’s performance, how to interpret its predictions, and steps for ongoing maintenance. This deliverable empowers your team to use the model effectively without requiring in-house ML expertise. It also includes recommendations for retraining the model as your data evolves. Format: PDF document (5-10 pages) and a 1-hour virtual walkthrough session (recorded for future reference). Quantity: 1 guide + 1 session. Revisions: 1 round of revisions to the guide based on your feedback after the walkthrough. Additional Details: The guide will specify Hex infrastructure dependencies, expected model lifespan, and contact details for troubleshooting support.

Example projects