You will get AI & Data Workflow
Project details
Perfect for businesses wanting to leverage AI without the overwhelm, this package delivers a complete data ingestion workflow using S3, Python, and Lambda. I clean, structure, and analyze your data using tools like Pandas and NumPy, then build a lightweight machine learning model (classification or forecasting) to generate meaningful insights. You’ll receive recommendations for deployment, plus visual dashboards built with Matplotlib, Seaborn, or Tableau to help your team interpret results at a glance. From forecasting trends to automating document processing, this package brings AI to your everyday operations.
Machine Learning Tools
Amazon SageMaker, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, R, SciPy, SQL, TableauWhat's included
| Service Tiers |
Starter
$900
|
Standard
$1,500
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 12 days |
Number of Revisions | 0 | 1 | 1 |
Number of Scenarios | 1 | 1 | 1 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$300
Additional Revision
+$300
Additional Model Variation
(+ 2 Days)
+$120
Additional Scenario
(+ 2 Days)
+$175
Additional Graph/Chart
(+ 1 Day)
+$50
Model Documentation
+$50
Source Code
+$10Frequently asked questions
About McKenzie
AI & Machine Learning Engineer | AWS Specialty
Aurora, United States - 2:13 am local time
AWS-certified Cloud Engineer and AI Practitioner skilled in deploying
and managing cloud infrastructure, automated workflows, and machine
learning (ML) models.
SKILLS
Programming: Python (Pandas, NumPy, Boto3, Lambda), R (in progress), Git,
Terraform
Data & AI: Data Manipulation, ML Algorithms (Regression, Classification,
Clustering), Feature Engineering, Model Evaluation & Deployment, Data
Visualization (Matplotlib, Seaborn, Tableau), Generative AI & LLM, Data
Wrangling / Cleaning, Statistical Analysis, Amazon Bedrock
Cloud & DevOps: AWS (S3, Lambda, EC2, VPC, IAM, SageMaker, CloudTrail,
CloudWatch), Docker, Infrastructure as Code (Terraform), CI/CD
In Development: SQL / NoSQL, MLOps, Big Data Tools (Spark, Hadoop)
Projects:
Cloud Setup & Modernization (Available on Youtube)
Machine Learning Model for Dinosaur Classification
Secure Image Transformation Pipeline
Steps for completing your project
After purchasing the project, send requirements so McKenzie can start the project.
Delivery time starts when McKenzie receives requirements from you.
McKenzie works on your project following the steps below.
Revisions may occur after the delivery date.
Midway Check-in Call
This can be a video or phone call in order for me to update you on the progress of the project and finalize requirements before the final push.