You will get a data collection service to power your machine learning project
Project details
I have over 8 years experience building machine learning models, and models require data. I almost always have to collect the data, but sometimes the data is provided. Whether the data is collected or provided, the data has to be thoughtfully translated to an appropriate output format, whether it is a .csv file, a Google Doc, a SQL database, or a NoSQL database. Thoughtful translation depends on requirements, but with a mind toward future requirements as the project develops.
Some projects are quick and dirty, some projects are fairly straightforward, and some projects are complicated. What kind of project do you have?
Some projects are quick and dirty, some projects are fairly straightforward, and some projects are complicated. What kind of project do you have?
Data Tool
PythonWhat's included
| Service Tiers |
Starter
$30
|
Standard
$40
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 3 days |
Number of Sources Mined/Scraped | 1 | 5 | 10 |
Number of Revisions | 0 | 0 | 0 |
About Kahlil
Senior Machine Learning Engineer
Chicago, United States - 5:10 am local time
Wow computing moves fast; if you don't stop and look around from time to time, you might miss where the industry quantum leapt. Reflecting on the past few years of computing advances, developing tests and testing developments, it is not always easy to see which new state of the art is meaningful, or which applications of new advancements deliver quantifiable gains.
But I have been working toward that:
- I have developed deep learning models, and fine-tuned models given cost and time constraints.
- I have tested various model architectures against transformers and other advances to determine if new approaches fit specific datasets and use cases.
- I have explored whether LLMs outperform specially designed model architectures on specific datasets and use cases.
- I have explored local LLMs, benchmarking numerous models, model sizes, and model quantizations, and fine-tuning against internal data sets.
- I have containerized workflows using Docker and Podman, for automation and reproducibility across compute.
- I have done cost comparisons on time and compute to gauge whether benefits outweigh costs.
Awash in an unending sea of data, I bent data toward meaningful insights by developing predictive models in Python and R, using pandas, scikit-learn, tensorflow, spark, mllib, LaTeX, shiny, hive, sql, jira, bitbucket, and bash, among other things, within the context of risk, specifically market and investment risk.
In previous roles, and during my spare time, I do sentiment analysis and quantitative research, but mostly wrangling raw data while trying to stay on top of new developments in artificial intelligence and the latest package updates.
Steps for completing your project
After purchasing the project, send requirements so Kahlil can start the project.
Delivery time starts when Kahlil receives requirements from you.
Kahlil works on your project following the steps below.
Revisions may occur after the delivery date.
Access the data
I access the data, proving accessibility for the script. I get the data features, to implement in the script.
Complete script
I complete the script to access the data, collect it, and output it to the required format.