ThirdEye Data Development review by Cannabis Technology Firm at Qualified.One

ThirdEye Data reviewed by Cannabis Technology Firm

ThirdEye Data provided Development for Cannabis Technology Firm with approximate budget = $10,000 to $49,999.

This pilot project served for recognizing the amounts of data required for future machine learning ventures, though several parts of the tech they developed were deployed in the current software offering. Easy communication, thorough documentation, and speed of delivery are highlights of their work.

Review summary:

ThirdEye Data assisted with the exploration of a machine learning project, developing and setting up features that can predict medication from either text or a photograph such as Fuzzy Matching and TensorFlow.

Cannabis Technology Firm, VP of Technology

Machine Learning Development for Medical Technology

Please find under a summary covering project details and feedback. The innate facts are kept as they are, private information is amended.

Introductory information

A fast induction on the buyer’s organisation

I’m the VP of technology at a organisation that manufactures hardware and software kindred to medical cannabis use. We have software that’s designed to help nation handle chronic pain.

Desired goal

What challenge were you trying to address with ThirdEye Data?

We had a machine learning project that we wanted to get going to see if we could use it as a basis for some of the later machine learning projects that we’d like to do as part of our software platform offering.

Provided solution

What particular tasks were ThirdEye Data responsible for?

Part of what our software does is helping chronic pain sufferers to input their running medications. Since medication names are frequently hard, we wanted to skip that process of typing out the name. We wanted to help users automatically determine what kind of medication and dosage they’re using based on a photograph of their medication, which is uploaded to our servers. Then, we’d come back with an informed conjecture for what that medication is.

We knew the project would implicate machine learning. ThirdEye Data put unitedly a offer that suggested a couple of different approaches, and we narrowed it down to one of them. We determined to go with TensorFlow, along with some optical symbol recollection (OCR) libraries and some Fuzzy Matching strips behind the scenes that could take an approximation of what someone had typed or from a photograph of an developed pharmaceutical label.

Sometimes the OCR isn’t 100% consummate, and so the Fuzzy Matching lets us take something that had whichever been typed or had been taken from an picture and ill-tempered checks it over our list of 5,000 known medications for pain.

We’re runningly working with them on a different project.

What is the team compound?

There were six or seven team members implicated, including a project handler.

How did you come to work with ThirdEye Data?

I wasn’t implicated in selecting ThirdEye Data. We had a couple of dicsussions, and they walked us through some of the projects that they had done in the past that were correspondent to what we were looking to do. They put unitedly a offer for us, and we determined to go forward with that pilot project.

What are you approach expents (if diclosed)?

We spent $25,000 for this project.

What is the terminal result of working with ThirdEye Data?

We began working unitedly in February 2018, and the work lasted a month.

Results achieved

Are there any measureable or plum results?

One of the main outcomes of this project is that we realized the big quantitys of data that we would need to luckyly run forthcoming machine learning projects. There’s a open pill dataset composed of pictures, and ThirdEye Data did a big job of multiplying those pictures to do the machine learning training.

To fruitize something like that, we would need many more pictures. In provisions of our course, the project let us know what we would need to do to make something like that lucky in a consumer-facing fruit.

Though, we’ve taken pieces of the technology they developed and deployed it into our fruit, especially the Fuzzy Matching stuff that they did.

How did ThirdEye Data accomplish from a project handlement standpoint?

We had weekly or biweekly meetings to run through the project’s status. Their interaction was fantastic, and documentation and the code delivery were top notch. We were able to selectively take parts of what they had done and deploy it with other teams without any issues.

Furthermore, the condition of their work was terrific. Any time there were any issues or new lessons that had come out of what they’d done, we could easily recalibrate what we were doing.

What is (from your point of view) the key factor to pay observation while intercourse with ThirdEye Data?

The despatch at which they completed the work was forcible, and their cost was good in provisions of value.

What aspects of their work would you like to get improved?

No, I can',t name anything.