Success in any form of tech-driven product or service is influenced by three main actors:
- People: skilled employees who are given well-defined roles to train data.
- Process: a system that guides people to move from one task to another until data training is completed.
- And Tools: that enable people to train data, report, and complete tasks defined within the process.
How people influence data training quality
To carry out complex data training, you need employees to be trained on the latest skills and processes.
If you choose to carry out data training in-house, you need to train your employees on the tools and skills required. This takes time and money.
While many AI and ML projects never develop into money making products or services, can your organization afford to train a large group of people to fulfil moonshot projects?
A better option might be to outsource your data training to a vendor.
Again, how sure are you about the skills and qualifications of the vendor's employees to carry out tasks to train your data?
Getting your data trained by skilled people is only the beginning.
The skills need to be channeled and guided by a well-defined process for your AI/ML projects to succeed.
How the process influences data training quality
A good AI/ML data training process should address four aspects:
If you choose to outsource data training, you need to find a vendor who is flexible to seamlessly fit into your process, quality and communication standards.
If you do not have a process in place and want your data to be trained by a vendor, then you must co-create the process with your data training partner.
Now we come to the third aspect that influences data training quality.
The tools you or your vendor use to train your data.
- The process should be thoroughly defined with clear parameters to achieve for each team.
- A clear line of communication should be established between the data training team (in-house or vendor) and the engineering team to quickly iterate training, testing, and validation of ML models.
- The process should have strict quality controls at every juncture to sustain quality.
- And the process should enable the above two to happen at scale.
How data training tools affects quality
The tools you need to train your data is defined by the type of data you need to structure.
Incompatible technologies (tools) eat up your time and money.
Poor-fit tooling can dramatically slow down and even stop the most innovative projects - or make the process more costly.
You need to assess and find the right data training technologies (tools) that can:
- enable people to accurately train and structure your data,
- speed up your data training work,
- and reduce training timeline and costs.
Quality data training should never be expensive nor a headache
Sourcing, training, and structuring data to train, test, and validate your machine learning models is a difficult task to carry out in-house.
That's why you need a reliable data training partner who can quickly understand your project needs, timeline and prepare your data swiftly and hand it over for ML model training and testing.
We are Ex-Yahoo!s with over 15 years of experience in preparing data for AI/ML modeling. Get your data trained on time and budget now.
Visit www.traindata.us to learn more.