An Intro to DataPrepOps
DataPrepOps is the operationalization of Data Preparation.
It’s full-stack Data Engineering for Machine Learning Data.
In short, it’s the process of applied technology and engineering best practices to convert raw data into ML-ready data.
Interested in how DataPrepOps can help you automate Data Preparation or manage Data Prep in production, at run time? Visit our Product page to learn more about how our Platform can help you.
Interested in the story and the technical details around DataPrepOps? Then read on, you’re in the right place!
The Genesis of MLOps
Machine Learning is complicated. And if you think that developing a model is hard, just try putting that model to production. That’s exactly why MLOps was invented: to enable any organization to deploy Machine Learning models to production seamlessly without the need to hire a full team of DevOps engineers and ML engineers.
But what is MLOps? MLOps is essentially DevOps for Machine Learning models, and it revolutionized Machine Learning in the late 2010s by enabling hundreds of organizations to push their models to production.
Preventing the next AI Winter
Traditional MLOps platforms might help put a model in production, but it won’t help reduce the costs of keeping that model running. That’s where DataPrepOps can help.
The advent of MLOps prevented countless ML projects from failing by ensuring that the models built by data scientists could be monetized as data products. But even with their models in production, organizations faced a major issue because of the astronomical cost of training and retraining to keep those models up to date. So those same projects were at risk again, this time not because companies could not monetize on it, but because of the absence of ROI.
That’s what we call the AI Cost Chasm, and our platform is designed to help you cross it!
The
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“Pushing models to prod is hard. Keeping them here is exorbitant.”
– Dr. Jennifer Prendki
Data Prep: From Unsuitable Manual Processes to Seamless Automation
DataPrepOps isn’t one single technology: it’s the application of Technology to solving the common pain points that data scientists face when preparing training data.
Flip the cards to learn how technology can help with each one of the problems.
In most organizations, data labeling jobs are still managed via email.
Your labels will be safely stored and backed up on the Alectio platform. You can download them whenever you would like.
There is no centralized repository for data scientists to collaborate with each other and their labeling provider.
On the Alectio platform, you can manage permission rights for your projects and give access to other users, so that they can collaborate with you.
Most labeling companies do not provide tools to visualize and search the labels.
The Human-in-the-Loop module is built to help you search for records that fulfill specific criteria such as information density or size of an object.
There is no standardized annotation format to make annotations easy to consume.
Alectio offers modules to convert your label files into any format that you would like, so that it is convenient for you to upload and download them.
Labeling companies are not always transparent about their pricing and timelines.
The Alectio Recommendation Engine not only finds appropriate partners based on your use case and criteria, but it also gives you precise quote and timeline, so that you know what to expect.
The performance metrics provided by labeling companies are biased and unreliable.
Because we are a neutral party, we have no incentive in misrepresenting the accuracy of the work of each partner.
The Big Data Lobby wants you to keep using more data because that’s how they make money.
The Alectio Selection Engine is meant to help you identify the useful records in your raw dataset, so that you can annotate just what matters.
When signing with a labeling company, you subject yourself to their strengths and weaknesses alike.
The benefit of a marketplace is that you will be able to work with a different partner for each project. You can leverage the strengths of each one of them.
No labeling process can ever be 100% accurate and labeling companies don't help audit the results.
The Human-in-the-Loop module is equipped with advanced auditing algorithms that leverage Anomaly Detection and Meta Learning to help you narrow down the records that need to be revised.
When you need to modify an annotation, you can easily loose track of the latest version.
In order to help you manage the changes you make to your labels, Alectio built an entire record/level versioning system to help with traceability.
Finding the right partner for your sue case normally requires month of research and expensive POCs.
The Labeling Partner Recommendation System leverages historical data on past job performance to recommend your perfect partner based on your use case and criteria.
The Data-Centric Revolution… at Risk?
Data-Centric AI is exciting and will drive the next wave of Machine Learning progress. But just like for Model-Centric ML, the industry needs access to the proper tools and workflows to reap the fruit of their labor. Without those tools, we are at risk for another AI Winter.
Alectio is the first end-to-end, full-stack, self-serve Data-Centric AI platform that regroups all the tools you’ll ever need under one umbrella.
DATA PREPARATION AS A SCIENTIFIC DISCIPLINE
- Raw data ≠ training data
- Data prep can be “high-tech”
- Bringing agility + expertise for data labeling
OPERATIONAL SUPPORT
- Building ≠ deploying
- ML as an engineering discipline
- ML has a lifecycle
DATA AS A FIRST-CITIZEN IN ML
- Data quality > data quantity
- Data value ≠ data quality
- Improving data > indiscernible model improvements
