With the introduction of cloud computing, it is now possible to harness the immense and virtually limitless computational potential for next-to-nothing. Global servers can be accessed quickly with no limit on storage or processing power—the possibilities are truly endless!
AI and ML are two dominant technologies in the cloud world. They take advantage of high speed, infinite scale, flexibility, and instant provisioning to perform their modelling tasks with ease.
Still, cloud computing is unable to solve all the AI and ML problems. Data preparation and pipelining consume much time on the cloud. Instead of analyzing data, data scientists invest a lot of time in these activities.
There are many challenges when it comes to data preparation and pipelining and cloud computing cannot be solved alone.
The data preparation scripts in R or Python that most data scientists write take up 80% of their time. This process is tedious and arduous to edit, manage, and maintain which leaves little room for the actual analysis portion.
Errors happen in the course of writing code for any task, and data scientists are no exception. They may be required to rework their own code at a future date due to errors that have been made along the way.
For Removing bias, the initial need is to feed the AI/ML model mass amounts of data for their training. But stated earlier, data grooming is heavily time-consuming.A relatively quicker way is data integration and blending.
Data assets should be developed in a way so that it can be reusable for the future. This cuts down the entail of rework by data scientists. Because of Manual writing, data asset reuse becomes difficult as data scientists must painstakingly hunt via the code to make requisite changes.
Today, companies need to have their data assets to be reproducible for applied and compliance purposes. This implies that any actions taken with the data must be documented, including where, when, and how of it.
To create reusability and reproducibility properties, all the dataset actions must be recorded.
Another major bottleneck is the reimplementation of data models. After a new data model creation, the next is to hand it off to operations, re-implement it for utilization at scale. Re-implementation creates a disintegrated process where no individual group is in charge of the outcome, resulting in delays and errors.
The solution is the implementation of commercial, custom-built data preparation and pipelining platforms. Several organizations are turning to them to manage their AI/ML cloud programs. The platforms come furnished with abundant capabilities like:
A visual exploration interface to traverse through data at scale even if you deal with billions of records, you’ll quickly explore this data without getting lost.
A Data blending capability right on the data lake without moving it. It makes data blending speedy and more iterative. As less data is moved around, it becomes easy and fast to feed AI and ML data models for training.
The preparation and pipelining platforms can provide you with a streamlined process for plugging in your AI/ML models, running on your data lake at scale. Streamlined operations ensure that data compliance and transparency are present, as well as the entire process. This is possible because a company can audit it all on one platform only.
A metadata management capability for automatic capturing and recording of information in the form of data. If you backtrack or edit a function on your algorithm, this leads to an automatic revision of the metadata reflecting that change.
Data preparation is a tedious task, and the problem worsens while running AI and ML initiatives. Companies do not want their best data scientists to invest 80 percent of their time in such a tedious process— the ingested amount continues to increase and old methods become obsolete.
With commercial data preparation and pipelining platforms, companies can implement robust AI/ML programs making it easier for a company to prepare their data while also working towards compliance on how your information is being used.
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