The multi-step process of preparing data and making it available to AI creates three challenges:
- Huge amounts of data are created and the impact storage has on the environment.
- A number of tools are required to handle this process end-to-end.
- The complexity of dealing with ever-changing requirements.
Processing large amounts of data and its impact on sustainability
Not only do data and storage requirements increase, but their processing complexity and environmental impact can also increase. However, when choosing infrastructure to reduce energy consumption, and It is designed to better support your AI needs so your organization can overcome these challenges.
It's important to remember that there is no such thing as cold data anymore. At best, we're talking about “warm” data that data scientists need to make quickly available on demand. Flash storage is the only solution that can provide this level of availability for the unstructured data that AI requires to succeed. This is because linking AI models with data requires a storage solution that allows data to be accessed reliably and easily at all times across silos and applications. This is often not possible with HDD storage solutions.
As more organizations sign up to science-based sustainability goals, they need to think about the environmental costs of storage. Data center operators are deploying more power-efficient technologies to address storage-intensive AI. Offloading this problem to someone else (such as a public cloud provider) will not solve the problem. Many companies will soon be required to report their Scope 3 emissions, including upstream and downstream environmental costs. Working with vendors that can reduce storage space, power, and cooling requirements is a key way to alleviate the challenge of storing the growing amount of data driven by AI.
Tools to support data scientists
Data scientists spend a lot of their time preprocessing and exploring data, so they need tools, resources, and platforms to efficiently perform this work when and where they need it. Python and Jupyter Notebooks have become everyday languages and tools for data scientists. Data ingestion, processing, and visualization tools all have one thing in common: they can be deployed as containers. Therefore, the ideal platform for data scientists to do everything they need to do is one that supports all these tools and allows containers to be deployed and run quickly, easily, and most importantly in a self-service manner. It is something to do.
According to 451 Research, 95% of new apps are written in containers, making quick and easy access even more important for data scientists. Not enabling this will negatively impact growth, digital transformation, customer service, and innovation across your organization. When data scientists are not properly supported, every area of the business is affected.
Leading AI organizations are now leveraging many of the aforementioned tools built on software infrastructures like Kubernetes to build “data science as a service” platforms. However, to be successful, these platforms must not only provide data frameworks and tools as a service, but also the data itself. Otherwise, the benefits of self-service will be negated. The key to success in this space is that the data platform is tightly integrated with his Kubernetes, making it easy to share, copy, checkpoint, and rollback the data itself.
Added flexibility for using as-a-Service
A key concern that IT organizations have regarding AI is the speed at which the market is evolving, far exceeding the average investment cycle of enterprise organizations. New AI models, frameworks, tools, and methodologies emerge regularly, and their introduction can have a significant impact on the underlying software and hardware platforms used for AI, and the underlying technology If changes are required, unplanned costs may be incurred.
As-a-Service consumption models should be considered as an effective tool to increase the flexibility of AI platforms. It also allows builders to easily incorporate new solutions and change the infrastructure to meet the ever-evolving needs of data scientists. Basically, it supports all 6 of his steps described in the first article.
Additionally, the as-a-Service model allows organizations to achieve their sustainability goals by better controlling energy costs through reduced power consumption and using only the resources needed at the time. Masu. Some Storage as-a-Service products have SLAs for payment of electricity usage, and by eliminating the disposal and replacement of technical refresh cycles and the e-waste they generate. Supports sustainability goals.
Solutions to address your AI data challenges
The AI data journey is one of data amplification. At each stage of your AI journey, data and metadata are created and added to. This will increasingly require infrastructure to support future AI developments. Data Science as-a-Service is what data scientists want to meet the demands of AI. This means tools and data delivered on demand and through automation. Achieving this requires the right software and hardware infrastructure, combined with the right consumption model, to make it successful and take organizations from data ingestion to innovation.