Optimized by Design: Solving the Limited Memory Challenge for Machine Learning
Before we delve into issues pertaining to Machine Learning (ML) and storage optimization, it’s crucial for the reader to understand what ML is and how it works.
A subset of Artificial Intelligence (AI), ML is a technique centered around training algorithms to make decisions. Designers train ML algorithms by feeding them large amounts of data, enabling it to learn things from processed data.
ML begins its learning process much like a human brain in that it first observes data. This data comes in the form of instructions, experiences and examples.
The algorithm then searches for patterns within the data, remembers those patterns and uses them to make better choices in the future.
Though designers provide ML algorithms with examples along the way, the primary goal is to design AI structures capable of learning automatically without any assistance or intervention from the user.
The Processes Involved in Machine Learning
Similar to predictive modeling and data mining, the processes of ML are intended to pinpoint patterns in large data sets and form connections. Through these connections, ML can then devise solutions to quandaries. One example of ML at work is Google’s AdSense Auto Ads which uses ML to automate the ad placement process.
AdSense Auto Ads “reads” webpages in order to discern what kind of advertisements may do well on them. In addition to that, it attempts to predict the best location on a webpage to place the ads, as well as how many.
Another way people see ML at work daily is when “recommendation engines” serve them ads based on their “interests.” ML is personalizing online ad placement in near real time. Google also uses ML for security purposes, including fraud detection, network security threat detection and spam filtering.
A few other Machine Learning use cases include:
- Building news feeds
- Predictive maintenance
- Financial trading
- Natural Language Processing (NLP)
- Smart vehicles
- Airline industry
How Machine Learning Works (Simplified)
ML algorithms fall under two general categories: supervised algorithms and unsupervised algorithms.
Supervised ML needs either a computer scientist or data analyst who is trained in Machine Learning to feed the algorithm with the necessary input data and expected output. Moreover, they will provide the algorithm with feedback concerning its accuracy during its training.
Unsupervised ML, on the other hand, uses Deep Learning (DL) to iterate data until it reaches a conclusion. Called neural networks, unsupervised ML algorithms are utilized for complex tasks such as image recognition, natural language generation and speech-to-text.
Unsupervised ML algorithms use banks of associations to explicate a constant stream of new data. It’s also worth noting that unsupervised ML algorithms have only recently become realized since the age of Big Data as it requires so much data for these processes to work.
Challenges of Machine Learning and Storage
At present, the accessibility of data and open-source ML frameworks have simplified the process of deploying Machine Learning systems.
While it’s relatively straightforward for data scientists and developers to write code for “training” basic, non-distributed ML models with data at rest, production-grade systems continue to pose a big challenge for most. And of course, many claim a medley of solutions promising to “operationalize” the ML process.
However, data scientists wind up operating more as a keeper of technology zoo while still fretting over issues such as data cleansing, serving infrastructures, feature extractions, among other things.
After everything is said and done, they’re still facing the problem of enabling distributed data storage and streaming large data sets. And concerning model engineering, dirty or coarse data needs to be cleansed.
For training models, an ML Framework must be installed on every machine. An ML Framework is what allows developers to quickly and painlessly build ML models minus the need to deal with the nuts and bolts of algorithms.
Once ML models are trained and large amounts of data begin to accumulate, model management is found to be the next issue on the list.
Finally, when it comes to the model serving, deployments must experience minimal downtime.
Optimizing Storage for Machine Learning
Our Xanadu X-AI Series has been engineered especially for AI-enabled data centers. The X-AI Series comes optimized for training, replication, data transformations, ingest and egress, as well as metadata and small data transfers. Furthermore, you can expect:
- Full integration
- Full GPU saturation
- Capacity-efficient AI storage
- Highest resilience, reliability and security
- A unified namespace
- Multi-tenancy and quota support
If you are interested in learning more about high-performance storage that is atomized by design for AI applications such as Machine Learning and Deep Learning, contact us today and speak to one of our knowledgeable experts.
