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How Machine Learning Can Be Used at Scale?
Data is an asset in today’s organizations. It is becoming more and more valuable because it can drive significant advantages and profitability when used properly. But when it comes to unlocking insights contained in data, it can be difficult.
Fremont, CA: Many businesses are building teams with data engineers and scientists to adopt machine learning into their data pipelines and apply algorithms to data. But using traditional machine learning on a large volume of data can be difficult.
Let’s explore some barriers to applying ML at scale:
Delayed Time to Insight: Moving large volumes of data between the systems can be time-consuming. In addition, tracing relationships between disparate data sources can be far more complex to handle for ordinary business intelligence tools. As a result, they end up relying on predictive analytics on smaller data sets.
Inaccuracy in predictions: Since large data sets cannot be processed due to computational limitations and memory, most data scientists have started to build and train machine learning models for small data sets. But this will reduce the accuracy of data and put the business at risk.
Increased Overheads: Moving data, rebuilding machine learning models, building down samples and running them on multiple platforms typically required additional hardware, developer tools, software, and resources. Many ML algorithms are not built for distributed processing. As a result, data movements in and out can increase the cost and effort of the model.
How business implements ML models faster: In order to overcome these barriers and reduce the overall time taken by the ML models to produce a result, businesses should choose databases that provide in-database capabilities. This approach has several benefits.
High Performance: In-database machine learning uses many common Machine Learning algorithms, which include data preparation, exploration and model evaluation functions. As a result, it eliminates or minimizes barriers associated with applying machine learning at scale.
Reduced Cost and Complexity: Reducing overhead and complexity eliminates the need for data duplication and running it on multiple platforms. Additionally, users can train, test and deploy machine learning models using familiar tools, languages and interfaces; It helps to improve speed, productivity and overall user experience.
Increased Prediction Accuracy: A well-known fact of machine learning is large data equals greater accuracy. In-database machine learning eliminates the constraints of small-scale analytics, like creating down samples or moving data to different systems. As a result, it increases prediction accuracy and better business decisions.
Better Machine Learning Model Management: In the past, Machine Learning exists on someone’s laptop or system. If that individual or system is unavailable, then the model might not be accessible. But when the database trains models, that can be shared with all systems using the platform.