Streamlining cost and complexity, improving prediction accuracy, and improving management are some of the ways to scale machine learning models.
Fremont, California: In today’s organizations, data continues to grow in value as an asset due to its ability to drive significant competitive advantage and profitability. However, this can be very difficult, especially when it comes to revealing the insights contained in the data.
Machine learning (ML) is increasingly embedded in enterprise data pipelines, with algorithms being applied at scale by data engineers and scientists. However, traditional ML approaches can be difficult to use for large amounts of data.
The biggest barriers to applying ML at scale are:
Inaccurate prediction: Most data scientists use small subsets of data to build and train machine learning models, as memory and computational power limitations prevent them from handling large data sets. As a result, subsequent insights become less accurate and business decisions based on those insights are jeopardized. As a result, the model cannot reproduce its predictions on real-world data after being trained to fit the data.
Installation is slow and tedious. As the data scientist’s operational tools mature, managing and deploying predictive models across multiple environments becomes increasingly difficult. As a result, large-scale analytical initiatives face significant challenges and significantly increase production time.
How can companies implement machine learning models faster and at scale?
To overcome these barriers and reduce the overall time it takes for machine learning models to produce useful results, enterprises should choose databases with in-database ML capabilities. This approach has several advantages.
Reduce complexity and cost: Databases are already optimized for machine learning, eliminating data duplication and processing on alternative platforms. Additionally, users can use familiar tools, languages, and interfaces (SQL, R, Python, PMML, TensorFlow, etc.) to train, test, and deploy ML models, improving speed, productivity, and the overall user experience. increase.
Accurate prediction: In machine learning, the more data, the better the accuracy. In-database machine learning allows organizations to eliminate the limitations of small-scale analysis, such as downsampling or moving data to another system. To improve the accuracy of business decisions and forecasts, data scientists can analyze large data sets to uncover insights and patterns.
Focus on performance: ML algorithms commonly used in databases are natively available for data preparation, exploration, model evaluation, and more. As a result, many of the barriers associated with applying ML at scale are minimized or eliminated. Additionally, the database already supports Massively Parallel Processing (MPP) and advanced data compression, which can reduce analytic query times from hours to minutes.
