Organizations have more data than ever living on servers and in the cloud, but these data stores are seldom ready for analysis and practical use. Automated machine learning accelerates development of models that can be used in organizational analysis.
Most AI is powered by machine learning (ML). But for someone to build ML models, they need an in-depth understanding of your business, market, and goals.
Automated machine learning provides a way to overcome this challenge and accelerate development. By using expert system software, our data scientists are able to make ML and other AI-related strategies much more efficient.
Automated ML incorporates data science best practices. This on-board intelligence enables us to scour open source libraries for the frameworks that are most relevant to your objectives. It then helps us isolate the best algorithms and begin training and tuning them to produce the most accurate prediction or decision.
Automated ML helps us to identify and prepare datasets for production. Organizations have more data than ever at the edge, core, and cloud, but these data stores are seldom ready for analysis.
Data often needs to be normalized, partitioned, and feature engineered to avoid compromising performance and results. Automated ML makes these processes much less time-intensive and helps us to ensure that the right data is delivered to the right location with the right level of throughput.
Using intelligence enables us to blend algorithms into effective models with less human intervention or trial and error. This leaves more time for our data scientists to work with you on deploying your solution and collaborating with your data analytics team on additional AI opportunities.