The Conference for Machine Learning Innovation

How AutoML can give companies, with few data scientists, a key advantage

Session
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos
Thursday, November 24 2022
14:00 - 14:45
Room:
Stage 2
Infos
Tuesday, November 29 2022
10:15 - 11:00

Over the last 10 years, there have been rapid advances in machine learning and data analysis. Software solutions like Automated Machine Learning can automate the process of data preprocessing, hyperparameter tweaking, and selection of best performing or most predictive model. However advanced algorithms in autoML have allowed for more functionality, like feature selection for the best interpretable model, models for aggressive feature selection, or for survival analysis. Feature selection was a process that took months, and engineering effort by several data scientists, using traditional methods.
Scaling a data science practice is challenging, time-consuming, and expensive. Whether that be discovering treatments, repurposing drugs, or understanding what triggers a disease. With Automated Machine Learning, you can empower data analysts, software engineers, and BI professionals to build and benefit from predictive models, while acquiring knowledge from interpreting the outputs. Through Data Science automation, a life-sciences team can be more productive, while freeing up the time of a data scientist to focus on understanding the problem and the potential solution.

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Behind the Tracks