October 14th, 2020

The Challenges and Benefits of AutoML

RSS icon RSS Category: AutoML, Driverless AI, Machine Learning, Responsible AI

Machine Learning and Artificial Intelligence have revolutionized how organizations are utilizing their data. AutoML or Automatic Machine Learning automates and improves the end-to-end data science process. This includes everything from cleaning the data, engineering features, tuning the model, explaining the model, and deploying it into production. AutoML accelerates your AI initiatives and can help make data scientists more effective and efficient at solving problems and providing business value. 

Last week, we hosted a live broadcast on H2O.ai’s Linkedin with John Spooner, EMEA Head of Artificial Intelligence at H2O.ai, and Harib Bakhshi, Lead Data Scientist at H2O.ai, who discussed the Challenges and Benefits of AutoML. 

Check out some key takeaways of this conversation:

 

  • What is AutoML?

For John and Harib, there tends to be a lot of ambiguity in the terminology that gets used in the industry when people talk about AutoML. If we consider the Machine Learning process and everything it takes to build the Machine Learning model, AutoML consists of trying to automate as much of that process as possible. AutoML is looking at areas where there is repeated work, it analyzes how certain parts of that process can be enhanced and makes data science teams more effective by automating all these processes.

 

  • Why is there such a big interest in Machine Learning now?

There is a lot of excitement around Machine Learning and AI and Harib sees several reasons for that. First, it is important to note that the math that gets used in the backend is old, this hasn’t changed over the years. What has changed though, is that there has been a serious reduction in compute costs over the past few years, so it is now cheaper to have very powerful computers to process the calculations. Another reason to explain the excitement around AI and Machine Learning is that these technologies allow us to handle different kinds of datasets: numeric, text, image… which considerably expands the range of possibilities. 

 

  • Accountability is still key

The downside of having Machine Learning models making automated decisions on a daily basis is that it makes it difficult to determine who is accountable for these decisions. In regulated industries like Financial Services or Healthcare, accountability is key. Harib recommends having some manual checkpoints where human beings can intervene and sign off on parts of the process. This can add the accountability needed and help with the regulation and governance.  

H2O Driverless AI makes it possible to automate the machine learning processes and helps the data science teams to work on projects faster and more efficiently. Find out more here.

Interested in watching the recording of the session and learning more? Click here.

About the Author

Eve-Anne Tréhin

After a successful experience in Marketing working for various software providers such as Aptean and Avolin, Eve-Anne is excited to join H2O.ai as EMEA Field Marketing Manager. In her previous roles, Eve-Anne was in charge of planning and running Marketing programs for different solutions of the companys’ portfolios and across several regions in order to generate leads, increase sales pipeline, build brand awareness and increase customer engagement. Eve-Anne is French, but was born and raised in Germany, she did part of her studies in Brussels and in London, which gave her a passion for multiculturality and foreign languages. She holds a bachelor’s degree in translation and a master’s in marketing, and was also recently certified in Project Management and Digital Marketing.

Leave a Reply

What it takes to become a World No 1 on Kaggle

In conversation with Guanshuo Xu: A Data Scientist, Kaggle Competitions Grandmaster, and a Ph.D. in

May 3, 2021 - by Parul Pandey
Unwrap Deep Neural Networks Using H2O Wave and Aletheia for Interpretability and Diagnostics

The use cases and the impact of machine learning can be observed clearly in almost

April 28, 2021 - by Shivam Bansal
Fallback Featured Image
Sign up for your free trial and get hands-on experience with H2O AI Hybrid Cloud

Hey Makers, today we launched our 14-day free trial of H2O AI Hybrid Cloud, giving

April 26, 2021 - by Ana Visneski and Jo-Fai Chow
Shapley summary plots: the latest addition to the H2O.ai’s Explainability arsenal

It is impossible to deploy successful AI models without taking into account or analyzing the

April 21, 2021 - by Parul Pandey
H2O.ai logra gran posicionamiento en integridad de visión en el cuadrante Visionarios del Cuadrante Mágico de Gartner 2021 para Data Science y Machine Learning

En H2O.ai, nuestra misión es democratizar la IA y creemos que impulsar el valor de

April 11, 2021 - by Read Maloney, SVP of Marketing
Safer Sailing with AI

In the last week, the world watched as responders tried to free a cargo ship

April 1, 2021 - by Ana Visneski, Jo-Fai Chow and Kim Montgomery

Join the AI Revolution

Subscribe, read the documentation, download or contact us.

Subscribe to the Newsletter

Start Your 21-Day Free Trial Today

Get It Now
Desktop img