CEO/Co-Founder @ mltrons
Automated Machine Learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning & data science.
Machine Learning is a branch of Artificial Intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Machine learning has provide us some significant breakthroughs in various industries. Areas like financial services, retail, healthcare, banking, and more have been using machine learning systems in one way or another, and the results have been very promising.
Machine Learning today is not just limited till R&D but has permeated into the enterprise domain. However, the traditional machine learning process is heavily human-dependent, and not all businesses have the resources to invest in experienced data science team. Even where the companies possess the resources, data scientists & engineers, these professionals have to spend hundreds of hours per month to building and maintaining these machine learning systems.
Our research has also show that most of the current pool of data scientists lack domain expertise and therefore, need to work with professionals form different departments to solve a specific problem, e.g., predicting which customers are more likely to buy a product. These department reps are experts with deeper business knowledge and analytical skills but lack predictive analytics skills, machine learning in specific.
Automated ML democratizes the machine learning model development process, and empowers its users, no matter what their data science expertise is, to identify an end-to-end machine learning pipeline for any problem
Automated Machine Learning is giving rise to the Citizen Data Scientist by making it easier to build and use machine learning models in the real-world without writing code. Automated Machine Learning incorporates the best machine learning practices from top-ranked data scientists, state-of-the-art open-source libraries to make machine learning and data science more accessible across the organization.
Here is the traditional model building process:
As it can be seen form Figure 1 above, developing a model with the traditional process is extremely time consuming, repetitive and tedious. Automated Machine Learning application automatically performs the model building tasks that usually require a skilled data scientist. Instead of taking weeks or months, the automated machine learning system is fast, and usually requires days for business users/data analysts to build 100s of models, make predictions and generate insights. The machine learning automated for data analysts allows organizations to achieve more in less.
AutoML is making it possible for businesses in industries like healthcare, FinTech, banking and more - to leverage advanced machine learning and AI technology that was previously limited to organizations with large resources at their disposal. By automating most of the machine learning modeling tasks, AutoML is enabling business users and data analysts to implement machine learning solutions with ease and focus more on solving complex business problems.
Data Analysts & Business Users across industries can use Automated Machine Learning to:
There are many tools out there and each has its own internal functionalities but we'll simply focus on mltrons Automated Machine Learning. Users can build new projects and run ML experiments with five simple steps by using a very intuitive Web User Interface.
*mltrons Automated Machine Learning curates state-of-the-art libraries like AutoKeras, PyTorch, TensorFlow, H2O, TPOT, Caffe, SageMaker, and AlphaD3M to build the best fit model for the data. Mltrons also provides GPUs to handle larger amount of data.
** mltrons AutoML for data analysts allows them to use "what-if" scenario simulator to create strategies for multiple outcomes. This fills the gap between the data analyst/scientist & executive decision maker.
It’s about taking on as many small projects as you can handle in order to generate value quickly. With automated machine learning, you can get multiple wins under the belt and complete multiple use-cases within your organizations in less time that will build up momentum and make it possible for your organization to iterate and expand the monetization of data.
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