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 provided us with 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 promising.
Machine learning today is not just limited to 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, infrastructure & maintenance. Even where the companies do have the resources, data scientists & engineers have to spend hundreds of hours per month to building and maintaining these machine learning systems.
Our research has also shown that most of the current pool of data scientists lack domain expertise and therefore, need to work with professionals from different departments to solve a specific problem, e.g., predicting which customers are more likely to buy a product. These reps are experts with deeper business knowledge and analytical skills but with no machine learning skills, e.g., marketing analytics manager. This process is very time consuming, slow and stressful.
Machine learning 101
1. Implement ML solutions without programming knowledge
2. Save time and resources
3. Leverage data science best practices
4. Provide agile problem-solving
Automated
Machine Learning
Machine learning today is not just limited to R&D but has permeated into the enterprise domain. However, the traditional machine learning process is heavily human-dependent, and not all the business have the resources to invest in experienced data science team, infrastructure & maintenance. Even where the companies do have resources, data scientists & engineers have to spend hundreds of hours per month to building and maintaining these machine learning systems.
Most of these data scientists lack domain expertise, therefore, need to work with representatives from different departments to solve a specific problem, e.g., predicting which customers are more likely to buy a product. These reps are experts with deeper business knowledge and analytical skills but with no machine learning skills, e.g., marketing analytics manager. This process is very time consuming, slow and stressful.
John Smith
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:
Figure 1
Identify the ML Problem: Classification, Regression or Time-Series
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How Automated Machine Learning Works?
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For Data Analysts & Business Users
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Deploy, Make Predictions & Generate Insights
mltrons Machine Learning platform creates 100s of different combinations of algorithms from state-of-the-art libraries and in-house algorithms.
mltrons Machine Learning allows data analysts to use APIs to integrate predictions into the workflow or can use "what-if" scanerio simulator to create strategies for multiple outcomes.
Choose what your want to predict or what your outcome is
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Get Started Today
mltrons Machine Learning platform creates 100s of different combinations of algorithms from state-of-the-art libraries and in-house algorithms.
mltrons Machine Learning allows data analysts to use APIs to integrate predictions into the workflow or can use "what-if" scanerio simulator to create strategies for multiple outcomes.
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Executive's Guide to Machine Learning
John Smith
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Our state-of-the-art platform allows organizations of all sizes and business users of all skill levels to quickly and easily leverage the power of machine learning and AI to solve problems. With mltrons, organizations in various industries have improved operations, increased customer retention, and identified key factors relevant to everything from demand forecasting to predicting customer lifetime value.
Data Analysts & Business Analysts across industries can use Automated ML to:
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Artificial Intelligence is "Human Intelligence Exhibited by machines"
Automated Machine Learning + mltrons
Using mltrons Automated ML, you can design and run your automated ML experiments with five simple steps:
Wednesday 21st August 2019
by Raheel Ahmad
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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 enables business users & data analysts to implement machine learning solutions with ease and focus on solving complex business problems.
Automated machine learning (AutoML) represents a fundamental shift in the way organizations of all sizes approach machine learning and data science.
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VISIT US AT MLTRONS.COM FOR MORE INFORMATION
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Machine Learning
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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.
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It’s about taking on as many small projects as you can handle in order to generate value quickly. With mltrons automated machine learning, you can get multiple wins under the belt and complete multiple use-cases within your organization that will build up substantial momentum and make it possible for you to iterate and expand your monetization of your data.
Train & Evaluate
100s
of Algorithms
Our state-of-the-art platform allows mid-sized organizations and data analysts/business users of all skill levels to quickly and easily leverage the power of machine learning and AI to solve problems. With mltronst, companie have experienced improved operations, increased customer retention, and identified key factors relevant to everything from ldemand forecasting to classify loyal customers.
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Specify the data source and format of the labelled training data
As it can be seen from the Figure 1 above, developing a model with the traditional process is extremely time consuming, repetitve 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 takes days for data analysts or business analysts to develop hundreds of models, make predictions and generate insights. The machine learning automation for data analysts allows organizations to achieve more in less.