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What is Automated Machine Learning?

Automated machine learning, as pioneered by DataRobot, replaces much of the manual work required by a more traditional data science process. But to be considered a trusted, end-to-end enterprise AI solution, a platform must meet a broader set of key requirements. DataRobot is the first, and only, enterprise AI platform to address all 10 steps required to effectively automate the building and deployment of advanced AI applications.

Why You Need Automated Machine Learning

Automated machine learning takes advantage of the strengths of both humans and computers. Humans excel at communication, engagement, context and general knowledge, as well as creativity and empathy. Computers and software systems are ideal for repetitive tasks, mathematics, data manipulation, and parallel processing — providing the power and speed to master complex solutions.


In short, automated machine learning is:

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An Expert System

Incorporating the best practices of the world’s top data scientists, the system automatically selects the best machine learning algorithms to test against your data and the business challenge at hand.

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Trustworthy

Provides human-friendly, easily-interpretable explanations for how a machine learning algorithm makes its decisions and retrains models when data suggests that patterns have changed.

Automated machine learning creates a new class of “citizen data scientists” with the power to create advanced machine learning models, all without having to learn to code or understand when and how to apply certain algorithms. Data scientists are also more productive as repetitive steps in the model building process are automated, allowing them to use their unique expertise for selecting and fine-tuning models.

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The 10 Steps of Automated Machine Learning

Automated machine learning replaces much of the manual work required by a more traditional data science process. But to be considered a complete automated machine learning solution, a platform must meet ALL of these key requirements.  DataRobot is the first, and only, machine learning platform to address all 10 steps required to effectively automate the building and deployment of machine learning models.


1 2 3 4 5 6 7 8 9 10

Data Identification

Data Preparation

Feature Engineering

Algorithm Diversity

Algorithm Selection

Training and Tuning

Head-to-Head Model Competitions

Human-Friendly Insights

Easy Deployment

Model Monitoring and Management

  • Step 1:

    Data Identification

    Data is the fuel that drives high-scale innovation with AI. Ironically, many organizations struggle to use their data effectively because of the overwhelming number of data sources that are available, and no clear way to identify the most trusted sources of data. The AI Catalog inside DataRobot serves as a centralized source of truth for data engineers, data stewards, data scientists, and analysts to gain self-service access to AI assets they can trust.

    Introducing DataRobot AI Catalog

  • Step 2:

    Data Preparation

    Every machine learning algorithm works differently and has different data requirements. For example, some algorithms need numeric features to be normalized, and some do not. DataRobot transforms raw data into the specific format that each algorithm needs for optimal performance then follows best practices for data partitioning.

    How Do Model Blueprints Add Value to DataRobot?

  • Step 3:

    Feature Engineering

    Feature engineering is the process of modifying data to help machine learning algorithms work better and is often time-consuming and expensive. DataRobot engineers new features from existing numeric, categorical, and text features. It knows which algorithms benefit from extra feature engineering and which don’t and only generates features that make sense given the data characteristics.

    DataRobot Automated Feature Engineering

  • Step 4:

    Algorithm Diversity

    Every dataset contains unique information that reflects the individual characteristics of a business. Due to the variety of situations and conditions, one algorithm cannot successfully solve every possible business problem or dataset. Some machine learning automation platforms only give users access to a few types of algorithms, but with DataRobot you get immediate access to hundreds of diverse algorithms, and the appropriate pre-processing, to test against your data in order to find the best one for your particular AI challenge.

    AIs are Individuals, Just Like People

  • Step 5:

    Algorithm Selection

    Having hundreds of algorithms at your fingertips is great, but in many cases users don’t have time to try each and every algorithm on their data. Some algorithms aren’t suited to the data, some are not suited to the data sizes, and some are extremely unlikely to work well on the data. DataRobot will only run the algorithms that make sense for your data.

    Can An AI Recommend the Best Algorithm for Me?

  • Step 6:

    Training and Tuning

    It’s standard for automated machine learning software to train the model on your data. DataRobot takes this a step further by using smart hyperparameter tuning, not just brute force, to tune the most important hyperparameters for each algorithm. The platform can also create ensemble models (also known as “blenders”) that combine the strengths of several algorithms and balance out the weaknesses of others. Ensemble models typically outperform individual algorithms because of their diversity. DataRobot finds the optimal algorithms to blend together and tunes the weighting of the algorithms within each blender model.

    Data Science Fails: There’s No Such Thing As A Free Lunch

  • Step 7:

    Head-to-Head Model Competitions

    You won’t know in advance which algorithm will perform the best, so you need to compare the accuracy and speed of different algorithms on your data regardless of which programming language or machine learning library they came from. You can think of it as a competition amongst the models where the best model wins. DataRobot builds and trains dozens of models for AI machine learning automation, comparing the results, and ranking the models by accuracy, speed, and the most efficient combination of the two.

    Competition in AI Blog

  • Step 8:

    Human-Friendly Insights

    Over the past few years, some automated machine learning tools and AI have made massive strides in predictive power, but at the price of complexity. It is not enough for a model to score well on accuracy and speed – you also have to trust the answers it is giving. And in regulated industries, you must justify the model to a regulator. DataRobot explains model decisions in a human-interpretable manner, showing which features have the greatest impact on the accuracy of each model and the patterns fitted for each feature. DataRobot can also provide prediction explanations to illustrate the key reasons why a specific prediction was made.

    Give me one good reason to trust artificial intelligence

  • Step 9:

    Easy Deployment

    Harvard Business Review once described a team of analysts that built an impressive predictive model, but the business lacked the infrastructure needed to directly implement the trained model in a production setting, which was a waste of time and resources. All DataRobot models are production-ready, and can be deployed in several ways on standard system hardware.

    Machine Learning Model Deployment

  • Step 10:

    Model Monitoring and Management

    In a constantly changing world, your AI applications will start to decay over time as the data that’s being used to make predictions is different than what the model was trained on. Unfortunately, figuring out when to replace an outdated model is difficult because traditional IT tools for managing software applications don’t effectively work for machine learning models. DataRobot MLOps provides a common framework for model deployment, monitoring, and governance no matter what data science language or software tool was used to create the model.

  • Step 1: Data Identification

    Data is the fuel that drives high-scale innovation with AI. Ironically, many organizations struggle to use their data effectively because of the overwhelming number of data sources that are available, and no clear way to identify the most trusted sources of data. The AI Catalog inside DataRobot serves as a centralized source of truth for data engineers, data stewards, data scientists, and analysts to gain self-service access to AI assets they can trust.

    Introducing DataRobot AI Catalog

  • Step 2: Data Preparation

    Every machine learning algorithm works differently and has different data requirements. For example, some algorithms need numeric features to be normalized, and some do not. DataRobot transforms raw data into the specific format that each algorithm needs for optimal performance then follows best practices for data partitioning.

    How Do Model Blueprints Add Value to DataRobot?

  • Step 3: Feature Engineering

    Feature engineering is the process of modifying data to help machine learning algorithms work better and is often time-consuming and expensive. DataRobot engineers new features from existing numeric, categorical, and text features. It knows which algorithms benefit from extra feature engineering and which don’t and only generates features that make sense given the data characteristics.

    DataRobot Automated Feature Engineering

  • Step 4: Algorithm Diversity

    Every dataset contains unique information that reflects the individual characteristics of a business. Due to the variety of situations and conditions, one algorithm cannot successfully solve every possible business problem or dataset. Some machine learning automation platforms only give users access to a few types of algorithms, but with DataRobot you get immediate access to hundreds of diverse algorithms, and the appropriate pre-processing, to test against your data in order to find the best one for your particular AI challenge.

    AIs are Individuals, Just Like People

  • Step 5: Algorithm Selection

    Having hundreds of algorithms at your fingertips is great, but in many cases users don’t have time to try each and every algorithm on their data. Some algorithms aren’t suited to the data, some are not suited to the data sizes, and some are extremely unlikely to work well on the data. DataRobot will only run the algorithms that make sense for your data.

    Can An AI Recommend the Best Algorithm for Me?

  • Step 6: Training and Tuning

    It’s standard for automated machine learning software to train the model on your data. DataRobot takes this a step further by using smart hyperparameter tuning, not just brute force, to tune the most important hyperparameters for each algorithm. The platform can also create ensemble models (also known as “blenders”) that combine the strengths of several algorithms and balance out the weaknesses of others. Ensemble models typically outperform individual algorithms because of their diversity. DataRobot finds the optimal algorithms to blend together and tunes the weighting of the algorithms within each blender model.

    Data Science Fails: There’s No Such Thing As A Free Lunch

  • Step 7: Head-to-Head Model Competitions

    You won’t know in advance which algorithm will perform the best, so you need to compare the accuracy and speed of different algorithms on your data regardless of which programming language or machine learning library they came from. You can think of it as a competition amongst the models where the best model wins. DataRobot builds and trains dozens of models for AI machine learning automation, comparing the results, and ranking the models by accuracy, speed, and the most efficient combination of the two.

    Competition in AI Blog

  • Step 8: Human-Friendly Insights

    Over the past few years, some automated machine learning tools and AI have made massive strides in predictive power, but at the price of complexity. It is not enough for a model to score well on accuracy and speed – you also have to trust the answers it is giving. And in regulated industries, you must justify the model to a regulator. DataRobot explains model decisions in a human-interpretable manner, showing which features have the greatest impact on the accuracy of each model and the patterns fitted for each feature. DataRobot can also provide prediction explanations to illustrate the key reasons why a specific prediction was made.

    Give me one good reason to trust artificial intelligence

  • Step 9: Easy Deployment

    Harvard Business Review once described a team of analysts that built an impressive predictive model, but the business lacked the infrastructure needed to directly implement the trained model in a production setting, which was a waste of time and resources. All DataRobot models are production-ready, and can be deployed in several ways on standard system hardware.

    Machine Learning Model Deployment

  • Step 10: Model Monitoring and Management

    In a constantly changing world, your AI applications will start to decay over time as the data that’s being used to make predictions is different than what the model was trained on. Unfortunately, figuring out when to replace an outdated model is difficult because traditional IT tools for managing software applications don’t effectively work for machine learning models. DataRobot MLOps provides a common framework for model deployment, monitoring, and governance no matter what data science language or software tool was used to create the model.

Automated Machine Learning Enables Your Entire Organization

Finding and retaining data scientists is often the hardest part of implementing AI and machine learning in an enterprise. With automated machine learning, you empower data analytics professionals and software engineers to build predictive models and embed AI into applications – all while making existing data science personnel more productive and satisfied.
research-statistics-search-1
Analytics Professionals
With practical, hands-on training and the support of DataRobot’s world-class team, data analytics professionals are quickly transformed into AI analysts that find and focus on what matters most to drive real business value.
coding-browser-code-text-editor-1
Software Engineers
Software engineers are crucial in driving value from machine learning models by integrating them into production systems. DataRobot delivers the training, tools, and support to enable software engineers to become AI engineers.
data-scientist-info-data-search-graph-1
Data Scientists
When the mundane tasks of model development are automated – like data partitioning, model tuning, feature selection, etc. – skilled data scientists accomplish radically more than they could with traditional hand-coded approaches. DataRobot also gives experts the flexibility to customize their models when needed.
delegated-group-user-people-man-team-1
Executives
When business leaders understand the importance of AI, and how to talk about and frame a machine learning project with their teams, they bring all of their domain knowledge and experience to bear in helping the company build AI applications.
  • research-statistics-search-1
    Analytics Professionals
    With practical, hands-on training and the support of DataRobot’s world-class team, data analytics professionals are quickly transformed into AI analysts that find and focus on what matters most to drive real business value.
  • coding-browser-code-text-editor-1
    Software Engineers
    Software engineers are crucial in driving value from machine learning models by integrating them into production systems. DataRobot delivers the training, tools, and support to enable software engineers to become AI engineers.
  • data-scientist-info-data-search-graph-1
    Data Scientists
    When the mundane tasks of model development are automated – like data partitioning, model tuning, feature selection, etc. – skilled data scientists accomplish radically more than they could with traditional hand-coded approaches. DataRobot also gives experts the flexibility to customize their models when needed.
  • delegated-group-user-people-man-team-1
    Executives
    When business leaders understand the importance of AI, and how to talk about and frame a machine learning project with their teams, they bring all of their domain knowledge and experience to bear in helping the company build AI applications.

How DataRobot Delivers Enterprise AI

DataRobot offers an advanced enterprise AI platform that democratizes data science and automates the end-to-end process for building, deploying, and maintaining artificial intelligence and machine learning at scale.

datarobot model building way
old model building way
What project will you use DataRobot for?