What is Automated Machine Learning?

Automated machine learning is a technology invented by DataRobot to automate many of the tasks needed to develop artificial intelligence (AI) and machine learning applications. Incorporating the knowledge and expertise of some of the world’s top data scientists, DataRobot enables more users across an enterprise to succeed with machine learning by simply utilizing their understanding of their data and business and letting DataRobot do the rest.

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:
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.
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.

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

Preparing Data

Feature Engineering

Diverse Algorithms

Algorithm Selection

Training and Tuning

Ensembling

Head-to-Head Model Competitions

Human-Friendly Insights

Easy Deployment

Model Monitoring and Management

Step 1:

Preparing Data

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 2:

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.

Automated Feature Engineering

Step 3:

Diverse Algorithms

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. 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 4:

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 5:

Training and Tuning

It’s standard for 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 knows which features to include and which to leave out, and which feature selection method works best for different algorithms.

Step 6:

Ensembling

In data science jargon, teams of algorithms voting together on an estimated outcome are called “ensembles” or “blenders.” Each algorithm’s strengths balance out the weaknesses of another. 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.

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, compares the results, and ranks 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, machine learning 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

A recent Harvard Business Review article 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. DataRobot offers AI Services and its technical support teams are located around the world to assist with model building and deployment, 24 hours per day.

Model Deployment with DataRobot

Step 10:

Model Monitoring and Management

In a constantly changing world, your AI applications need to keep up to date with the latest trends. DataRobot makes it easy to compare predictions to actual results and to train a new model on the latest data. DataRobot also proactively identifies when a model’s performance is deteriorating over time.

What’s Model Risk and Why Does it Matter?

Step 1: Preparing Data

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 2: 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.

Automated Feature Engineering

Step 3: Diverse Algorithms

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. 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 4: 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 5: Training and Tuning

It’s standard for 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 knows which features to include and which to leave out, and which feature selection method works best for different algorithms.

Step 6: Ensembling

In data science jargon, teams of algorithms voting together on an estimated outcome are called “ensembles” or “blenders.” Each algorithm’s strengths balance out the weaknesses of another. 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.

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, compares the results, and ranks 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, machine learning 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

A recent Harvard Business Review article 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. DataRobot offers AI Services and its technical support teams are located around the world to assist with model building and deployment, 24 hours per day.

Model Deployment with DataRobot

Step 10: Model Monitoring and Management

In a constantly changing world, your AI applications need to keep up to date with the latest trends. DataRobot makes it easy to compare predictions to actual results and to train a new model on the latest data. DataRobot also proactively identifies when a model’s performance is deteriorating over time.

What’s Model Risk and Why Does it Matter?

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 business 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.
Analytics Professionals

Analytics Professionals

With practical, hands-on training and the support of DataRobot’s world-class team, business analytics professionals are quickly transformed into AI analysts that find and focus on what matters most to drive real business value.

Software Engineers

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 Scientists

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.

Executives

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.

DataRobot transforms model building

Keeping up with the ever-growing ecosystem of algorithms has never been this easy

With DataRobot
Old way

What project will you use DataRobot for?

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