You want to invest in AI and machine learning, but rapid change, vendor churn, hype, and jargon make it hard to choose an AI vendor. In this webinar recording, you’ll learn what to consider as you seek a partner in AI, and get real-world AI implementation best practices from Pauline McKinney, Vice President of Data and Analytics at Wellen Capital.
Supervised machine learning generally includes two types of prediction problems—regression and classification. Regression problems predict continuous numerical values such as a price or weight. Classification problems, by contrast, classify values into discrete categories (yes or no, red or blue, buy or sell or hold, etc.).
DataRobot incorporates a variety of regression techniques, ranging from the simplest (linear regression) to complicated statistical classic regression models, to more complex techniques including gradient boosting and neural networks. The platform can also solve simple binary classification problems, as well as highly complex multiclass classification problems with up to 100 different categories. Imagine being able to predict which product a customer is likely to purchase next, or why a customer is likely to churn, with a high degree of accuracy. With DataRobot it’s easy to automate the creation of machine learning models like this – with unprecedented transparency so you can understand and trust the predictions they make.
DataRobot prepares data for modeling automatically, performing operations like one-hot encoding, missing value imputation, text mining, standardization, and data partitioning. The platform transforms data into features that are optimized for each algorithm so you always get the best results.
DataRobot uses the latest and most powerful open source machine learning libraries, including R, Python, scikit-learn, H2O, TensorFlow, Vowpal Wabbit, Spark ML, and XGBoost. It employs the same advanced techniques that data scientists use, including boosting, bagging, random forests, kernel-based methods, generalized linear models, deep learning, and many others.
DataRobot supports the advanced capabilities that data scientists require, in a way that makes them easy to use for all. From multiclass modeling to anomaly detection and monotonicity, DataRobot employs data science best practices and guardrails at every step so all users can collaborate on machine learning projects, and participate in an AI-driven culture.
Every model built in DataRobot can be put into production immediately. You can upload data to be scored in batches, utilize our API to generate predictions, and even download scoring code to be embedded directly into your applications. Monitor the performance of all deployed models from a central portal, and easily refresh and replace models if data and accuracy changes over time.
The biggest impact DataRobot has had on Lenovo is that decisions are now made in a more proactive and precise way.
Senior Business Development Manager, Latin America, Lenovo Brazil
Lenovo, one of the world's largest technology companies, invoices more than $45 billion of computers, laptops, and accessories globally each year. The Chinese multinational company considers Brazil to be one of its primary emerging markets.
DataRobot has already helped Lenovo more accurately predict sell out volume and, as a result, Lenovo has surged to become the leader in volume share on notebook sales for the B2C segment in Brazil. With numerous other machine learning initiatives in the pipeline, Rodrigo and his team are poised to leverage the power of automated machine learning to dominate the huge market opportunity facing Lenovo in Brazil.
"DataRobot not only empowered our data scientists by making them more productive, but is starting to democratize machine learning for our business analysts and data managers. The software itself is easy to use and understand. It is simple and gives clear results. It will be impactful to our organization and integral in advancing our overall AI strategy."
Digital Strategic Alliance Lead, Monsanto
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