This webinar explores how automated Machine Learning (AutoML) can combine with Natural Language Processing (NLP) to open up new possibilities for analyzing, categorizing and deriving value from text documents—no complex skills or theoretical knowledge required.
Register and attend the next "Ask the Expert" session: Improving Time Series Models.
In this session, Travis and Calvin will provide a deep dive into how DataRobot can supercharge your forecasting needs. They will show how to achieve better model outcomes--and your business objectives--in the following topics:
How to use clustering/segmenting series for better model performance
How to leverage hierarchical modeling to extract more signal from your data
Strategies to explore feature derivation windows and backtesting length
This session is relevant for any user who has a basic familiarity with data science and the DataRobot platform and is interested in improving their time series models! It does not require advanced knowledge of the platform or of time series modeling.
We're announcing a new user series called Community Live Forums. This is your chance to get time with a DataRobot Data Scientist. There will be two types of live, virtual events, DataRobot Live! and Ask the Expert:
DataRobot Live! -- Are you just getting started with DataRobot and want to learn more? These sessions will be a short high level demo with the majority of the session dedicated to answering user questions in a hands-on environment. Learn how DataRobot can automate the entire end to end process of preparing, building, and deploying highly accurate models to power your modern AI systems. This is for users who are currently in a trial and customers.
Ask the Expert -- Are you a regular user and want to ask questions about specific functionalities? These sessions will have topics for a targeted audience, deep diving around your pre-posted questions from the community. To get the most from this session, it is best to have access to the product.
These sessions will be hosted most Tuesdays at 11:00 AM ET. Users can attend as many sessions as they want. You can register here.
Register and attend the next "Ask the Expert" session: Build a Churn Use Case.
In this session, @jake and @Olga Shpyrko will provide a deep dive into how to predict which customers will churn and how to use machine learning to choose the optimal retention strategies. They will show how critical it is to frame the churn problem appropriately, give an example with DataRobot, then discuss how to manage the use case in production and how to measure its success!
This session is relevant for any user who has a basic familiarity with data science and the DataRobot platform and is interested in building a churn model! It does not require advanced knowledge of the platform or of churn modeling.
Thanks to everyone who joined us for our first DataRobot Live! We reviewed the model capabilities of our platform as well as a demo on building production ready models. A transcript of the Q&A is outlined below.
Q1:You mentioned Pathfinder [has] regular use cases, where can I find this?
A1: Pathfinder can be found at pathfinder.datarobot.com. This includes many use case ideas from common industries, some of which are fleshed out to include business considerations and implementations, and even notebook solutions!
Q2: I am new to DataRobot and I am working with a Time Series dataset that only uses weekdays, is there a way to include weekends as well?
A2: DataRobot has a Time Series DataPrep tool that allows you to aggregate to the weekly level easily if you think weekly predictions are granular enough. If you want to stick to daily, you can include those weekends with zero value outcomes, and DataRobot will automatically generate indicators for each day of the week. It will quickly learn that there are zero-value outcomes on weekends.
Q3: Is there a common way to analyze just weekdays? I know that there are ways to build SQL code to use the Time Series stuff with gaps.
A3: Following up on a similar question here, you can access time series data prep to further clean your time series data (or use the recommendation provided above). Learn more here.
Q4: I have seen many different examples/ iterations of Time Series. My current example is to predict One Day Ahead. Any examples that can be shared on just that alone?
A4: As shared live, when you build time series models with DataRobot's AutoTS, you can choose the "forecast window". This could be just the next day, or the next week, or between 4-7 days away from the prediction time. There is a lot of flexibility here.
Q5: What are the steps to collect the information based on the past - I think I saw a video of including future dates, but input them as blank, and the program automatically estimates the missing dates, one day ahead.
A5: Time Series (and feature discovery) do automatic feature engineering in which they derive rolling metrics over the past day, week, month, or any custom time frame. To make future predictions, you do need to provide a dataset with blank outcomes in future dates. If you have multiple series, then you need blanks on future dates for each series.
Q6:Does the model retrain itself automatically when it is fed with new data entries?
A6: It won't retrain itself by default, but you can set up automatic retraining jobs in MLOps. You can retrain based on triggers like a decline in model performance, or you can retrain on a schedule. Learn more here.
Q7: What dataset size can be used with DataRobot? (million data points? and max number of features?)
A7: Data size limits are mostly based on file size. Depending on your license and/or install (if on premises), you can model on 5 GB or 10 GB of data. There is also a feature limit of 20,000 features, but the size limit overrides this one. When dealing with big data, we recommend downsampling the data.
If you have any feedback on answers or other questions, please feel free to comment below. You can join us for an upcoming session by registering here.
We're announcing a new user series called Community Live Forums. This is your chance to get time with a DataRobot Data Scientist. There will be two types of live, virtual events:
DataRobot Live! -- These sessions will be a short demo with majority of the session dedicated to answering user questions in a hands-on environment.
Ask the Expert -- These sessions will have targeted topics for a specific audience
These sessions will be hosted most Tuesdays at 11:00 AM ET. Users can attend as many sessions as they want. Our first DataRobot Live! will take place on July 12, 2022 at 11:00AM ET. You can register here.
We will be posting reminders about upcoming forums as well as the upcoming schedule. In the meantime, if you have ideas for sessions topics, we're all ears! Please comment below.