What's New

JulieH
DataRobot Employee
DataRobot Employee

Hi all,
DataRobot is retiring the App2 SaaS offering that enables anyone to sign up and use the DataRobot platform for free. The DataRobot team is working on a new, more comprehensive experience for customers to explore the capabilities of the DataRobot platform, which will be shared soon. Enterprise buyers can experience a DataRobot Tour and/or reach out for a proof of value conversation with their account team.
 
Important date
  • As soon as June 30, 2022, DataRobot will disable App2. Once App2 is disabled, you won’t be able to log into an existing account or create a new account.
Next steps
  • Download any important assets (datasets, model artifacts, scoring code, etc.) from your App2 account before June 30.
  • If you are a paying App2 user, your DataRobot success team will reach out to you and work with you to develop a tailored transition plan.
  • Feel free to reply to this post (click Reply below), or reach out to your DataRobot success team at any time regarding this change.
  • You can also open a ticket by emailing  with the subject line: App2 Deprecation.

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jenD
DataRobot Employee
DataRobot Employee

Coming December 1, 2022, join the webinar!

 

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 here.

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jenD
DataRobot Employee
DataRobot Employee

Accelerate the delivery of AI to production through a hosted implementation of the DataRobot AI Cloud platform

 

Dedicated Managed AI Cloud is a full instance of the latest DataRobot platform release, hosted by DataRobot. By eliminating implementation time and resources, organizations can more quickly apply machine learning, decision intelligence, and MLOps capabilities.

 

Read all about it here.

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jenD
DataRobot Employee
DataRobot Employee

September 20, 2022, 11:00 am EDT: Join data scientist Vandan Parmar and data science trainer Andreea Turcu as they provide an introduction to identifying and qualifying AI use cases. 

 

This session is appropriate for data scientists, business analysts, and business leaders who are interested in data science.

 

Interested? Register here!

 

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DRLive!
DataRobot Alumni

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.

 

You can register here!

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DRLive!
DataRobot Alumni

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.

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DRLive!
DataRobot Alumni

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.

 

You can register here!

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DRLive!
DataRobot Alumni

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.

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Tleemcnabb
DataRobot Alumni

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.

 

image (19).png

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vVineet
DataRobot Alumni

DataRobot is deprecating the use of Python 2 from the platform codebase; this will require nearly all current users to migrate their active projects and model deployments to use the new Python 3 runtime in the platform. Your action is required to migrate projects as well as deployments to ensure business continuity, before these are disabled per the schedule in Managed Cloud or the on-premise version in use.

A detailed migration guide including the timeline is available.

How do I know if this impacts me or my organization?

This migration affects all users of DataRobot, due to the underlying changes in the Python version used in the platform. To ensure you can oversee and control the changes to your own projects, models and deployments, DataRobot does not automatically execute the migration steps; as a result, user intervention is required.

The following set of users should take action to plan and execute the migration steps:

  • Managed AI Cloud: All users and organizations using one of our cloud (SaaS) instances and having projects created before March 7, 2022 that leverage Python 2 runtime (new projects started using Python 3 starting on March 7). The Manage Projects page will identify which projects will be deprecated. To ensure your deprecated projects are not disabled in July, follow the preset schedule of migration phases.
  • AI Cloud Platform Trial: Users who are doing short-term evaluations of the platform, and expect to be using the deprecated projects or deployments associated with these projects after July 25, 2022. The migration steps are to future-proof your work. Otherwise, if you do not intend to continue using these projects and model deployments long-term (after July 25), then you can ignore the migration steps.
  • On-Premise: All organizations using DataRobot software deployed in their privately-managed cloud or data center, except where a completely new installation (i.e., not an upgrade from a previous DataRobot version) was done on Release 7.1 or higher. An exception is Hadoop-based installations, where the Python migration is necessary regardless of the DataRobot version in use. For such eligible versions and environments, upgrading to DataRobot Release 8.0 is mandatory to execute the migration steps, prior to upgrading to any future Release 9.0 or higher. The timeline for migration is dependent on the current versions in use and upgrade plans for Release 8.0.
  • Python client: The DataRobot Python API client continues to support Python 2 and at this time we are not deprecating its use, so API client code will not need to be migrated yet. We will provide additional guidance in coming months as we look to deprecate the py2 client code. The project and deployment migration is relevant to all the users mentioned above, irrespective of whether they are using the Python client or not.

How can I get support or have my questions answered?

Existing customers can contact the DataRobot Support team or their representative for further assistance after reviewing the migration guide. DataRobot Community support is also available to all DataRobot users, including Trial and Enterprise. If you have any questions about the migration, ask them here. We hope you’ll encourage further discussions and planning in your organization to execute next steps for migration very soon.

Thanks,
DataRobot Platform team

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Linda
DataRobot Alumni

Maintenance for the DataPrep service will be performed on Friday, April 1st, from 4:30AM UTC to 8:00AM UTC. This maintenance will affect both Managed AI Cloud (US and EU) and AI Platform Trial environments.

During this time all users for those environments will experience a brief outage of the DataPrep service.

To receive email or SMS updates on maintenance or incidents, please subscribe to status.datarobot.com.

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nenshad
DataRobot Alumni

December 15, 2021

To our valued DataRobot customers, 

I’m reaching out with an update on DataRobot’s response to the Log4j vulnerability.

On December 10, 2021, DataRobot became aware of a vulnerability in the widely used logging library Log4j (CVE-2021-44228) for Java-based applications, which is impacting enterprise applications and cloud services around the world. Since then, (CVE-2021-45046) has been issued and the situation continues to evolve.

In response to the initial and subsequent vulnerabilities, DataRobot immediately assembled a cross-functional team to assess the scope of the vulnerabilities and begin implementing steps for remediation.

Security is a foundational element of an Enterprise AI Platform. The new 7.3 release has shipped with a remediation as will all future releases. Please review the following for more detailed guidance: 

  • If you currently do not use DataRobot Scoring Code, MLOps Monitoring Agents, or Portable Prediction Servers (PPS) with MLOps Monitoring enabled, no further action is required.
  • If you are using any of the above features, the Log4j vulnerability may continue to exist in any previously generated artifact. As general guidance, please follow the Apache Security Advisory for Log4j for mitigation. As the situation evolves, new updates with new mitigations will be posted by Apache at this link. If you need further details, please review the appendix for specific mitigation steps on DataRobot artifacts to help address these risks. 
  • The DataRobot DataPrep CDH 6 connector is being patched as a priority. Customers using this feature should do so only in secured environments until a patch is applied. 
  • Other DataRobot products, including Zepl and Algorithmia, are not affected.

We would also urge you to make a plan to upgrade to DataRobot 7.3 in your current environment as soon as possible. We are happy to work with you on this upgrade and to enable your users on all of the latest capabilities that your upgrade would give them access to. 

Please do not hesitate to reach out to your account team or email support@datarobot.com if we can assist you in any way. As always, thank you for including DataRobot as a cornerstone in your AI transformation. We will provide updates on Log4j on DataRobot Community if we have new information relevant to you. For now, we wish you the happiest of holiday seasons. 

Best Regards,

Nenshad Bardoliwalla

Chief Product Officer

 


Appendix: DataRobot Customer-Managed Release

This vulnerability is dependent on which features are enabled and how they are being utilized. 

1. DataRobot Scoring Code (formerly known as "CodeGen")

DataRobot provides a capability to export ‘code’ and executable Jar files for the purpose of running predictions on other platforms.

Mitigating Actions:

Scoring Code Jars generated from trained models could be vulnerable. If your runtime environment is not already secured, please follow the current guidance provided in the following Apache Security Advisory.

2. MLOps Monitoring Agent

DataRobot provides a capability to monitor and manage ML models running outside of DataRobot’s platform via the MLOps Monitoring Agent.

Mitigating Actions:

If you are running the MLOps Monitoring Agent, and your runtime environment is not already secured, please follow the guidance from Apache Security Advisory.

3. Portable Prediction Server (PPS) with MLOps Monitoring enabled

DataRobot provides a capability to export and execute ML models in an external Docker container outside of DataRobot’s platform and monitor the execution via DataRobot’s MLOps Monitoring Agent (see 2, above). Only the Java MLOps Monitoring Agent contains the vulnerable library.

Mitigating Actions:

If your runtime environment is not already secured, please follow the current guidance provided in the following  Apache Security Advisory.

4. JDBC Driver Support

DataRobot allows customers to connect to external JDBC data sources. We recommend upgrading any JDBC driver to a release which meets the requirements of the Apache Security Advisory.

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