DataRobot

JUNE 16, 2021

Our DataRobot MLOps product has focused on resolving many of the issues with bringing models into production. Additionally, we have recently announced a partnership and integration with Snowflake to expand deployment options by bringing models directly into the database. New in DataRobot MLOps Release 7.1:

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DataRobot

JUNE 9, 2021

With Snowflake’s announcement of Snowpark and Java UDFs (user-defined functions), DataRobot has continued to expand on this theme. With Snowpark, data preparation tasks in Zepl can be pushed down into Snowflake for in-database feature engineering. Snowflake has always delivered performance and ease-of-use for users familiar with SQL.

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DataRobot

MARCH 17, 2022

release, I’m very pleased to announce that our unique MLOps capability — Continuous AI —is now available to all on-prem customers. Now, all of our MLOps customers have access to the best of both automated machine learning and machine learning operations, with a human in the loop to continually improve models over the full AI/ML lifecycle.

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Cloud That

NOVEMBER 16, 2022

Import – We can connect to and import data from multiple sources like Amazon Simple Storage Service (Amazon S3), Amazon Athena (Athena), Amazon Redshift, Snowflake, and Databricks. Yes, we can very well integrate it with pipelines to maintain the whole flow for an MLOps project. . Amazon SageMaker Data Wrangler Core Functionalities.

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DataRobot

MAY 5, 2022

DataRobot’s already market-leading AutoML, AutoTS, and MLOps products will only be able to drive more value after fully unlocking the power of data-agnostic AI. DataRobot + Labelbox + Snowflake Model-Assisted Labeling Solution. Jared Bowns. VP of Engineering at DataRobot. Today, managing unstructured data is an arduous task.

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DataRobot

MARCH 10, 2022

DataRobot and Snowflake Jointly Unleash Human and Machine Intelligence Across the Industrial Enterprise Landscape. With Snowflake and DataRobot, organizations can capture this data and rapidly develop artificially intelligent applications that immediately impact the bottom line. Leveraging Snowflake and DataRobot for Speed and Scale.

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DataRobot

JUNE 9, 2022

Snowflake Summit 2022 (June 13-16) draws ever closer, and I believe it’s going to be a great event. Users can now run scoring code directly inside Snowflake. This value-add feature is now available on models run within Snowflake. Another real treat for attendees will be the conversation with elite rock climber Alex Honnold.

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DataRobot

SEPTEMBER 6, 2022

Transparency Is Key In MLOps. As mentioned by Gartner analyst Sumit Agarwal in his session, Developing Your MLOps Playbook to Accelerate Machine Learning Deployment , “one person cannot do everything.”. Gartner, Technical Insights: Develop Your MLOps Playbook to Accelerate Machine Learning Deployment, Sumit Agarwal.

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DataRobot

MARCH 16, 2023

Value at Production Scale – DataRobot’s ML Production is more than just basic MLOps tooling and now new features are making it even easier and faster to scale and maintain model performance. New Snowflake integrations and the SAP joint solution have tightened the data to experimentation to deployment loop.

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Cloudera

OCTOBER 26, 2022

This AMP can also be repurposed to help you find the most unique snowflake. Companies also rely on AMPs like continuous model monitoring to improve their MLOps capabilities. The project launches an interactive visualization for exploring the quality of representations extracted using multiple model architectures.

Machine Learning 76

Machine Learning Artificial Inteligence Software Review Technical Review 76

O'Reilly Media - Ideas

OCTOBER 19, 2021

The new category is often called MLOps. Just introducing a new term like MLOps doesn’t solve anything by itself, rather, it just adds to the confusion. This approach has worked well for software development, so it is reasonable to assume that it could address struggles related to deploying machine learning in production too.

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DataRobot

APRIL 12, 2021

When possible Feature Discovery operations are pushed into the database to minimize data movement and get faster results (read more on snowflake integration). DataRobot Feature Discovery integrates seamlessly with DataRobot deployment/MLOps capabilities. These MLOps capabilities include model monitoring, management, and governance.

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DataRobot

SEPTEMBER 14, 2021

And finally, AI Cloud significantly expands the concept of MLOps , bringing AI together with proven practices for software engineering and operations to protect and secure your data and your business. And will serve as a foundation for the vast ecosystem of partners that extend, integrate and build upon DataRobot.

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DataRobot

DECEMBER 15, 2022

Import the data from various options: from a local file or URL or create a data connection with diverse data sources, such as Snowflake or Amazon Redshift, and upload it to the AI Catalog, which helps manage datasets, versioning, and shared capabilities with other users. The machine learning life cycle always starts with the dataset.

Machine Learning 81

Machine Learning Artificial Inteligence Training Software Review 81

Altexsoft

MARCH 30, 2023

Besides that, its native integration with MLflow , an open-source tool for building machine learning pipelines in production, backs MLOps initiatives. Yet, let’s say it right away, Databricks delivers the best in class ML and MLOps capabilities and is unbeatable in this sense. Now, let’s see which most close alternatives it has.

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DataRobot

SEPTEMBER 14, 2021

To support the need to connect AI-driven decisions directly with existing business applications and services, like Snowflake, Salesforce, and ServiceNow. To offer the flexibility to deploy AI solutions anywhere. Most critically, to unify the ability to do it all in a single environment.

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