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Snowpark for Python gives data scientists a nice way to do DataFrame-style programming against the Snowflake data warehouse, including the ability to set up full-blown machine learning pipelines to run on a recurrent schedule.
Databricks Lakehouse Platform combines cost-effective data storage with machine learning and data analytics, and it's available on AWS, Azure, and GCP. Could it be an affordable alternative for your data warehouse needs?
Nvidia’s VMware-optimized AI software stack offers a strong alternative to doing machine learning in the AWS, Azure, and Google clouds. Nvidia LaunchPad lets you try it out for free.
Vertex AI greatly improves the integration of Google Cloud’s AI/ML platform and AutoML services, combining a new unified API with very good modeling capabilities.
Dataiku’s end-to-end machine learning platform combines visual tools, notebooks, and code to address the needs of data scientists, data engineers, business analysts, and AI consumers.
Ahana Cloud for Presto turns a data lake on Amazon S3 into what is effectively a data warehouse, without moving any data. SQL queries run quickly even when joining multiple heterogeneous data sources.
BlazingSQL builds on RAPIDS to distribute SQL query execution across GPU clusters, delivering the ETL for an all-GPU data science workflow.
Amazon Web Services provides an impressively broad and deep set of machine learning and AI services, rivaling Google Cloud and Microsoft Azure.
Microsoft Azure combines a wide range of cognitive services and a solid platform for machine learning that supports automated ML, no-code/low-code ML, and Python-based notebooks.
DataRobot’s end-to-end AutoML suite not only speeds up the creation of accurate models, but can combine time series, images, geographic information, tabular data, and text in a single model.