MMLSpark 标签 - Gitee.com

v0.9.4

e6da4d5

2021-11-16 13:18

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v0.9.2

81f5f80

2021-11-03 11:10

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v0.9.1

6b81426

2021-10-16 04:11

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v0.9.0

a6c7fea

2021-10-15 12:58

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mmlspark-v1.0.0-rc4

5fc65ab

2021-10-12 10:19

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v1.0.0-rc4

5fc65ab

2021-10-12 10:19

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mmlspark-v1.0.0-rc3

67891a6

2020-10-03 03:25

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v1.0.0-rc3

67891a6

2020-10-03 03:25

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mmlspark-v1.0.0-rc2

81e73a2

2020-08-20 01:29

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v1.0.0-rc2

81e73a2

2020-08-20 01:29

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mmlspark-v1.0.0-rc1

8d31c02

2019-10-16 21:02

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v1.0.0-rc1

8d31c02

2019-10-16 21:02

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mmlspark-v0.18.1

62946d1

2019-08-20 12:39

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v0.18.1

62946d1

2019-08-20 12:39

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mmlspark-v0.18.0

3bb48b8

2019-08-19 11:15

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v0.18.0

3bb48b8

2019-08-19 11:15

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mmlspark-v0.17

- LightGBM evaluation 3-4x faster!
- Spark Serving v2
- LightGBM training supports early stopping and regularization
- LIME on Spark significantly faster

- Both Microbatch and Continuous mode have sub-millisecond latency
- Supports fault tolerance
- Can reply from anywhere in the pipeline
- Fail fast modes for warning callers of bad JSON parsing
- Fully based on DataSource API v2

- 3-4x evaluation performance improvement
- Add early stopping capabilities
- Added L1 and L2 Regularization parameters
- Made network init more robust
- Fixed bug caused by empty partitions

- LIME Parallelization significantly faster for large datasets
- Tabular Lime now supported

- Added UnicodeNormalizer for working with complex text
- Recognize Text exposes parameters for its polling handlers

We would like to acknowledge the developers and contributors, both internal and external who helped create this version of MMLSpark.

- Ilya Matiach, Markus Cozowicz, Scott Graham, Daniel Ciborowski, Jeremy Reynolds, Miguel Fierro, Robert Alexander, Tao Wu, Sudarshan Raghunathan, Anand Raman,Casey Hong,  Karthik Rajendran, Dalitso Banda, Manon Knoertzer, Lars Ahlfors, The Microsoft AI Development Acceleration Program, Cognitive Search Team, Azure Search Team

bba5c10

2019-04-23 15:06

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v0.17

- LightGBM evaluation 3-4x faster!
- Spark Serving v2
- LightGBM training supports early stopping and regularization
- LIME on Spark significantly faster

- Both Microbatch and Continuous mode have sub-millisecond latency
- Supports fault tolerance
- Can reply from anywhere in the pipeline
- Fail fast modes for warning callers of bad JSON parsing
- Fully based on DataSource API v2

- 3-4x evaluation performance improvement
- Add early stopping capabilities
- Added L1 and L2 Regularization parameters
- Made network init more robust
- Fixed bug caused by empty partitions

- LIME Parallelization significantly faster for large datasets
- Tabular Lime now supported

- Added UnicodeNormalizer for working with complex text
- Recognize Text exposes parameters for its polling handlers

We would like to acknowledge the developers and contributors, both internal and external who helped create this version of MMLSpark.

- Ilya Matiach, Markus Cozowicz, Scott Graham, Daniel Ciborowski, Jeremy Reynolds, Miguel Fierro, Robert Alexander, Tao Wu, Sudarshan Raghunathan, Anand Raman,Casey Hong,  Karthik Rajendran, Dalitso Banda, Manon Knoertzer, Lars Ahlfors, The Microsoft AI Development Acceleration Program, Cognitive Search Team, Azure Search Team

bba5c10

2019-04-23 15:06

下载

mmlspark-v0.16

- Added the `AzureSearchWriter` for integrating Spark with [Azure Search](https://azure.microsoft.com/en-us/services/search/)
- Added the [Smart Adaptive Recommender (SAR)](https://github.com/Azure/mmlspark/blob/master/docs/SAR.md) for better recommendations in SparkML
- Added [Named Entity Recognition Cognitive Service](https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/) on Spark
- Several new [LightGBM features](#LightGBM-on-Spark) (Multiclass Classification, Windows Support, Class Balancing, Custom Boosting, etc.)
- Added Ranking Train Validation Splitter for easy ranking experiments
- All Computer Vision Services can now send binary data or URLs to Cognitive Services

- Learn how to use the Azure Search writer to create a visual search system for The Metropolitan Museum of Art with: [AzureSearchIndex - Met Artworks.ipynb](https://github.com/Azure/mmlspark/blob/master/notebooks/samples/AzureSearchIndex%20-%20Met%20Artworks.ipynb)

- MMLSpark Image Schema now unified with Spark Core
    - Now supports Query pushdown and [Deep Learning Pipelines](https://github.com/databricks/spark-deep-learning)
- Bugfixes for Text Analytics services
- `PageSplitter` now propagates nulls
- HTTP on Spark now supports socket and read timeouts
- `HyperparamBuilder` python wrappers now return idiomatic python objects

- Added multiclass classification
- Added multiple types of boosting (Gradient Boosting Decision Tree, Random Forest, Dropout meet Multiple Additive Regression Trees, Gradient-based One-Side Sampling)
- Added windows OS support/bugfix
- LightGBM version bumped to `2.2.200`
- Added native support for categorical columns, either through Spark's StringIndexer, MMLSpark's ValueIndexer or list of indexes/slot names parameter
- `isUnbalance` parameter for unbalanced datasets
- Added boost from average parameter

We would like to acknowledge the developers and contributors, both internal and external who helped create this version of MMLSpark.

- Ilya Matiach, Casey Hong, Daniel Ciborowski, Karthik Rajendran, Dalitso Banda, Manon Knoertzer, Sudarshan Raghunathan, Anand Raman,Markus Cozowicz, The Microsoft AI Development Acceleration Program, Cognitive Search Team, Azure Search Team

1d29394

2019-03-06 11:43

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v0.16

- Added the `AzureSearchWriter` for integrating Spark with [Azure Search](https://azure.microsoft.com/en-us/services/search/)
- Added the [Smart Adaptive Recommender (SAR)](https://github.com/Azure/mmlspark/blob/master/docs/SAR.md) for better recommendations in SparkML
- Added [Named Entity Recognition Cognitive Service](https://azure.microsoft.com/en-us/services/cognitive-services/text-analytics/) on Spark
- Several new [LightGBM features](#LightGBM-on-Spark) (Multiclass Classification, Windows Support, Class Balancing, Custom Boosting, etc.)
- Added Ranking Train Validation Splitter for easy ranking experiments
- All Computer Vision Services can now send binary data or URLs to Cognitive Services

- Learn how to use the Azure Search writer to create a visual search system for The Metropolitan Museum of Art with: [AzureSearchIndex - Met Artworks.ipynb](https://github.com/Azure/mmlspark/blob/master/notebooks/samples/AzureSearchIndex%20-%20Met%20Artworks.ipynb)

- MMLSpark Image Schema now unified with Spark Core
    - Now supports Query pushdown and [Deep Learning Pipelines](https://github.com/databricks/spark-deep-learning)
- Bugfixes for Text Analytics services
- `PageSplitter` now propagates nulls
- HTTP on Spark now supports socket and read timeouts
- `HyperparamBuilder` python wrappers now return idiomatic python objects

- Added multiclass classification
- Added multiple types of boosting (Gradient Boosting Decision Tree, Random Forest, Dropout meet Multiple Additive Regression Trees, Gradient-based One-Side Sampling)
- Added windows OS support/bugfix
- LightGBM version bumped to `2.2.200`
- Added native support for categorical columns, either through Spark's StringIndexer, MMLSpark's ValueIndexer or list of indexes/slot names parameter
- `isUnbalance` parameter for unbalanced datasets
- Added boost from average parameter

We would like to acknowledge the developers and contributors, both internal and external who helped create this version of MMLSpark.

- Ilya Matiach, Casey Hong, Daniel Ciborowski, Karthik Rajendran, Dalitso Banda, Manon Knoertzer, Sudarshan Raghunathan, Anand Raman,Markus Cozowicz, The Microsoft AI Development Acceleration Program, Cognitive Search Team, Azure Search Team

1d29394

2019-03-06 11:43

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