fix(predict-diabetes): update dependency mlflow to v2.12.1 #15957
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This PR contains the following updates:
2.12.0
->2.12.1
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Release Notes
mlflow/mlflow (mlflow)
v2.12.1
MLflow 2.12.1 includes several major features and improvements
With this release, we're pleased to introduce several major new features that are focused on enhanced GenAI support, Deep Learning workflows involving images, expanded table logging functionality, and general usability enhancements within the UI and external integrations.
Major Features and Improvements:
PromptFlow: Introducing the new PromptFlow flavor, designed to enrich the GenAI landscape within MLflow. This feature simplifies the creation and management of dynamic prompts, enhancing user interaction with AI models and streamlining prompt engineering processes. (#11311, #11385 @brynn-code)
Enhanced Metadata Sharing for Unity Catalog: MLflow now supports the ability to share metadata (and not model weights) within Databricks Unity Catalog. When logging a model, this functionality enables the automatic duplication of metadata into a dedicated subdirectory, distinct from the model’s actual storage location, allowing for different sharing permissions and access control limits. (#11357, #11720 @WeichenXu123)
Code Paths Unification and Standardization: We have unified and standardized the
code_paths
parameter across all MLflow flavors to ensure a cohesive and streamlined user experience. This change promotes consistency and reduces complexity in the model deployment lifecycle. (#11688, @BenWilson2)ChatOpenAI and AzureChatOpenAI Support: Support for the ChatOpenAI and AzureChatOpenAI interfaces has been integrated into the LangChain flavor, facilitating seamless deployment of conversational AI models. This development opens new doors for building sophisticated and responsive chat applications leveraging cutting-edge language models. (#11644, @B-Step62)
Custom Models in Sentence-Transformers: The sentence-transformers flavor now supports custom models, allowing for a greater flexibility in deploying tailored NLP solutions. (#11635, @B-Step62)
Image Support for Log Table: With the addition of image support in
log_table
, MLflow enhances its capabilities in handling rich media. This functionality allows for direct logging and visualization of images within the platform, improving the interpretability and analysis of visual data. (#11535, @jessechancy)Streaming Support for LangChain: The newly introduced
predict_stream
API for LangChain models supports streaming outputs, enabling real-time output for chain invocation via pyfunc. This feature is pivotal for applications requiring continuous data processing and instant feedback. (#11490, #11580 @WeichenXu123)Security Fixes:
Features:
predict_stream
API for streamable output for Langchain models and theDatabricksDeploymentClient
(#11490, #11580 @WeichenXu123)code_paths
alias forcode_path
inpyfunc
to be standardized to other flavor implementations (#11688, @BenWilson2)sentence-transformers
flavor (#11635, @B-Step62)MapType
support within model signatures when used with Spark udf inference (#11265, @WeichenXu123)ChatOpenAI
andAzureChatOpenAI
LLM interfaces within the LangChain flavor (#11644, @B-Step62)Image
object for handling the logging and optimized compression of images (#11404, @jessechancy)UCVolumeDatasetSource
(#11301, @chenmoneygithub)mlflow.Image
files within tables (#11535, @jessechancy)chat
&chat streaming
for Anthropic within the MLflow deployments server (#11195, @gabrielfu)Security fixes:
Bug fixes:
%
in model names to prevent URL mangling within the UI (#11474, @daniellok-db)LangChain
loading functions to handle uncorrectable pickle-related exceptions that are thrown when loading a model in certain versions (#11582, @B-Step62)sklearn
flavor to reintroduce support for custom prediction methods (#11577, @B-Step62)langchain
flavor (#11485, @WeichenXu123)transformers
models that contain custom code (#11412, @daniellok-db)transformers
flavor that generates an inconsistent input example display within the MLflow UI (#11508, @B-Step62)keras
autologging training dataset generator (#11383, @WeichenXu123)GetSampledHistoryBulkInterval
API to produce more consistent results when displayed within the UI (#11475, @daniellok-db)langchain
andlanchain_community
withinlangchain
models when logging (#11450, @sunishsheth2009)Documentation updates:
code_paths
docstrings in API documentation (#11675, @BenWilson2)sentence-transformers
OpenAI
-compatible API interfaces (#11373, @es94129)Small bug fixes and documentation updates:
#11723, @freemin7; #11722, #11721, #11690, #11717, #11685, #11689, #11607, #11581, #11516, #11511, #11358, @serena-ruan; #11718, #11673, #11676, #11680, #11671, #11662, #11659, #11654, #11633, #11628, #11620, #11610, #11605, #11604, #11600, #11603, #11598, #11572, #11576, #11555, #11563, #11539, #11532, #11528, #11525, #11514, #11513, #11509, #11457, #11501, #11500, #11459, #11446, #11443, #11442, #11433, #11430, #11420, #11419, #11416, #11418, #11417, #11415, #11408, #11325, #11327, #11313, @harupy; #11707, #11527, #11663, #11529, #11517, #11510, #11489, #11455, #11427, #11389, #11378, #11326, @B-Step62; #11715, #11714, #11665, #11626, #11619, #11437, #11429, @BenWilson2; #11699, #11692, @annzhang-db; #11693, #11533, #11396, #11392, #11386, #11380, #11381, #11343, @WeichenXu123; #11696, #11687, #11683, @chilir; #11387, #11625, #11574, #11441, #11432, #11428, #11355, #11354, #11351, #11349, #11339, #11338, #11307, @daniellok-db; #11653, #11369, #11270, @chenmoneygithub; #11666, #11588, @jessechancy; #11661, @jmjeon94; #11640, @tunjan; #11639, @minkj1992; #11589, @tlm365; #11566, #11410, @brynn-code; #11570, @lababidi; #11542, #11375, #11345, @edwardfeng-db; #11463, @taranarmo; #11506, @ernestwong-db; #11502, @fzyzcjy; #11470, @clemenskol; #11452, @jkfran; #11413, @GuyAglionby; #11438, @victorsun123; #11350, @liangz1; #11370, @sunishsheth2009; #11379, #11304, @zhouyou9505; #11321, #11323, #11322, @michael-berk; #11333, @cdancette; #11228, @TomeHirata
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