관리형 MLflow(Managed MLflow)

MLFLOW TRACKING: Automatically log parameters, code versions, metrics, and artifacts for each run using Python, REST, R API, and Java API

GENERATIVE AI DEVELOPMENT: Simplify model development to build GenAI applications for a variety of use cases such as chatbots, document summarization, sentiment analysis and classification with MLflow’s Deployments Server and Evaluation UI, supported by native integration with LangChain, and seamless UI for fast prototyping and iteration.

MLFLOW TRACKING SERVER: Get started quickly with a built-in tracking server to log all runs and experiments in one place. No configuration needed on Databricks.

EXPERIMENT MANAGEMENT: Create, secure, organize, search and visualize experiments from within the workspace with access control and search queries.

MLFLOW RUN SIDEBAR: Automatically track runs from within notebooks and capture a snapshot of your notebook for each run so that you can always go back to previous versions of your code.

LOGGING DATA WITH RUNS: Log parameters, datasets, metrics, artifacts and more as runs to local files, to a SQLAlchemy compatible database, or remotely to a tracking server.

DELTA LAKE INTEGRATION: Track large-scale datasets that fed your models with Delta Lake snapshots.

ARTIFACT STORE: Store large files such as S3 buckets, shared NFS file system, and models in Amazon S3, Azure Blob Storage, Google Cloud Storage, SFTP server, NFS, and local file paths.

MLFLOW MODELS: A standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark.

MODEL CUSTOMIZATION: Use Custom Python Models and Custom Flavors for models from an ML library that is not explicitly supported by MLflow’s built-in flavors.

BUILT-IN MODEL FLAVORS: MLflow provides several standard flavors that might be useful in your applications, like Python and R functions, Hugging Face, OpenAI and LangChain, PyTorch, Spark MLlib, TensorFlow and ONNX.

BUILT-IN DEPLOYMENT TOOLS: Quickly deploy on Databricks via Apache Spark UDF for a local machine, or several other production environments such as Microsoft Azure ML, Amazon SageMaker, and building Docker Images for Deployment.

CENTRAL REPOSITORY: Register MLflow models with the MLflow Model Registry. A registered model has a unique name, version, stage and other metadata.

MODEL VERSIONING: Automatically keep track of versions for registered models when updated.

MODEL STAGE: Assign preset or custom stages to each model version, like “Staging” and “Production” to represent the lifecycle of a model.

CI/CD WORKFLOW INTEGRATION: Record stage transitions, request, review and approve changes as part of CI/CD pipelines for better control and governance.

MODEL STAGE TRANSITIONS: Record new registration events or changes as activities that automatically log users, changes, and additional metadata such as comments.

GOVERN ACCESS TO LLMS: Manage SaaS LLM credentials.

CONTROL COSTS: Set up rate limits.

STANDARDIZE LLM INTERACTIONS: Experiment with different OSS/SaaS LLMs with standard input/output interfaces for different tasks: completions, chat, embeddings.

MLFLOW TRACKING: Automatically log parameters, code versions, metrics, and artifacts for each run using Python, REST, R API, and Java API

GENERATIVE AI DEVELOPMENT: Simplify model development to build GenAI applications for a variety of use cases such as chatbots, document summarization, sentiment analysis and classification with MLflow’s Deployments Server and Evaluation UI, supported by native integration with LangChain, and seamless UI for fast prototyping and iteration.

MLFLOW TRACKING SERVER: Get started quickly with a built-in tracking server to log all runs and experiments in one place. No configuration needed on Databricks.

EXPERIMENT MANAGEMENT: Create, secure, organize, search and visualize experiments from within the workspace with access control and search queries.

MLFLOW RUN SIDEBAR: Automatically track runs from within notebooks and capture a snapshot of your notebook for each run so that you can always go back to previous versions of your code.

LOGGING DATA WITH RUNS: Log parameters, datasets, metrics, artifacts and more as runs to local files, to a SQLAlchemy compatible database, or remotely to a tracking server.

DELTA LAKE INTEGRATION: Track large-scale datasets that fed your models with Delta Lake snapshots.

ARTIFACT STORE: Store large files such as S3 buckets, shared NFS file system, and models in Amazon S3, Azure Blob Storage, Google Cloud Storage, SFTP server, NFS, and local file paths.

MLFLOW MODELS: A standard format for packaging machine learning models that can be used in a variety of downstream tools — for example, real-time serving through a REST API or batch inference on Apache Spark.

MODEL CUSTOMIZATION: Use Custom Python Models and Custom Flavors for models from an ML library that is not explicitly supported by MLflow’s built-in flavors.

BUILT-IN MODEL FLAVORS: MLflow provides several standard flavors that might be useful in your applications, like Python and R functions, Hugging Face, OpenAI and LangChain, PyTorch, Spark MLlib, TensorFlow and ONNX.

BUILT-IN DEPLOYMENT TOOLS: Quickly deploy on Databricks via Apache Spark UDF for a local machine, or several other production environments such as Microsoft Azure ML, Amazon SageMaker, and building Docker Images for Deployment.

CENTRAL REPOSITORY: Register MLflow models with the MLflow Model Registry. A registered model has a unique name, version, stage and other metadata.

MODEL VERSIONING: Automatically keep track of versions for registered models when updated.

MODEL STAGE: Assign preset or custom stages to each model version, like “Staging” and “Production” to represent the lifecycle of a model.

CI/CD WORKFLOW INTEGRATION: Record stage transitions, request, review and approve changes as part of CI/CD pipelines for better control and governance.

MODEL STAGE TRANSITIONS: Record new registration events or changes as activities that automatically log users, changes, and additional metadata such as comments.

GOVERN ACCESS TO LLMS: Manage SaaS LLM credentials.

CONTROL COSTS: Set up rate limits.

STANDARDIZE LLM INTERACTIONS: Experiment with different OSS/SaaS LLMs with standard input/output interfaces for different tasks: completions, chat, embeddings.