Whats new in_mlflow
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In the last several months, MLflow has introduced significant platform enhancements that simplify machine learning lifecycle management. Expanded autologging capabilities, including a new integration with scikit-learn, have streamlined the instrumentation and experimentation process in MLflow Tracking. Additionally, schema management functionality has been incorporated into MLflow Models, enabling users to seamlessly inspect and control model inference APIs for batch and real-time scoring. In this session, we will explore these new features. We will share MLflow’s development roadmap, providing an overview of near-term advancements in the platform.
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Whats new in_mlflow
- 1. What’s new in ? Corey Zumar Software Engineer, Databricks A System to Accelerate the Machine Learning Lifecycle
- 2. Overview of new developments Feature deep dives Autologging for scikit-learn Model schemas & input examples MLflow’s plugin ecosystem What’s next for MLflow? Outline
- 3. : An Open Source ML Platform Experiment management Model managementReproducible runs Model packaging and deployment T R A C K I N G P R O J E C T S M O D E L R E G I S T R YM O D E L S Training Deployment Raw Data Data Prep ML Engineer Application DeveloperData Engineer Any Language Any ML Library
- 4. : Overview of new developments TRACKING PROJECTS MODELS MODEL REGISTRY - Scikit-learn autologging (1.11) - Fast.ai autologging (1.9) - UI accessibility, syntax highlighting for artifacts + PDF support (1.9, 1.11) - Backend plugin support (1.9) - YARN execution backend - Expanded artifact resolution capabilities (1.9) - Model schemas & input examples (1.9) - Deployment plugin support (1.9) - Spacy model flavor (1.8) - Tags for registered models and model versions (1.9) - Enhanced version comparison UI, including schemas (1.11) - Simplified model archiving (1.10)
- 5. Overview of new developments Feature deep dives Autologging for scikit-learn Model signatures & input examples MLflow’s plugin ecosystem What’s next for MLflow? Outline
- 6. Instrumenting scikit-learn workflows mlflow.start_run() mlflow.log_param(“alpha”, alpha) mlflow.log_param(“l1_ratio”, l1_ratio) model = ElasticNet(alpha, l1_ratio) model.fit(X, y) y_pred = model.predict(X) loss = mean_absolute_error(y, y_pred) mlflow.log_metric(“loss”, loss) mlflow.sklearn.log_model(model) mlflow.end_run()
- 7. Instrumenting scikit-learn workflows mlflow.start_run() mlflow.log_param(“alpha”, alpha) mlflow.log_param(“l1_ratio”, l1_ratio) model = ElasticNet(alpha, l1_ratio) model.fit(X, y) y_pred = model.predict(X) loss = mean_absolute_error(y, y_pred) mlflow.log_metric(“loss”, loss) mlflow.sklearn.log_model(model) mlflow.end_run() ElasticNet ARDRegression AdaBoostClassifier AdaBoostRegressor AdditiveChi2Sampler AffinityPropagation AgglomerativeClustering BaggingClassifier BaggingRegressor BayesianGaussianMixture BayesianRidge BernoulliNB BernoulliRBM Binarizer Birch CCA CalibratedClassifierCV CategoricalNB ClassifierChain ColumnTransformer ComplementNB CountVectorizer DBSCAN DecisionTreeClassifier DecisionTreeRegressor DictVectorizer DictionaryLearning DummyClassifier DummyRegressor ElasticNet ElasticNetCV EllipticEnvelope EmpiricalCovariance ExtraTreeClassifier ExtraTreeRegressor ExtraTreesClassifier ExtraTreesRegressor FactorAnalysis FastICA FeatureAgglomeration FeatureHasher FeatureUnion FunctionTransformer GammaRegressor GaussianMixture GaussianNB GaussianProcessClassifier GaussianProcessRegressor GaussianRandomProjection GenericUnivariateSelect GradientBoostingClassifier GradientBoostingRegressor GraphicalLasso GraphicalLassoCV GridSearchCV HashingVectorizer HistGradientBoostingClassifier HistGradientBoostingRegressor HuberRegressor IncrementalPCA IsolationForest Isomap IsotonicRegression IterativeImputer KBinsDiscretizer KMeans KNNImputer KNeighborsClassifier KNeighborsRegressor KNeighborsTransformer KernelCenterer KernelDensity KernelPCA KernelRidge LabelBinarizer LabelEncoder LabelPropagation LabelSpreading Lars LarsCV Lasso LassoCV LassoLars LassoLarsCV LassoLarsIC LatentDirichletAllocation LedoitWolf LinearDiscriminantAnalysis LinearRegression LinearSVC LinearSVR LocalOutlierFactor LocallyLinearEmbedding LogisticRegression LogisticRegressionCV MDS MLPClassifier MLPRegressor MaxAbsScaler MeanShift MinCovDet MinMaxScaler MiniBatchDictionaryLearning MiniBatchKMeans MiniBatchSparsePCA MissingIndicator MultiLabelBinarizer MultiOutputClassifier MultiOutputRegressor MultiTaskElasticNet MultiTaskElasticNetCV MultiTaskLasso MultiTaskLassoCV MultinomialNB NMF NearestCentroid NearestNeighbors NeighborhoodComponentsAnaly sis Normalizer NuSVC NuSVR Nystroem OAS OPTICS OneClassSVM OneHotEncoder OneVsOneClassifier OneVsRestClassifier OrdinalEncoder OrthogonalMatchingPursuit OrthogonalMatchingPursuitCV OutputCodeClassifier PCA PLSCanonical PLSRegression PLSSVD PassiveAggressiveClassifier PassiveAggressiveRegressor PatchExtractor Perceptron Pipeline PoissonRegressor PolynomialFeatures PowerTransformer QuadraticDiscriminantAnalysis QuantileTransformer RANSACRegressor RBFSampler RFE RFECV RadiusNeighborsClassifier RadiusNeighborsRegressor RadiusNeighborsTransformer RandomForestClassifier RandomForestRegressor RandomTreesEmbedding RandomizedSearchCV RegressorChain Ridge RidgeCV RidgeClassifier RidgeClassifierCV RobustScaler SGDClassifier SGDRegressor SVC SVR SelectFdr SelectFpr SelectFromModel SelectFwe SelectKBest SelectPercentile ShrunkCovariance SimpleImputer SkewedChi2Sampler SparseCoder SparsePCA SparseRandomProjection SpectralBiclustering SpectralClustering SpectralCoclustering SpectralEmbedding StackingClassifier StackingRegressor StandardScaler TSNE TfidfTransformer TfidfVectorizer TheilSenRegressor TransformedTargetRegressor TruncatedSVD TweedieRegressor VarianceThreshold VotingClassifier VotingRegressor
- 8. Autologging for scikit-learn One line to record params, metrics and models: mlflow.sklearn.autolog() new in 1.11
- 21. Overview of new developments Feature deep dives Autologging for scikit-learn Model schemas & input examples MLflow’s plugin ecosystem What’s next for MLflow? Outline
- 22. Model Schemas Specify input and output data types for models Incompatible schemas! Model Input Schema Output Schema Check Compatibility and Validate New Model Versions new in 1.9 zipcode: string, sqft: double, distance: double price: double log_model(…)
- 23. Model Schemas Infer model input / output signature from data infer_signature( inputs, outputs ) inputs: [ 'year built': long, 'year sold': long, 'lot area': long, 'zip code': long, 'quality': long ] outputs: ['sale price': double] log_model( ..., signature )
- 24. Model Schemas Validate inputs against schema during inference input_frame = pd.DataFrame.from_dict({ "year built": data["year built"], "year sold" data["year sold"], "lot area": data["lot area"], "zip code": data["zip code"], "condition": data["condition"], }) Input Schema Output Schema Model Schema Mismatch Error
- 34. Overview of new developments Feature deep dives Autologging for scikit-learn Model schemas & input examples MLflow’s plugin ecosystem What’s next for MLflow? Outline
- 35. Plugins Seamlessly integrate third-party code & infrastructure with MLflow Third-party plugin code TrackingStore Artifact Repository Deployment Client setup( entry_points = { "mlflow.tracking_store": …, … } ) Installation entrypoint MLflow Library
- 36. Plugins Supported plugin types: Tracking storage & search (Tracking Store) Tracking metadata collection (Run Context Provider) Model registry storage & search (Model Registry Store) Project execution (Project Backend) Model deployment (Deployment Client)
- 37. Creating a plugin 1. Implement the plugin interface (https://mlflow.org/docs/latest/plugins.html) 2. Add an MLflow plugin entrypoint & upload your package to PyPI 3. Add your plugin to the MLflow docs: https://mlflow.org/docs/latest/plugins.html#community-plugins
- 38. Pluggable way to create and manage deployment endpoints in MLflow Used in new endpoint Other integrations being ported: Deployments API mlflow deployments create -t redisai -n spam -m models:/SpamScorer/production mlflow deployments predict -t redisai –n spam -f emails.json new in 1.9
- 39. Pluggable way to execute MLflow Projects on a variety of compute resources Used for new YARN backend Other integrations being ported: Project Backend Plugins mlflow run --backend yarn https://github.com/mlflow/mlflow#examples/pytorch new in 1.9
- 40. Community Plugins Elastic Search backend for MLflow Tracking (experimental) (https://pypi.org/project/mlflow-elasticsearchstore/) Model deployment to RedisAI (https://pypi.org/project/mlflow-redisai/) Project execution on YARN (https://pypi.org/project/mlflow-yarn/) Artifact storage in SQL Server (https://pypi.org/project/mlflow-dbstore/) Artifact storage in Alibaba Cloud OSS (https://pypi.org/project/aliyunstoreplugin/)
- 41. Overview of new developments Feature deep dives Autologging for scikit-learn Model schemas & input examples MLflow’s plugin ecosystem What’s next for MLflow? Outline
- 42. What’s next for ? Model explainability integrations (see Data + AI Summit Europe 2020) Support for tensor input / output schemas Expanded input example & schema collection for autologging (XGBoost, TensorFlow, and more) Input Schema Output Schema autolog() Model
- 43. Model Serving on Databricks Tracking Experiment tracking Logged Model Model Registry Model management Model Serving Turnkey serving for MLflow models new Staging Production Archived Data Scientists Application Engineers Reports Applications ... REST Endpoint public preview Deployment Backends
- 45. Thank you! pip install mlflow GitHub Repository: github.com/mlflow/mlflow Website: mlflow.org Slack server: https://tinyurl.com/mlflow-slack-server