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class=section-nav-list-title>Concepts</span><ul class=section-nav-list><li><a href=/documentation/basics/>Basics of the Beam model</a></li><li><a href=/documentation/runtime/model/>How Beam executes a pipeline</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Beam programming guide</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/>Overview</a></li><li><a href=/documentation/programming-guide/#creating-a-pipeline>Pipelines</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>PCollections</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#pcollections>Creating a PCollection</a></li><li><a href=/documentation/programming-guide/#pcollection-characteristics>PCollection characteristics</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Transforms</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#applying-transforms>Applying transforms</a></li><li><span class=section-nav-list-title>Core Beam transforms</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#pardo>ParDo</a></li><li><a href=/documentation/programming-guide/#groupbykey>GroupByKey</a></li><li><a href=/documentation/programming-guide/#cogroupbykey>CoGroupByKey</a></li><li><a href=/documentation/programming-guide/#combine>Combine</a></li><li><a href=/documentation/programming-guide/#flatten>Flatten</a></li><li><a href=/documentation/programming-guide/#partition>Partition</a></li></ul></li><li><a href=/documentation/programming-guide/#requirements-for-writing-user-code-for-beam-transforms>Requirements for user code</a></li><li><a href=/documentation/programming-guide/#side-inputs>Side inputs</a></li><li><a href=/documentation/programming-guide/#additional-outputs>Additional outputs</a></li><li><a href=/documentation/programming-guide/#composite-transforms>Composite transforms</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Pipeline I/O</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#pipeline-io>Using I/O transforms</a></li><li><a href=/documentation/io/connectors/>I/O connectors</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>I/O connector guides</span><ul class=section-nav-list><li><a href=/documentation/io/built-in/parquet/>Apache Parquet I/O connector</a></li><li><a href=/documentation/io/built-in/hadoop/>Hadoop Input/Output Format IO</a></li><li><a href=/documentation/io/built-in/hcatalog/>HCatalog IO</a></li><li><a href=/documentation/io/built-in/google-bigquery/>Google BigQuery I/O connector</a></li><li><a href=/documentation/io/built-in/snowflake/>Snowflake I/O connector</a></li><li><a href=/documentation/io/built-in/cdap/>CDAP I/O connector</a></li><li><a href=/documentation/io/built-in/sparkreceiver/>Spark Receiver I/O connector</a></li><li><a href=/documentation/io/built-in/singlestore/>SingleStoreDB I/O connector</a></li><li><a href=/documentation/io/built-in/webapis/>Web APIs I/O connector</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Developing new I/O connectors</span><ul class=section-nav-list><li><a href=/documentation/io/developing-io-overview/>Overview: Developing connectors</a></li><li><a href=/documentation/io/developing-io-java/>Developing connectors (Java)</a></li><li><a href=/documentation/io/developing-io-python/>Developing connectors (Python)</a></li><li><a href=/documentation/io/io-standards/>I/O Standards</a></li></ul></li><li><a href=/documentation/io/testing/>Testing I/O transforms</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Schemas</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#what-is-a-schema>What is a schema</a></li><li><a href=/documentation/programming-guide/#schemas-for-pl-types>Schemas for programming language types</a></li><li><a href=/documentation/programming-guide/#schema-definition>Schema definition</a></li><li><a href=/documentation/programming-guide/#logical-types>Logical types</a></li><li><a href=/documentation/programming-guide/#creating-schemas>Creating schemas</a></li><li><a href=/documentation/programming-guide/#using-schemas>Using schemas</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Data encoding and type safety</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#data-encoding-and-type-safety>Data encoding basics</a></li><li><a href=/documentation/programming-guide/#specifying-coders>Specifying coders</a></li><li><a href=/documentation/programming-guide/#default-coders-and-the-coderregistry>Default coders and the CoderRegistry</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Windowing</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#windowing>Windowing basics</a></li><li><a href=/documentation/programming-guide/#provided-windowing-functions>Provided windowing functions</a></li><li><a href=/documentation/programming-guide/#setting-your-pcollections-windowing-function>Setting your PCollection’s windowing function</a></li><li><a href=/documentation/programming-guide/#watermarks-and-late-data>Watermarks and late data</a></li><li><a href=/documentation/programming-guide/#adding-timestamps-to-a-pcollections-elements>Adding timestamps to a PCollection’s elements</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Triggers</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#triggers>Trigger basics</a></li><li><a href=/documentation/programming-guide/#event-time-triggers>Event time triggers and the default trigger</a></li><li><a href=/documentation/programming-guide/#processing-time-triggers>Processing time triggers</a></li><li><a href=/documentation/programming-guide/#data-driven-triggers>Data-driven triggers</a></li><li><a href=/documentation/programming-guide/#setting-a-trigger>Setting a trigger</a></li><li><a href=/documentation/programming-guide/#composite-triggers>Composite triggers</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Metrics</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#metrics>Metrics basics</a></li><li><a href=/documentation/programming-guide/#types-of-metrics>Types of metrics</a></li><li><a href=/documentation/programming-guide/#querying-metrics>Querying metrics</a></li><li><a href=/documentation/programming-guide/#using-metrics>Using metrics in pipeline</a></li><li><a href=/documentation/programming-guide/#export-metrics>Export metrics</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>State and Timers</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#types-of-state>Types of state</a></li><li><a href=/documentation/programming-guide/#deferred-state-reads>Deferred state reads</a></li><li><a href=/documentation/programming-guide/#timers>Timers</a></li><li><a href=/documentation/programming-guide/#garbage-collecting-state>Garbage collecting state</a></li><li><a href=/documentation/programming-guide/#state-timers-examples>State and timers examples</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Splittable DoFns</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#sdf-basics>Basics</a></li><li><a href=/documentation/programming-guide/#sizing-and-progress>Sizing and progress</a></li><li><a href=/documentation/programming-guide/#user-initiated-checkpoint>User-initiated checkpoint</a></li><li><a href=/documentation/programming-guide/#runner-initiated-split>Runner initiated split</a></li><li><a href=/documentation/programming-guide/#watermark-estimation>Watermark estimation</a></li><li><a href=/documentation/programming-guide/#truncating-during-drain>Truncating during drain</a></li><li><a href=/documentation/programming-guide/#bundle-finalization>Bundle finalization</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Multi-language Pipelines</span><ul class=section-nav-list><li><a href=/documentation/programming-guide/#create-x-lang-transforms>Creating cross-language transforms</a></li><li><a href=/documentation/programming-guide/#use-x-lang-transforms>Using cross-language transforms</a></li><li><a href=/documentation/programming-guide/#x-lang-transform-runner-support>Runner Support</a></li></ul></li><li><a href=/documentation/programming-guide/#batched-dofns>Batched DoFns</a></li><li><a href=/documentation/programming-guide/#transform-service>Transform service</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Pipeline development lifecycle</span><ul class=section-nav-list><li><a href=/documentation/pipelines/design-your-pipeline/>Design Your Pipeline</a></li><li><a href=/documentation/pipelines/create-your-pipeline/>Create Your Pipeline</a></li><li><a href=/documentation/pipelines/test-your-pipeline/>Test Your Pipeline</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Common pipeline patterns</span><ul class=section-nav-list><li><a href=/documentation/patterns/overview/>Overview</a></li><li><a href=/documentation/patterns/file-processing/>File processing</a></li><li><a href=/documentation/patterns/side-inputs/>Side inputs</a></li><li><a href=/documentation/patterns/pipeline-options/>Pipeline options</a></li><li><a href=/documentation/patterns/custom-io/>Custom I/O</a></li><li><a href=/documentation/patterns/custom-windows/>Custom windows</a></li><li><a href=/documentation/patterns/bigqueryio/>BigQueryIO</a></li><li><a href=/documentation/patterns/ai-platform/>AI Platform</a></li><li><a href=/documentation/patterns/schema/>Schema</a></li><li><a href=/documentation/patterns/bqml/>BigQuery ML</a></li><li><a href=/documentation/patterns/grouping-elements-for-efficient-external-service-calls/>Grouping elements for efficient external service calls</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>AI/ML pipelines</span><ul class=section-nav-list><li><a href=/documentation/ml/overview/>Get started with AI/ML</a></li><li><a href=/documentation/ml/about-ml/>About Beam ML</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Prediction and inference</span><ul class=section-nav-list><li><a href=/documentation/ml/inference-overview/>Overview</a></li><li><a href=/documentation/ml/multi-model-pipelines/>Build a pipeline with multiple models</a></li><li><a href=/documentation/ml/tensorrt-runinference>Build a custom model handler with TensorRT</a></li><li><a href=/documentation/ml/large-language-modeling>Use LLM inference</a></li><li><a href=/documentation/ml/multi-language-inference/>Build a multi-language inference pipeline</a></li><li><a href=/documentation/ml/side-input-updates/>Update your model in production</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Data processing</span><ul class=section-nav-list><li><a href=/documentation/ml/preprocess-data/>Preprocess data</a></li><li><a href=/documentation/ml/data-processing/>Explore your data</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Workflow orchestration</span><ul class=section-nav-list><li><a href=/documentation/ml/orchestration/>Use ML-OPS workflow orchestrators</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Model training</span><ul class=section-nav-list><li><a href=/documentation/ml/per-entity-training>Per-entity training</a></li><li><a href=/documentation/ml/online-clustering/>Online clustering</a></li><li><a href=/documentation/ml/model-evaluation/>ML model evaluation</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Use cases</span><ul class=section-nav-list><li><a href=/documentation/ml/anomaly-detection/>Build an anomaly detection pipeline</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Reference</span><ul class=section-nav-list><li><a href=/documentation/ml/runinference-metrics/>RunInference metrics</a></li><li><a href=/documentation/ml/model-evaluation/>Model validation</a></li></ul></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Runtime systems</span><ul class=section-nav-list><li><a href=/documentation/runtime/environments/>Container environments</a></li><li><a href=/documentation/runtime/resource-hints/>Resource hints</a></li><li><a href=/documentation/runtime/sdk-harness-config/>SDK Harness Configuration</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Transform catalog</span><ul class=section-nav-list><li class=section-nav-item--collapsible><span class=section-nav-list-title>Python</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/overview/>Overview</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Element-wise</span><ul class=section-nav-list><li class=section-nav-item--collapsible><span class=section-nav-list-title>Enrichment</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/elementwise/enrichment/>Overview</a></li><li><a href=/documentation/transforms/python/elementwise/enrichment-bigtable/>Bigtable example</a></li><li><a href=/documentation/transforms/python/elementwise/enrichment-vertexai/>Vertex AI Feature Store examples</a></li></ul></li><li><a href=/documentation/transforms/python/elementwise/filter/>Filter</a></li><li><a href=/documentation/transforms/python/elementwise/flatmap/>FlatMap</a></li><li><a href=/documentation/transforms/python/elementwise/keys/>Keys</a></li><li><a href=/documentation/transforms/python/elementwise/kvswap/>KvSwap</a></li><li><a href=/documentation/transforms/python/elementwise/map/>Map</a></li><li><a href=/documentation/transforms/python/elementwise/mltransform/>MLTransform</a></li><li><a href=/documentation/transforms/python/elementwise/pardo/>ParDo</a></li><li><a href=/documentation/transforms/python/elementwise/partition/>Partition</a></li><li><a href=/documentation/transforms/python/elementwise/regex/>Regex</a></li><li><a href=/documentation/transforms/python/elementwise/reify/>Reify</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>RunInference</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/elementwise/runinference/>Overview</a></li><li><a href=/documentation/transforms/python/elementwise/runinference-pytorch/>PyTorch examples</a></li><li><a href=/documentation/transforms/python/elementwise/runinference-sklearn/>Sklearn examples</a></li></ul></li><li><a href=/documentation/transforms/python/elementwise/tostring/>ToString</a></li><li><a href=/documentation/transforms/python/elementwise/values/>Values</a></li><li><a href=/documentation/transforms/python/elementwise/withtimestamps/>WithTimestamps</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Aggregation</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/aggregation/approximatequantiles/>ApproximateQuantiles</a></li><li><a href=/documentation/transforms/python/aggregation/approximateunique/>ApproximateUnique</a></li><li><a href=/documentation/transforms/python/aggregation/cogroupbykey/>CoGroupByKey</a></li><li><a href=/documentation/transforms/python/aggregation/combineglobally/>CombineGlobally</a></li><li><a href=/documentation/transforms/python/aggregation/combineperkey/>CombinePerKey</a></li><li><a href=/documentation/transforms/python/aggregation/combinevalues/>CombineValues</a></li><li><a href=/documentation/transforms/python/aggregation/count/>Count</a></li><li><a href=/documentation/transforms/python/aggregation/distinct/>Distinct</a></li><li><a href=/documentation/transforms/python/aggregation/groupby/>GroupBy</a></li><li><a href=/documentation/transforms/python/aggregation/groupbykey/>GroupByKey</a></li><li><a href=/documentation/transforms/python/aggregation/groupintobatches/>GroupIntoBatches</a></li><li><a href=/documentation/transforms/python/aggregation/latest/>Latest</a></li><li><a href=/documentation/transforms/python/aggregation/max/>Max</a></li><li><a href=/documentation/transforms/python/aggregation/mean/>Mean</a></li><li><a href=/documentation/transforms/python/aggregation/min/>Min</a></li><li><a href=/documentation/transforms/python/aggregation/sample/>Sample</a></li><li><a href=/documentation/transforms/python/aggregation/sum/>Sum</a></li><li><a href=/documentation/transforms/python/aggregation/top/>Top</a></li><li><a href=/documentation/transforms/python/aggregation/tolist/>ToList</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Other</span><ul class=section-nav-list><li><a href=/documentation/transforms/python/other/create/>Create</a></li><li><a href=/documentation/transforms/python/other/flatten/>Flatten</a></li><li><a href=/documentation/transforms/python/other/reshuffle/>Reshuffle</a></li><li><a href=/documentation/transforms/python/other/windowinto/>WindowInto</a></li></ul></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Java</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/overview/>Overview</a></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Element-wise</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/elementwise/filter/>Filter</a></li><li><a href=/documentation/transforms/java/elementwise/flatmapelements/>FlatMapElements</a></li><li><a href=/documentation/transforms/java/elementwise/keys/>Keys</a></li><li><a href=/documentation/transforms/java/elementwise/kvswap/>KvSwap</a></li><li><a href=/documentation/transforms/java/elementwise/mapelements/>MapElements</a></li><li><a href=/documentation/transforms/java/elementwise/pardo/>ParDo</a></li><li><a href=/documentation/transforms/java/elementwise/partition/>Partition</a></li><li><a href=/documentation/transforms/java/elementwise/regex/>Regex</a></li><li><a href=/documentation/transforms/java/elementwise/reify/>Reify</a></li><li><a href=/documentation/transforms/java/elementwise/tostring/>ToString</a></li><li><a href=/documentation/transforms/java/elementwise/values/>Values</a></li><li><a href=/documentation/transforms/java/elementwise/withkeys/>WithKeys</a></li><li><a href=/documentation/transforms/java/elementwise/withtimestamps/>WithTimestamps</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Aggregation</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/aggregation/approximatequantiles/>ApproximateQuantiles</a></li><li><a href=/documentation/transforms/java/aggregation/approximateunique/>ApproximateUnique</a></li><li><a href=/documentation/transforms/java/aggregation/cogroupbykey/>CoGroupByKey</a></li><li><a href=/documentation/transforms/java/aggregation/combine/>Combine</a></li><li><a href=/documentation/transforms/java/aggregation/combinewithcontext/>CombineWithContext</a></li><li><a href=/documentation/transforms/java/aggregation/count/>Count</a></li><li><a href=/documentation/transforms/java/aggregation/distinct/>Distinct</a></li><li><a href=/documentation/transforms/java/aggregation/groupbykey/>GroupByKey</a></li><li><a href=/documentation/transforms/java/aggregation/groupintobatches/>GroupIntoBatches</a></li><li><a href=/documentation/transforms/java/aggregation/hllcount/>HllCount</a></li><li><a href=/documentation/transforms/java/aggregation/latest/>Latest</a></li><li><a href=/documentation/transforms/java/aggregation/max/>Max</a></li><li><a href=/documentation/transforms/java/aggregation/mean/>Mean</a></li><li><a href=/documentation/transforms/java/aggregation/min/>Min</a></li><li><a href=/documentation/transforms/java/aggregation/sample/>Sample</a></li><li><a href=/documentation/transforms/java/aggregation/sum/>Sum</a></li><li><a href=/documentation/transforms/java/aggregation/top/>Top</a></li></ul></li><li class=section-nav-item--collapsible><span class=section-nav-list-title>Other</span><ul class=section-nav-list><li><a href=/documentation/transforms/java/other/create/>Create</a></li><li><a href=/documentation/transforms/java/other/flatten/>Flatten</a></li><li><a href=/documentation/transforms/java/other/passert/>PAssert</a></li><li><a href=/documentation/transforms/java/other/view/>View</a></li><li><a href=/documentation/transforms/java/other/window/>Window</a></li></ul></li></ul></li></ul></li><li><a href=/documentation/glossary/>Glossary</a></li><li><a href=https://cwiki.apache.org/confluence/display/BEAM/Apache+Beam>Beam Wiki <img src=/images/external-link-icon.png width=14 height=14 alt="External link."></a></li></ul></nav></div><nav class="page-nav clearfix" data-offset-top=90 data-offset-bottom=500><nav id=TableOfContents><ul><li><a href=#use-mltransform>Why use MLTransform</a></li><li><a href=#support>Support and limitations</a></li><li><a href=#transforms>Transforms</a><ul><li><a href=#text-embedding-transforms>Text embedding transforms</a></li><li><a href=#data-processing-transforms-that-use-tft>Data processing transforms that use TFT</a></li></ul></li><li><a href=#io>I/O requirements</a></li><li><a href=#artifacts>Artifacts</a><ul><li><a href=#write-mode>Write mode</a></li><li><a href=#read-mode>Read mode</a></li><li><a href=#artifact-workflow>Artifact workflow</a></li></ul></li><li><a href=#use-mltransform>Preprocess data with MLTransform</a></li></ul></nav></nav><div class="body__contained body__section-nav arrow-list arrow-list--no-mt"><h1 id=preprocess-data-with-mltransform>Preprocess data with MLTransform</h1><p>This page explains how to use the <code>MLTransform</code> class to preprocess data for machine learning (ML) |
| workflows. Apache Beam provides a set of data processing transforms for |
| preprocessing data for training and inference. The <code>MLTransform</code> class wraps the |
| various transforms in one class, simplifying your workflow. For a full list of |
| available transforms, see the <a href=#transforms>Transforms</a> section on this page.</p><h2 id=use-mltransform>Why use MLTransform</h2><ul><li>With <code>MLTransform</code>, you can use the same preprocessing steps for both |
| training and inference, which ensures consistent results.</li><li>Generate <a href=https://en.wikipedia.org/wiki/Embedding>embeddings</a> on text data using large language models (LLMs).</li><li><code>MLTransform</code> can do a full pass on the dataset, which is useful when |
| you need to transform a single element only after analyzing the entire |
| dataset. For example, with <code>MLTransform</code>, you can complete the following tasks:<ul><li>Normalize an input value by using the minimum and maximum value |
| of the entire dataset.</li><li>Convert <code>floats</code> to <code>ints</code> by assigning them buckets, based on |
| the observed data distribution.</li><li>Convert <code>strings</code> to <code>ints</code> by generating vocabulary over the |
| entire dataset.</li><li>Count the occurrences of words in all the documents to calculate |
| <a href=https://en.wikipedia.org/wiki/Tf%E2%80%93idf>TF-IDF</a> |
| weights.</li></ul></li></ul><h2 id=support>Support and limitations</h2><ul><li>Available in the Apache Beam Python SDK versions 2.53.0 and later.</li><li>Supports Python 3.8, 3.9, and 3.10.</li><li>Only available for pipelines that use <a href=/documentation/programming-guide/#single-global-window>default windows</a>.</li></ul><h2 id=transforms>Transforms</h2><p>You can use <code>MLTransform</code> to generate text embeddings and to perform various data processing transforms.</p><h3 id=text-embedding-transforms>Text embedding transforms</h3><p>You can use <code>MLTranform</code> to generate embeddings that you can use to push data into vector databases or to run inference.</p><div class=table-wrapper><table><thead><tr><th>Transform name</th><th>Description</th></tr></thead><tbody><tr><td>SentenceTransformerEmbeddings</td><td>Uses the Hugging Face <a href=https://huggingface.co/sentence-transformers><code>sentence-transformers</code></a> models to generate text embeddings.</td></tr><tr><td>VertexAITextEmbeddings</td><td>Uses models from the <a href=https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings>the Vertex AI text-embeddings API</a> to generate text embeddings.</td></tr></tbody></table></div><h3 id=data-processing-transforms-that-use-tft>Data processing transforms that use TFT</h3><p>The following set of transforms available in the <code>MLTransform</code> class come from |
| the TensorFlow Transforms (TFT) library. TFT offers specialized processing |
| modules for machine learning tasks. For information about these transforms, see |
| <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft>Module:tft</a> in the |
| TensorFlow documentation.</p><div class=table-wrapper><table><thead><tr><th>Transform name</th><th>Description</th></tr></thead><tbody><tr><td>ApplyBuckets</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/apply_buckets><code>tft.apply_buckets</code></a> in the TensorFlow documentation.</td></tr><tr><td>Bucketize</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/bucketize><code>tft.bucketize</code></a> in the TensorFlow documentation.</td></tr><tr><td>ComputeAndApplyVocabulary</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/compute_and_apply_vocabulary><code>tft.compute_and_apply_vocabulary</code></a> in the TensorFlow documentation.</td></tr><tr><td>NGrams</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/ngrams><code>tft.ngrams</code></a> in the TensorFlow documentation.</td></tr><tr><td>ScaleByMinMax</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/scale_by_min_max><code>tft.scale_by_min_max</code></a> in the TensorFlow documentation.</td></tr><tr><td>ScaleTo01</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/scale_to_0_1><code>tft.scale_to_0_1</code></a> in the TensorFlow documentation.</td></tr><tr><td>ScaleToZScore</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/scale_to_z_score><code>tft.scale_to_z_score</code></a> in the TensorFlow documentation.</td></tr><tr><td>TFIDF</td><td>See <a href=https://www.tensorflow.org/tfx/transform/api_docs/python/tft/tfidf><code>tft.tfidf</code></a> in the TensorFlow documentation.</td></tr></tbody></table></div><h2 id=io>I/O requirements</h2><ul><li>Input to the <code>MLTransform</code> class must be a dictionary.</li><li><code>MLTransform</code> outputs a Beam <code>Row</code> object with transformed elements.</li><li>The output <code>PCollection</code> is a schema <code>PCollection</code>. The output schema |
| contains the transformed columns.</li></ul><h2 id=artifacts>Artifacts</h2><p>Artifacts are additional data elements created by data transformations. |
| Examples of artifacts are the minimum and maximum values from a <code>ScaleTo01</code> |
| transformation, or the mean and variance from a <code>ScaleToZScore</code> |
| transformation.</p><p>In the <code>MLTransform</code> class, the <code>write_artifact_location</code> and the |
| <code>read_artifact_location</code> parameters determine |
| whether the <code>MLTransform</code> class creates artifacts or retrieves |
| artifacts.</p><h3 id=write-mode>Write mode</h3><p>When you use the <code>write_artifact_location</code> parameter, the <code>MLTransform</code> class runs the |
| specified transformations on the dataset and then creates artifacts from these |
| transformations. The artifacts are stored in the location that you specify in |
| the <code>write_artifact_location</code> parameter.</p><p>Write mode is useful when you want to store the results of your transformations |
| for future use. For example, if you apply the same transformations on a |
| different dataset, use write mode to ensure that the transformation parameters |
| remain consistent.</p><p>The following examples demonstrate how write mode works.</p><ul><li>The <code>ComputeAndApplyVocabulary</code> transform generates a vocabulary file that contains the |
| vocabulary generated over the entire dataset. The vocabulary file is stored in |
| the location specified by the <code>write_artifact_location</code> parameter value. |
| The <code>ComputeAndApplyVocabulary</code> |
| transform outputs the indices of the vocabulary to the vocabulary file.</li><li>The <code>ScaleToZScore</code> transform calculates the mean and variance over the entire dataset |
| and then normalizes the entire dataset using the mean and variance. |
| When you use the <code>write_artifact_location</code> parameter, these |
| values are stored as a <code>tensorflow</code> graph in the location specified by |
| the <code>write_artifact_location</code> parameter value. You can reuse the values in read mode |
| to ensure that future transformations use the same mean and variance for normalization.</li></ul><h3 id=read-mode>Read mode</h3><p>When you use the <code>read_artifact_location</code> parameter, the <code>MLTransform</code> class expects the |
| artifacts to exist in the value provided in the <code>read_artifact_location</code> parameter. |
| In this mode, <code>MLTransform</code> retrieves the artifacts and uses them in the |
| transform. Because the transformations are stored in the artifacts when you use |
| read mode, you don’t need to specify the transformations.</p><h3 id=artifact-workflow>Artifact workflow</h3><p>The following scenario provides an example use case for artifacts.</p><p>Before training a machine learning model, you use <code>MLTransform</code> with the |
| <code>write_artifact_location</code> parameter. |
| When you run <code>MLTransform</code>, it applies transformations that preprocess the |
| dataset. The transformation produces artifacts that are stored in the location |
| specified by the <code>write_artifact_location</code> parameter value.</p><p>After preprocessing, you use the transformed data to train the machine learning |
| model.</p><p>After training, you run inference. You use new test data and use the |
| <code>read_artifact_location</code> parameter. By using this setting, you ensure that the test |
| data undergoes the same preprocessing steps as the training data. In read |
| mode, running <code>MLTransform</code> fetches the transformation artifacts from the |
| location specified in the <code>read_artifact_location</code> parameter value. |
| <code>MLTransform</code> applies these artifacts to the test data.</p><p>This workflow provides consistency in preprocessing steps for both training and |
| test data. This consistency ensures that the model can accurately evaluate the |
| test data and maintain the integrity of the model’s performance.</p><h2 id=use-mltransform>Preprocess data with MLTransform</h2><p>To use the <code>MLTransform</code> transform to preprocess data, add the following code to |
| your pipeline:</p><pre tabindex=0><code> import apache_beam as beam |
| from apache_beam.ml.transforms.base import MLTransform |
| from apache_beam.ml.transforms.tft import <TRANSFORM_NAME> |
| import tempfile |
| |
| data = [ |
| { |
| <DATA> |
| }, |
| ] |
| |
| artifact_location = tempfile.mkdtemp() |
| <TRANSFORM_FUNCTION_NAME> = <TRANSFORM_NAME>(columns=['x']) |
| |
| with beam.Pipeline() as p: |
| transformed_data = ( |
| p |
| | beam.Create(data) |
| | MLTransform(write_artifact_location=artifact_location).with_transform( |
| <TRANSFORM_FUNCTION_NAME>) |
| | beam.Map(print)) |
| </code></pre><p>Replace the following values:</p><ul><li>TRANSFORM_NAME: The name of the <a href=#transforms>transform</a> to use.</li><li>DATA: The input data to transform.</li><li>TRANSFORM_FUNCTION_NAME: The name that you assign to your transform |
| function in your code.</li></ul><p>For more examples, see |
| <a href=/documentation/transforms/python/elementwise/mltransform>MLTransform for data processing</a> |
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