New built-in Amazon SageMaker algorithms for tabular data modeling: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer

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{"value":"[Amazon SageMaker](https://aws.amazon.com/sagemaker/) provides a suite of [built-in algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html), [pre-trained models](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html#jumpstart-solutions), and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text.\n\nStarting today, SageMaker provides four new built-in tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer. You can use these popular, state-of-the-art algorithms for both tabular classification and regression tasks. They’re available through the [built-in algorithms](https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html) on the SageMaker console as well as through the [Amazon SageMaker JumpStart](https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html) UI inside [Amazon SageMaker Studio](https://docs.aws.amazon.com/sagemaker/latest/dg/studio.html).\n\nThe following is the list of the four new built-in algorithms, with links to their documentation, example notebooks, and source.\n\n![微信图片_20220830130411.png](1)\n\nIn the following sections, we provide a brief technical description of each algorithm, and examples of how to train a model via the SageMaker SDK or SageMaker Jumpstart.\n\n\n#### **LightGBM**\n\n\n[LightGBM](https://lightgbm.readthedocs.io/en/latest/) is a popular and efficient open-source implementation of the Gradient Boosting Decision Tree (GBDT) algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. LightGBM uses additional techniques to significantly improve the efficiency and scalability of conventional GBDT.\n\n\n#### **CatBoost**\n\n\n[CatBoost](https://catboost.ai/) is a popular and high-performance open-source implementation of the GBDT algorithm. Two critical algorithmic advances are introduced in CatBoost: the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms.\n\n\n#### **AutoGluon-Tabular**\n\n\n[AutoGluon-Tabular](https://auto.gluon.ai/stable/index.html) is an open-source AutoML project developed and maintained by Amazon that performs advanced data processing, deep learning, and multi-layer stack ensembling. It automatically recognizes the data type in each column for robust data preprocessing, including special handling of text fields. AutoGluon fits various models ranging from off-the-shelf boosted trees to customized neural network models. These models are ensembled in a novel way: models are stacked in multiple layers and trained in a layer-wise manner that guarantees raw data can be translated into high-quality predictions within a given time constraint. Over-fitting is mitigated throughout this process by splitting the data in various ways with careful tracking of out-of-fold examples. AutoGluon is optimized for performance, and its out-of-the-box usage has achieved several top-3 and top-10 positions in data science competitions.\n\n\n#### **TabTransformer**\n\n\n[TabTransformer](https://www.amazon.science/blog/bringing-the-power-of-deep-learning-to-data-in-tables) is a novel deep tabular data modelling architecture for supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. Furthermore, the contextual embeddings learned from TabTransformer are highly robust against both missing and noisy data features, and provide better interpretability. This model is the product of recent [Amazon Science](https://www.amazon.science/) research ([paper](https://arxiv.org/pdf/2012.06678.pdf) and official [blog post](https://www.amazon.science/blog/bringing-the-power-of-deep-learning-to-data-in-tables) here) and has been widely adopted by the ML community, with various third-party implementations ([Keras](https://keras.io/examples/structured_data/tabtransformer/), [AutoGluon](https://github.com/awslabs/autogluon/tree/master/tabular/src/autogluon/tabular/models/tab_transformer),) and social media features such as [tweets](https://twitter.com/fchollet/status/1484212136846921730), [towardsdatascience](https://towardsdatascience.com/pytorch-widedeep-deep-learning-for-tabular-data-9cd1c48eb40d), medium, and [Kaggle](https://www.kaggle.com/code/usharengaraju/tensorflow-tabtransformer/notebook).\n\n\n#### **Benefits of SageMaker built-in algorithms**\n\n\nWhen selecting an algorithm for your particular type of problem and data, using a SageMaker built-in algorithm is the easiest option, because doing so comes with the following major benefits:\n\n- The built-in algorithms require no coding to start running experiments. The only inputs you need to provide are the data, hyperparameters, and compute resources. This allows you to run experiments more quickly, with less overhead for tracking results and code changes.\n- The built-in algorithms come with parallelization across multiple compute instances and GPU support right out of the box for all applicable algorithms (some algorithms may not be included due to inherent limitations). If you have a lot of data with which to train your model, most built-in algorithms can easily scale to meet the demand. Even if you already have a pre-trained model, it may still be easier to use its corollary in SageMaker and input the hyperparameters you already know rather than port it over and write a training script yourself.\n- You are the owner of the resulting model artifacts. You can take that model and deploy it on SageMaker for several different inference patterns (check out all the [available deployment types](https://docs.aws.amazon.com/sagemaker/latest/dg/inference-cost-optimization.html)) and easy endpoint scaling and management, or you can deploy it wherever else you need it.\n\nLet’s now see how to train one of these built-in algorithms.\n\n\n#### **Train a built-in algorithm using the SageMaker SDK**\n\n\nTo train a selected model, we need to get that model’s URI, as well as that of the training script and the container image used for training. Thankfully, these three inputs depend solely on the model name, version (for a list of the available models, see [JumpStart Available Model Table](https://sagemaker.readthedocs.io/en/stable/doc_utils/jumpstart.html)), and the type of instance you want to train on. This is demonstrated in the following code snippet:\n\n```\\nfrom sagemaker import image_uris, model_uris, script_uris\\n\\ntrain_model_id, train_model_version, train_scope = \\"lightgbm-classification-model\\", \\"*\\", \\"training\\"\\ntraining_instance_type = \\"ml.m5.xlarge\\"\\n\\n# Retrieve the docker image\\ntrain_image_uri = image_uris.retrieve(\\n region=None,\\n framework=None,\\n model_id=train_model_id,\\n model_version=train_model_version,\\n image_scope=train_scope,\\n instance_type=training_instance_type\\n)\\n# Retrieve the training script\\ntrain_source_uri = script_uris.retrieve(\\n model_id=train_model_id, model_version=train_model_version, script_scope=train_scope\\n)\\n# Retrieve the model artifact; in the tabular case, the model is not pre-trained \\ntrain_model_uri = model_uris.retrieve(\\n model_id=train_model_id, model_version=train_model_version, model_scope=train_scope\\n)\\n```\n\nThe ```train_model_id``` changes to ```lightgbm-regression-model``` if we’re dealing with a regression problem. The IDs for all the other models introduced in this post are listed in the following table.\n\n![微信图片_20220830131105.png](2)\n\nWe then define where our input is on [Amazon Simple Storage Service](http://aws.amazon.com/s3) ([Amazon S3](https://aws.amazon.com/cn/s3/?trk=cndc-detail)). We’re using a public sample dataset for this example. We also define where we want our output to go, and retrieve the default list of hyperparameters needed to train the selected model. You can change their value to your liking.\n\n```\\nimport sagemaker\\nfrom sagemaker import hyperparameters\\n\\nsess = sagemaker.Session()\\nregion = sess.boto_session.region_name\\n\\n# URI of sample training dataset\\ntraining_dataset_s3_path = f\\"s3:///jumpstart-cache-prod-{region}/training-datasets/tabular_multiclass/\\"\\n\\n# URI for output artifacts \\noutput_bucket = sess.default_bucket()\\ns3_output_location = f\\"s3://{output_bucket}/jumpstart-example-tabular-training/output\\"\\n\\n# Retrieve the default hyper-parameters for training\\nhyperparameters = hyperparameters.retrieve_default(\\n model_id=train_model_id, model_version=train_model_version\\n)\\n\\n# [Optional] Override default hyperparameters with custom values\\nhyperparameters[\\n \\"num_boost_round\\"\\n] = \\"500\\" # The same hyperparameter is named as \\"iterations\\" for CatBoost\\n```\n\nFinally, we instantiate a SageMaker ```Estimator``` with all the retrieved inputs and launch the training job with ```.fit```, passing it our training dataset URI. The ```entry_point``` script provided is named ```transfer_learning.py``` (the same for other tasks and algorithms), and the input data channel passed to ```.fit``` must be named ```training```.\n\n```\\nfrom sagemaker.estimator import Estimator\\nfrom sagemaker.utils import name_from_base\\n\\n# Unique training job name\\ntraining_job_name = name_from_base(f\\"built-in-example-{model_id}\\")\\n\\n# Create SageMaker Estimator instance\\ntc_estimator = Estimator(\\n role=aws_role,\\n image_uri=train_image_uri,\\n source_dir=train_source_uri,\\n model_uri=train_model_uri,\\n entry_point=\\"transfer_learning.py\\",\\n instance_count=1,\\n instance_type=training_instance_type,\\n max_run=360000,\\n hyperparameters=hyperparameters,\\n output_path=s3_output_location,\\n)\\n\\n# Launch a SageMaker Training job by passing s3 path of the training data\\ntc_estimator.fit({\\"training\\": training_dataset_s3_path}, logs=True)\\n```\n\nNote that you can train built-in algorithms with [SageMaker automatic model tuning](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html) to select the optimal hyperparameters and further improve model performance.\n\n\n#### **Train a built-in algorithm using SageMaker JumpStart**\n\n\nYou can also train any these built-in algorithms with a few clicks via the SageMaker JumpStart UI. JumpStart is a SageMaker feature that allows you to train and deploy built-in algorithms and pre-trained models from various ML frameworks and model hubs through a graphical interface. It also allows you to deploy fully fledged ML solutions that string together ML models and various other AWS services to solve a targeted use case.\n\nFor more information, refer to [Run text classification with Amazon SageMaker JumpStart using TensorFlow Hub and Hugging Face models](https://aws.amazon.com/blogs/machine-learning/run-text-classification-with-amazon-sagemaker-jumpstart-using-tensorflow-hub-and-huggingface-models/).\n\n\n#### **Conclusion**\n\n\nIn this post, we announced the launch of four powerful new built-in algorithms for ML on tabular datasets now available on SageMaker. We provided a technical description of what these algorithms are, as well as an example training job for LightGBM using the SageMaker SDK.\n\nBring your own dataset and try these new algorithms on SageMaker, and check out the sample notebooks to use built-in algorithms available on [GitHub](https://github.com/aws/amazon-sagemaker-examples/tree/main/introduction_to_amazon_algorithms).\n\n\n##### **About the Authors**\n\n\n[![image.png](3)](https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2022/06/28/xinhuang-selfie.jpg)\n\n**Dr. Xin Huang** is an Applied Scientist for [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) JumpStart and [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) built-in algorithms. He focuses on developing scalable machine learning algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. He has published many papers in ACL, ICDM, KDD conferences, and Royal Statistical Society: Series A journal.\n\n[![image.png](4)](https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2022/05/10/Ashish-Khetan.jpg)\n\n**Dr. Ashish Khetan** is a Senior Applied Scientist with [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) JumpStart and [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) built-in algorithms and helps develop machine learning algorithms. He is an active researcher in machine learning and statistical inference and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.\n\n[![image.png](5)](https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2022/05/10/Jo%C3%A3o-Moura.jpg)\n\n**João Moura** is an AI/ML Specialist Solutions Architect at Amazon Web Services. He is mostly focused on NLP use-cases and helping customers optimize Deep Learning model training and deployment. He is also an active proponent of low-code ML solutions and ML-specialized hardware.","render":"<p><a href=\\"https://aws.amazon.com/sagemaker/\\" target=\\"_blank\\">Amazon SageMaker</a> provides a suite of <a href=\\"https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\\" target=\\"_blank\\">built-in algorithms</a>, <a href=\\"https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html#jumpstart-solutions\\" target=\\"_blank\\">pre-trained models</a>, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, image, and text.</p>\\n<p>Starting today, SageMaker provides four new built-in tabular data modeling algorithms: LightGBM, CatBoost, AutoGluon-Tabular, and TabTransformer. You can use these popular, state-of-the-art algorithms for both tabular classification and regression tasks. They’re available through the <a href=\\"https://docs.aws.amazon.com/sagemaker/latest/dg/algos.html\\" target=\\"_blank\\">built-in algorithms</a> on the SageMaker console as well as through the <a href=\\"https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html\\" target=\\"_blank\\">Amazon SageMaker JumpStart</a> UI inside <a href=\\"https://docs.aws.amazon.com/sagemaker/latest/dg/studio.html\\" target=\\"_blank\\">Amazon SageMaker Studio</a>.</p>\\n<p>The following is the list of the four new built-in algorithms, with links to their documentation, example notebooks, and source.</p>\n<p><img src=\\"data:image/png;base64,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\\" alt=\\"微信图片_20220830130411.png\\" rel=\\"1\\" /></p>\n<p>In the following sections, we provide a brief technical description of each algorithm, and examples of how to train a model via the SageMaker SDK or SageMaker Jumpstart.</p>\n<h4><a id=\\"LightGBM_11\\"></a><strong>LightGBM</strong></h4>\\n<p><a href=\\"https://lightgbm.readthedocs.io/en/latest/\\" target=\\"_blank\\">LightGBM</a> is a popular and efficient open-source implementation of the Gradient Boosting Decision Tree (GBDT) algorithm. GBDT is a supervised learning algorithm that attempts to accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. LightGBM uses additional techniques to significantly improve the efficiency and scalability of conventional GBDT.</p>\\n<h4><a id=\\"CatBoost_17\\"></a><strong>CatBoost</strong></h4>\\n<p><a href=\\"https://catboost.ai/\\" target=\\"_blank\\">CatBoost</a> is a popular and high-performance open-source implementation of the GBDT algorithm. Two critical algorithmic advances are introduced in CatBoost: the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms.</p>\\n<h4><a id=\\"AutoGluonTabular_23\\"></a><strong>AutoGluon-Tabular</strong></h4>\\n<p><a href=\\"https://auto.gluon.ai/stable/index.html\\" target=\\"_blank\\">AutoGluon-Tabular</a> is an open-source AutoML project developed and maintained by Amazon that performs advanced data processing, deep learning, and multi-layer stack ensembling. It automatically recognizes the data type in each column for robust data preprocessing, including special handling of text fields. AutoGluon fits various models ranging from off-the-shelf boosted trees to customized neural network models. These models are ensembled in a novel way: models are stacked in multiple layers and trained in a layer-wise manner that guarantees raw data can be translated into high-quality predictions within a given time constraint. Over-fitting is mitigated throughout this process by splitting the data in various ways with careful tracking of out-of-fold examples. AutoGluon is optimized for performance, and its out-of-the-box usage has achieved several top-3 and top-10 positions in data science competitions.</p>\\n<h4><a id=\\"TabTransformer_29\\"></a><strong>TabTransformer</strong></h4>\\n<p><a href=\\"https://www.amazon.science/blog/bringing-the-power-of-deep-learning-to-data-in-tables\\" target=\\"_blank\\">TabTransformer</a> is a novel deep tabular data modelling architecture for supervised learning. The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embeddings of categorical features into robust contextual embeddings to achieve higher prediction accuracy. Furthermore, the contextual embeddings learned from TabTransformer are highly robust against both missing and noisy data features, and provide better interpretability. This model is the product of recent <a href=\\"https://www.amazon.science/\\" target=\\"_blank\\">Amazon Science</a> research (<a href=\\"https://arxiv.org/pdf/2012.06678.pdf\\" target=\\"_blank\\">paper</a> and official <a href=\\"https://www.amazon.science/blog/bringing-the-power-of-deep-learning-to-data-in-tables\\" target=\\"_blank\\">blog post</a> here) and has been widely adopted by the ML community, with various third-party implementations (<a href=\\"https://keras.io/examples/structured_data/tabtransformer/\\" target=\\"_blank\\">Keras</a>, <a href=\\"https://github.com/awslabs/autogluon/tree/master/tabular/src/autogluon/tabular/models/tab_transformer\\" target=\\"_blank\\">AutoGluon</a>,) and social media features such as <a href=\\"https://twitter.com/fchollet/status/1484212136846921730\\" target=\\"_blank\\">tweets</a>, <a href=\\"https://towardsdatascience.com/pytorch-widedeep-deep-learning-for-tabular-data-9cd1c48eb40d\\" target=\\"_blank\\">towardsdatascience</a>, medium, and <a href=\\"https://www.kaggle.com/code/usharengaraju/tensorflow-tabtransformer/notebook\\" target=\\"_blank\\">Kaggle</a>.</p>\\n<h4><a id=\\"Benefits_of_SageMaker_builtin_algorithms_35\\"></a><strong>Benefits of SageMaker built-in algorithms</strong></h4>\\n<p>When selecting an algorithm for your particular type of problem and data, using a SageMaker built-in algorithm is the easiest option, because doing so comes with the following major benefits:</p>\n<ul>\\n<li>The built-in algorithms require no coding to start running experiments. The only inputs you need to provide are the data, hyperparameters, and compute resources. This allows you to run experiments more quickly, with less overhead for tracking results and code changes.</li>\n<li>The built-in algorithms come with parallelization across multiple compute instances and GPU support right out of the box for all applicable algorithms (some algorithms may not be included due to inherent limitations). If you have a lot of data with which to train your model, most built-in algorithms can easily scale to meet the demand. Even if you already have a pre-trained model, it may still be easier to use its corollary in SageMaker and input the hyperparameters you already know rather than port it over and write a training script yourself.</li>\n<li>You are the owner of the resulting model artifacts. You can take that model and deploy it on SageMaker for several different inference patterns (check out all the <a href=\\"https://docs.aws.amazon.com/sagemaker/latest/dg/inference-cost-optimization.html\\" target=\\"_blank\\">available deployment types</a>) and easy endpoint scaling and management, or you can deploy it wherever else you need it.</li>\\n</ul>\n<p>Let’s now see how to train one of these built-in algorithms.</p>\n<h4><a id=\\"Train_a_builtin_algorithm_using_the_SageMaker_SDK_47\\"></a><strong>Train a built-in algorithm using the SageMaker SDK</strong></h4>\\n<p>To train a selected model, we need to get that model’s URI, as well as that of the training script and the container image used for training. Thankfully, these three inputs depend solely on the model name, version (for a list of the available models, see <a href=\\"https://sagemaker.readthedocs.io/en/stable/doc_utils/jumpstart.html\\" target=\\"_blank\\">JumpStart Available Model Table</a>), and the type of instance you want to train on. This is demonstrated in the following code snippet:</p>\\n<pre><code class=\\"lang-\\">from sagemaker import image_uris, model_uris, script_uris\\n\\ntrain_model_id, train_model_version, train_scope = &quot;lightgbm-classification-model&quot;, &quot;*&quot;, &quot;training&quot;\\ntraining_instance_type = &quot;ml.m5.xlarge&quot;\\n\\n# Retrieve the docker image\\ntrain_image_uri = image_uris.retrieve(\\n region=None,\\n framework=None,\\n model_id=train_model_id,\\n model_version=train_model_version,\\n image_scope=train_scope,\\n instance_type=training_instance_type\\n)\\n# Retrieve the training script\\ntrain_source_uri = script_uris.retrieve(\\n model_id=train_model_id, model_version=train_model_version, script_scope=train_scope\\n)\\n# Retrieve the model artifact; in the tabular case, the model is not pre-trained \\ntrain_model_uri = model_uris.retrieve(\\n model_id=train_model_id, model_version=train_model_version, model_scope=train_scope\\n)\\n</code></pre>\\n<p>The <code>train_model_id</code> changes to <code>lightgbm-regression-model</code> if we’re dealing with a regression problem. The IDs for all the other models introduced in this post are listed in the following table.</p>\\n<p><img 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alt=\\"微信图片_20220830131105.png\\" rel=\\"2\\" /></p>\n<p>We then define where our input is on <a href=\\"http://aws.amazon.com/s3\\" target=\\"_blank\\">Amazon Simple Storage Service</a> ([Amazon S3](https://aws.amazon.com/cn/s3/?trk=cndc-detail)). We’re using a public sample dataset for this example. We also define where we want our output to go, and retrieve the default list of hyperparameters needed to train the selected model. You can change their value to your liking.</p>\\n<pre><code class=\\"lang-\\">import sagemaker\\nfrom sagemaker import hyperparameters\\n\\nsess = sagemaker.Session()\\nregion = sess.boto_session.region_name\\n\\n# URI of sample training dataset\\ntraining_dataset_s3_path = f&quot;s3:///jumpstart-cache-prod-{region}/training-datasets/tabular_multiclass/&quot;\\n\\n# URI for output artifacts \\noutput_bucket = sess.default_bucket()\\ns3_output_location = f&quot;s3://{output_bucket}/jumpstart-example-tabular-training/output&quot;\\n\\n# Retrieve the default hyper-parameters for training\\nhyperparameters = hyperparameters.retrieve_default(\\n model_id=train_model_id, model_version=train_model_version\\n)\\n\\n# [Optional] Override default hyperparameters with custom values\\nhyperparameters[\\n &quot;num_boost_round&quot;\\n] = &quot;500&quot; # The same hyperparameter is named as &quot;iterations&quot; for CatBoost\\n</code></pre>\\n<p>Finally, we instantiate a SageMaker <code>Estimator</code> with all the retrieved inputs and launch the training job with <code>.fit</code>, passing it our training dataset URI. The <code>entry_point</code> script provided is named <code>transfer_learning.py</code> (the same for other tasks and algorithms), and the input data channel passed to <code>.fit</code> must be named <code>training</code>.</p>\\n<pre><code class=\\"lang-\\">from sagemaker.estimator import Estimator\\nfrom sagemaker.utils import name_from_base\\n\\n# Unique training job name\\ntraining_job_name = name_from_base(f&quot;built-in-example-{model_id}&quot;)\\n\\n# Create SageMaker Estimator instance\\ntc_estimator = Estimator(\\n role=aws_role,\\n image_uri=train_image_uri,\\n source_dir=train_source_uri,\\n model_uri=train_model_uri,\\n entry_point=&quot;transfer_learning.py&quot;,\\n instance_count=1,\\n instance_type=training_instance_type,\\n max_run=360000,\\n hyperparameters=hyperparameters,\\n output_path=s3_output_location,\\n)\\n\\n# Launch a SageMaker Training job by passing s3 path of the training data\\ntc_estimator.fit({&quot;training&quot;: training_dataset_s3_path}, logs=True)\\n</code></pre>\\n<p>Note that you can train built-in algorithms with <a href=\\"https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html\\" target=\\"_blank\\">SageMaker automatic model tuning</a> to select the optimal hyperparameters and further improve model performance.</p>\\n<h4><a id=\\"Train_a_builtin_algorithm_using_SageMaker_JumpStart_138\\"></a><strong>Train a built-in algorithm using SageMaker JumpStart</strong></h4>\\n<p>You can also train any these built-in algorithms with a few clicks via the SageMaker JumpStart UI. JumpStart is a SageMaker feature that allows you to train and deploy built-in algorithms and pre-trained models from various ML frameworks and model hubs through a graphical interface. It also allows you to deploy fully fledged ML solutions that string together ML models and various other AWS services to solve a targeted use case.</p>\n<p>For more information, refer to <a href=\\"https://aws.amazon.com/blogs/machine-learning/run-text-classification-with-amazon-sagemaker-jumpstart-using-tensorflow-hub-and-huggingface-models/\\" target=\\"_blank\\">Run text classification with Amazon SageMaker JumpStart using TensorFlow Hub and Hugging Face models</a>.</p>\\n<h4><a id=\\"Conclusion_146\\"></a><strong>Conclusion</strong></h4>\\n<p>In this post, we announced the launch of four powerful new built-in algorithms for ML on tabular datasets now available on SageMaker. We provided a technical description of what these algorithms are, as well as an example training job for LightGBM using the SageMaker SDK.</p>\n<p>Bring your own dataset and try these new algorithms on SageMaker, and check out the sample notebooks to use built-in algorithms available on <a href=\\"https://github.com/aws/amazon-sagemaker-examples/tree/main/introduction_to_amazon_algorithms\\" target=\\"_blank\\">GitHub</a>.</p>\\n<h5><a id=\\"About_the_Authors_154\\"></a><strong>About the Authors</strong></h5>\\n<p><a href=\\"https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2022/06/28/xinhuang-selfie.jpg\\" target=\\"_blank\\"><img src=\\"data:image/png;base64,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\\" alt=\\"image.png\\" rel=\\"3\\" /></a></p>\\n<p><strong>Dr. Xin Huang</strong> is an Applied Scientist for [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) JumpStart and [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) built-in algorithms. He focuses on developing scalable machine learning algorithms. His research interests are in the area of natural language processing, explainable deep learning on tabular data, and robust analysis of non-parametric space-time clustering. He has published many papers in ACL, ICDM, KDD conferences, and Royal Statistical Society: Series A journal.</p>\\n<p><a href=\\"https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2022/05/10/Ashish-Khetan.jpg\\" target=\\"_blank\\"><img src=\\"data:image/png;base64,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\\" alt=\\"image.png\\" rel=\\"4\\" /></a></p>\\n<p><strong>Dr. Ashish Khetan</strong> is a Senior Applied Scientist with [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) JumpStart and [Amazon SageMaker](https://aws.amazon.com/cn/sagemaker/?trk=cndc-detail) built-in algorithms and helps develop machine learning algorithms. He is an active researcher in machine learning and statistical inference and has published many papers in NeurIPS, ICML, ICLR, JMLR, ACL, and EMNLP conferences.</p>\\n<p><a href=\\"https://d2908q01vomqb2.cloudfront.net/f1f836cb4ea6efb2a0b1b99f41ad8b103eff4b59/2022/05/10/Jo%C3%A3o-Moura.jpg\\" target=\\"_blank\\"><img 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28OjoFj4Y6uHhkU7eHdjE2wc6eFfQvvW81r+KV7pqJK/uytplXF+1lPOrchiuTKAzyY+2GH9WxoQyf5or+oY6jDMxltTW0+pJqCtB8u9+meWvBoj4AA0tTQyMdNHR0cHC0Ig0f28OVaZydWU6Z6oTOVaZyNXmHO5vXM6bPTU86K3jre1NvL1nHe8NtEuAPDq+m68vHeG7Gyekzt/jM4d4/8Q+Pji5n0enDvHhyT18cGI37x/bxsOjW3n/aB/vDfXy3mA37x7azDsDm3hrfzvvCHW1fz33t67iTvcTQC6vWyZJyOkVGRwui6UzcQ5rF3rRFLOAyNnTMTbWwXCckSQhgulyIEQpb3QvEwxxrxcGRC7C4mZCIgQQ4oVFKQyiGPnT0tDEeYIFJQsDGCiO43x1HOeqEzhWmfwEkM1lkst7v7uGB9saeWv3Wol57wojfGQbn50Z4K9XR/jq8jE+Pj3A+8cPSPTBiYN8dOogH57Yz/uju/ng2K4ndHQHD4e28e7hft4/0i2B8t6hTZLL+9q2Jm53VXO1vYTzq/O4vCKX4zVLGCiJoitpNqtDptOwcC4Jcz2wMhfJFAb/GyC/Fhi/GiByj0RbV4dx4wylEISHw0RqYxZwID+ck0VhnC6P4VR9OlfX5HK3vZhXNpVxr7taAuSNnWt4Y89a3j7QzvtHd/LJqQN8ef4wn50/widnB/n4zGGJPjo9yOPTQ3x08jCPTgzy6MQAj08e4uMTB/h4dD8fjezjw2EhLZ08HNwsSd3rO1Zzp6dWAuTMqmzOVWcwXJ7IgaJFbEn2ZU3QNGqCZpHsOw1HKyMJENGw5A1PgCEam6CXLR2/OiA6eroYGxtJgHhNsqchPoj9uaGM5M7neEkEF5pyudNWJAFyp6OU+721vL17jdSbfmv/ej4c6ubxyf18evogn54+hFBXn50b5qsrx/ny8iifXRjh8ZkRHp869oROH+XzMyN8fuYoX5w5yuenh3k43COpLiEh7w608/ruFu71reBaRymnm3M4Vb6Ew6Vx7C9cyNZUP9YGT6UqYBqJs91wsRknqSxdfT0pVUkOwlhb+TKBeWGVJV5SLsLiReUtSRwX+VY6uvro62niM9mW5aE+bE8NYyQ/mmPF0ZxZk8GFdWXc3lTNg85KXttczBu9pby/t5FHw208OtYptfLHJw/y1YVhvr18nG8unpDo20un+ebiKb46e4JPTh7l4zNH+eTCMR6fP8pnF47y+fmjknoT4RlB7x7dxbtDW3lzfzt3t1Rxoz2XC03xnC6N4VhJJEPl0XQl+UgqS4yVJPhMZrLdOPT09KThA2VlZZSUlNDQ0EBsi4QMRcWXb9hfCiDyFiJvOWJfbAujrqtngJGhjgRI4XxPehICOZwZznBhJKea07jWUSa5oPc2FvP2lgoebq/l4d4mPjyykc9ObefLS0clqfjiwjG+vXqSv988z99unOOrS6f49OwIj08f5YMTQoUd5fOLo3xy7iifnDnCl+eP8tW5o3x1WkR9D/FoZBfvD2/l3cFN3N+xkjtdRZxbvZhTFYsZKU9gqDKGvhRf2iNnSYNX0Z5OuNqZ/ACIqqoYyHqSZCcHRxyTf/vLKl8qIEIq5CSkQ+hebR09DA208XSyJMd3Ch2RcziwJEgC5GRTKmeb0rjeVsD97lIpdPLm1nre2dsqhTreH97O47PDfHn5BN9cOy3Rt1dP880VAcZRPjx+iPdOHuLd0QM8OnOEz8+P8Onpw3x6cpCvzwxL9N3pvXx39iCfjezik5GdfDTcz9v72nhlSz3n1wh3N4NjdZkcKothY4wHG8KnUzN/GvHerjhZG0oSIWJxgkTGoxjVFJLxJPvx5duRFwZESIK8dcjBEKVIERU2RORC6WirMdnKiLRZzrRHzOZgajBHi6IYaUjmQd9y7nUXcWtTEbc6y3htRwsfDPXz+PgBPj4xwEcXR/nm1gW+vX2Rzy+d5NOLI5IkCGMuAHl4/hAPzxzi8dkjfH52iC9OHuaLE4N8OLiXe9u6OLmylOG6EvZXLOXwympOtq3m3Oa1XNuygSvdq3llWxt3t7VwvDmXDTEeNAdOpn7BDNL8Z+BorouQAkFi1FNLS0sCQwAijv0uVdbzAFFWUUNTQxlncz0yfVzpivNnMCNMkpDD1bGcbkrmcls+r+1slDp0wn19bWAr17Z3cXHLRo73tDPS3cqR9maOdjRzbks713Zs5u7eHt4Z2s3rJ3by1uhOHg7v4r1D23h7Tx+v9G1msHY5jbFhJM+exqKpzsxxsMZ/igshMz0I8/IgPmAOi4P8qFrkT1fRYo63lnG4OpX2aF+Wz3YlL8CD2ZMs0NTUlKRE5HIJkqsrNTU1yabIG+PLKl9YQoQ0yF/maQmRqyyRkCYkxM16HEULZrIjLVyyIUPLIhioiOLC+gIpAvvOkS0c61jJhuIs6tOTKFgURMysqcx1smaWlSneVsZETHUkO9CLkrC5tOcmcaaziZt7NnB370bu79zI3e5WLrQ0sL+ikIrwQOZZjWO6syX2VoboayuhpakiNQ51FRnGuspMMNbEXUOG93hNchZMY2NODJ1LIqgP8mJ5qBdh7paSVAjmCxJqSxh2YdCF6hLb8m9/WeVLBeTHjLrIODHQ12LWJCvqY+dzMD+OI1kLJS/r2Iok3tjTzun2OuoSF+FtY4GdvjZTLM1wMTVgnIIMU2UZZsoybNRkTDfVxs/WhEBbY7L9ptFfnsWV3kau9zRxp2sN11sbGKoqZlNWMgUhvgS72RA0w55gTyf83CcSMM2JcG83Fs+fSWVyCE2ZkbSkRRPj6cQUUw3mWBlRMGcGzZHzaUmcT4SLoQSCkAqhngQgQmLE9u/ay/opt1dRSUXysnzd7WlJjWC4JIWh7EWcLE/g3NosOrNiiXe3Z7qxHqGeHixbsoSS7EzykmPJTYgkJ3YhBfERFESHsiwyiIIwPzLmTqcoxIuO/ERpPP5EczHnm8o4WVvM9pzFNMUvpChqPhmL/KgKnkV7agT9hWnsr8xnqK6Qc6tKuLm2XAqbDDQtp7Myj5TgObib6OFjpEuOhwsbFgexzN9BUldyCTEyMsLCwkIKBQlAhGF/WZIhv88LS4hMVYaRSP0XfrmapjRca64kw1iMpatqYaJqgJamHqKnvisnhctV6QzVJdNbmU7zkgS8HGyJ8XRjQ0YEp1blc23NUmkU7/W+1dzuXM3dDcWcW5HJgeJY9ixPYGtRLK3pQbTlLKJ3eRLnqpM411TIUGMZeyuXsrNwMRuT59MQPoe6RQH0JAWxNT2CQyXpHKvIZaQimyv1S7nfXMLrq5ZyqTaN0eoU2nOCyQ5xY7adFiFOJqyKCqE5eAGGRlooaekgU1fGdrw2pfO8pGxIkSKkofK/HBo5Q1+0fHFAlGWoKsjQUFdFpqiIgbKM1pQIjjTXEOg1E02ZOsqamkxxsGFnQSo3VuZxoDyGsggvwidakjHXg4158RxvzOf8qiyp5y6Gce/0NHC+vZaRmmx25kbTGjWHloWzaYsPpDt9If35sfTkRHG2ejHX2qu52ruO4+vq2bEsmTWRPlQETKNk/kwaw31YGenHmiXhrM2MZdOyxfQsTWZLXhybkoPZlh7O9oJo2vMWUp00j2RfJ9K8J9EUFUTLohAsx+ujpKaOTE2JCToy0twccTHWl+ac/C4BEV6UyF6XqSpIeVeBEy0Yzozjcv0yUv08UBYzmbTUsLY0YkNKBJfrczhUFU9Dgh9ZM1wlxu4pjOdEXRrnGjM4XBHHrtJ4dtRksqU6i5WJERTOm0Wl3ywa/L1Z4TudpgBPNsYEsCcvnjP1S7jb18T7o3u5vWsTO4qSaFzoSVXQdMpDvFgR5UeShz0BrhPwm2ZP2DwPptqbMd1mHCHTHcicYU+h/zTKFvpQEeXL8nBvyoJn0RQVQHdaNFMcrKSUVuEp2qnIiLEZj6uJkfStGjKF35/KUlbRQFVTQ8ryE2milfN9GPCfTt9cF3I8bSWwFA3VsbIxpmVxBOdrMhmuTaa/OI4tmQn0p4UxWpPBQGk8bUl+rIj1ITNgCqEznYgO9GKeuzPTzIyInexEltskiqY6sz7Mj66EIA4UJXGmIZ3Xdq2TMlHeG93DvvI01sb60Jroz9rFoeR4OLMiPoySpEU4WRmjr6+OhoYCznYT8PVwY4qmjDlm2sRNtqHQfwZN8fNZHevPqghf+vJimDfNRVLHTsYGzDfWIcluAlPGm/yQ9vqiKmrs9S+sshSUNZApKyLTEJ0lGQ1BPlyImMOpKG9WLpiCroYSMh0lJliPY31aDGdqsjhcHc/+ysUcLc9jf2EiG5OCyPdyJHayBYumTmSGrSkWhjpYmZtKGeoz7CdQlRBNwWxPCjwmsSMvgb3FSQxUpXGxOYcHuzbw3Z3zfH5hiOEVuXQvCaA/O5zu7EiWz5rMQFUhpze3khkSiKedNT6uzmRGhpMaFoSPiSa+ptpETDQl19OJ1dHzWB8fQFOUD9uWxhAyyx1tJRmhLg6UebqRO9UFx3GGT1KbFF5+qukLAyJTfjLoL+bxCVvSFOnPzfQwLqUEcrR0MW5WpiipyjA1UqctLZ5z1VkMVsUxWLuY0coc+tLDpVBFkos5XoYqTDXUJM7Ph8XhIbhaW+MzyZrMUD/2NlZKtqkiYAb9OZEcKEvibGuhlKDwxu42/uX2ef56eZSzq4vYlRnMruxw+rIWMbBsCT3JEewqyKA7K42V0REsD/ZnfUYSrZlJdOfE05oYyuroANbFCjUVSldqMOsTfNmWH8FCn6kYaMhI9XSjOzSAXA83jNRVEXMf1VR/hypLpqKCmpgBJbwqmYz6+AVcKoxhNCWQ1zobyQ6ah6GWAha6qmzJSeVSfR7D1QkcWbGYwyWL6U2aw5aUQNbH+dGwyJfmxHA68lLZvCyLjQUZtKdG0Ry9gLaEENZH+dOVtIAjZYs5tSKL2x1lvNpezju7NvLPV0/z3aVRrq8r51BOGIP5ERxYGs/oijzJy+pNCKU3Lpwt8eH0xIewMzOSXXnRkhfWmRDA1vRw9hTEsndZjCRdG5P9ERQT4ImRtoxEZys6A7xIcXeUvlVRURlltd+jhIgEAJkMfQVlCZDI6RM5XZ7E+ZJ4zjaUsCEnDUsdVUxVZXRnJHOuMlsC5PS6XM6syOVCTTwXapO40pTD9XXLudxSwZmVJZysL+RUXSFHc5LYk7yIroi5bEtYwMmyVO6tL+UtEbJvW86bm2p5b+dG/n7xON+dG+J2ayUjBRGcLI3nbG02I6vzud5RzYnyTLZGBbA9Yh79EbPZEOTK+lAX2oOn0h3lxcG8SEaqUxksS2Tn0gh604JYH+NDSvhcDLVlBI3TYLW7E5FOEyR1JTq8QiuMtQEvuv/CKktyd2WKKEmp/IpY6WnSU5whJTEfzw1i/0JfglzN0NSVUZ8dz4UVedxrzuZ6Uy43VpVxZW0RtzaUcb4xj0NFcezLXSSpnG0pAfQl+dGVGEh/chiH8pZwd91K3t3Sxlv9Lbyzdy2v7WngwY4K3t3Xxl/PHefvl85wf1s7p1bmcq4ui+sNSzm7vIDbTVW8sqaW6w2lnC7P4mB2NNtSgtiTuVDqqF5oWMqNphLO12QzsjyFgcJYejIWsCZ6Jpnz5zFRW8YCXRmtvtPxtzaRPCyhqlWVNKWxINEx/qU0FsAXBkRZSQHh+ooXFDNcdWQyGhcv5J2+Rg4kebMvzJuyiNloa8ik3rMA5EZDupQYd7WpjEtrirjVXsnVdaUcr85goDCevdkR7M5cyL6cSIZK07m1tpZ3e9p4f8tmXt+0ltd710ipQu8cWMu7u1fx8aE+/u3KRf7j1nU+PNjPxaZlXKzL5k5zCTcbK7jXUsera+slUF5treNBWwOvtq3gfnsDtzfU8GDzSu6313GlYRmjZUs4UBhHb0YIa+K8yAn2Z7KRjEgzZdb5euBrafwEECVV1FR+h4CIaWciuUEAIoARdiQnYCbv727laG4wg+FzGKjOw9POmNzAWZyqFj3ldB50VmoTR/UAACAASURBVHOttZobbdXc727m9S0tvNbdzP2uJu5tXMX9zc283beed7dt4v3tnXzY38Wjrb18vL2XT/b18dGBzdztXckn+zv5ZP9O/unEOf7j0jW+GNzJ9TUlnFy+mJMlKdxpruStjlV81NfOe13rebChmTc3reeDvm4e9nbydl8L7/Sv5fVNK7nSWMRoeToHCxPZkh1Ga9Jc0gJ98LHWJn+KBW3Bc5ltM15SVWK1CGFHfqlkyK976RKiovxknp5Y0kJBRVXy2WNnOHO7s55bTbkcXDSHMyuKyV/gTY6fOwOFiVxtyOJ2Wzk3e1dyt6uZB/2tvLGtnXd2beajfX18uKePD3b28vHe7RKzP969jc/27uCz3dt5a/MG7rQ28OrmZmlG1FcHd/Dp7j385fAJ/vuJc3x5YDv3O2q4UJ3NUH4i+9MjOVGSxhsdTXyyvZt3utp5u3MTH/Rv5eGWPt7qX8cbPWt4ZUM9FxqWMbw8jb1L49icHkJLwlwS/Waw0G08zSGz2LAokOlWplJWjZg/IpgpZ+wvLV86IMpKAhDRU1dHpqKOhoKMQCdLLrZWcmVlDoMp8xksSKQsaCbpXk5sz4vmUn0Ot9rLpalm7+zr5IND/Xx0aBufDu3h62OH+GJogMcD+/l08CBfHRnkq8MHJUDut67ibMUyLjeU80Z3K4/29PDxni083rmTvw8e57sjw3zUv4m3ulbxWkc9F+tLOFuZw66UcA7nLea19tWS2nuvt1sC5NG27TzctYk3t7Rya309p+uWcqh4Cf3ZEaxfHMiqmNlE+UwmxcuBvpRFrAoLwM5EX5rMoyLmTmr+DgERcz/UNFRFgpcUXBQel7upDufWVzNYGMvFxiyGS5Kl2FLGHEe2FcZxoiJNmjBzub+B+ztaeW+gh0dD23k8tIdPjx7gs6FDfHr4EF8dG+avJ0YkSbnWVMPetBhGC9P4ZMdGvj64ldc2rubhznYe7drCNwcO8uXuHbzVtZrXO1dyp2MFJ2rLeGtTEzuSw9kUEcDZ6iIebGzhfnsLr2/awDtbOvnwwBbe7G/j+rp6jlcXsLcgWYqViWjvmuRAAqdYkOHjxIHcJMqDfDEz0kFJSUWanCoCq79UMuTXvXQJUVSUSTNspRV71DRQkskYryhjX3WuZFyv9ZZxo72MnuxFpPs5srEggqOlS3hlYx3nt9Rxf9c6HorM9KPb+GBwG4+O7OHx0AEeHznEJ0ODvL19CzdbV3G6eimH8+M5UZLC3TXlXKorZH9WAsfqU7neWvmE0W2NXG8u5VhVmpQ0vSIsiM2RgTTOnU5b2FyOlmRys6WKe8Kgb17Fg65m3tyxiTub13ChuZLhijwpCtCREsa65CBp3uMcl3FkzHJgT3o8S2a6oaGpLE0KVZcpSLZEzthfWr50QDRUZU+mEIt4lmg5KkoYyGSsSQrh22PbGG7P4cGOlVxsq2DxnIk0JM1jqDSVqy3Ludi/gtvbm3n9QBtv7t/Ig92beDiwjcfDB3l0+ADvH9jL3c3rOVpRwLn6Al5dL4DIpCvSm2XOZuQ4mJDraUBb4hwOFCQxuCyJXZmhVM9zJny8LnN0dUmxMKDKcxJd0QvYkxnD0dJUrq0t49XNddxoLeNOVwtXWldwpqGMI2U59GfFsTYukIbIOdRH+zLfYwJZs+zpjQsj0MpE+lYlBVV0FFSQqf8OJURVWx2ZkngxBRTERH8NVUmcLQ316dnQygdi7mBbJdeqs6kLcCMz0InNNakcaynnzrqVvLKpnvs9q3i1t4V3dnfyxvaNvLm9g0f7unll4ypprGNndiQjlRnca6/jbvsK9hUksiJkFhuXhNMUGkDhzCmUzJnBqsgF5HpPYYGFHkE2RkROsiTWfTwNoV4M5iYyXJDEnpwYTjYWcL+nkRstpVxfXcOZuhJGa5axfWkSa5IWsCp5PlXxc8le4EaigxVNidGETp0sOSzSSkVSmtP3uVnP6YOMlYDn7b9wP0T0VgUgquoq0oIAGiqKUs9dLAAwc7KLNK7xcOtabqwqpdJ/MvnBU+itT2ewYSnnGkq4sq6cm5vquLm5gde2tvJgaxv3etdJAF1fX8dAQTy7MhZxpjpX8oTubGjkeH0J65MWUh7kTW1kqETFQX4s8ZnGosl2hE2xI3yqA14TDAlzs6AyZDYDhRmcqFrG7qWJ7ChOkMZfLq8p5VJTOaNV+ewvTmVT6kJqF/lQFT2b8pjZ5Aa7E25nQdhke2z1NVCRfa+epWRrBRS/X+vrp9TV8wAYW//igIj1rZSfGHaxmo8w8pqqSj+AMs/GgH1VBTw+tJ0teQlkznVmTW44uypSGCxJYXTVUi62lnOto4473au43dXIK71ruNJWx2B1PmuCZnBoaSJ319dxv7OFy62r2FqYS8bsGcww1sHTyox5kx1YMM2F0Jnu+M9wwcPJCpcJ4xivp4KjoZoE0sqoMPryU+nMS6A1exF9RXGcal7G6foihity2J6fIIXdlwd7UhE1h8r4eWQHT2OWtTHGInAqk6EkVqGQ1t0SuVkyxBz8nwJD1I1l+PP2XxgQ+QtKGSfKihIgYq0Sfe0nBl50FH1sLaVgYUdeChl+k6lO8GV3ZQrDNekM1GZzbFUB54V92FDOmdXF3OpZxbHGIppjA6kP82B/eRoHq3LpW5bK+uzF0rSG6dZm2Bvr4mRpgp2ZIRaGWjhOMMHR2hRDXRWJxGifiYYSbuP0CXOyI23ODIqj5lGRFMjq9FDp2cMVWRwqS6crc5EkHUXBHlTFz6M8wZ8kv0nYTTCQGpdQVUrqYrqAGIhTQEQofp+AKImV2p60FJEMIFqOIA1NFVRUFVDXfDLebqGqxDQTHbytdVm6aBa9pfHsLklke2U6+2oyOdqYx/HGbEYasrm0sZpN2TGkznSgtTCWtUtjyQvzlLIJw6Y7Ms3aGAczPezMDXAyM8B5vCH6SjI0ZTK0ZTLGqckw11HBVEcZG11NHHU0cNXWYKqRFnNdLQnzsic1wI0N2VEMVqSypzSZdcnzqQyfRXHYTIqj51AQOZuFM20xNdeT1mcRS4IoimFqBUVpiUCxzpfog/3uJET45MrKqpJvLhdHYeDF2ibSUhoiPK+mhtb3DHM0USMvajbthQlsK02mrzqTXTUZHKxNZbQxg7Nr8znVWkJxsAeLJlsQFzIVP3czvO0NCJxkjo+VIbMsDfBzsmCKmQ4+4w2JcHMk1NmWedZmLLCfQLiLHeHOE/E1H4ef/QT8bSyYN8GUGabaTLbUxnG8Ou5m6izxmcKBysXsKI6nKWGuZDuKI7zJDZtFRugsFs6yR2+cjqSSxdIgikpqKCiqSiEioZ4lFfZ7M+rS6p6qIu9VTZIUAYqKpuoPK4GK3ruUJCDsi4IMuwl6LJwzmcKIubTnxLOpPJWt1akcqFnMaGMqZ9fmMtSYTcIMawIcjHGbqI+zqQqBTuPJ9/egLMCLFaFzaUsIpylsHi1hQWyKi6IjJoK2yHDWhMynwW8OTYHzaJ7vT/6cGeR6TyXdw1XK6XK31sPRXBNrbRneFka0JvuxOTuElfFzKIucSX64J+lB01kS7EGwhx3qmk8WyxGhEhUldZQURcbi94trqvweJURRqCllVFXVJVEWLUmsACp5X6qK0kIuAhCxXKs4pqUjw8vdhsxgHyqj5tOUG0VHUSy7q+IZqk/gcE0COysSCZ00Do/x6ky1McDb1pgo14mUz5tFW/h8OgLn0Dbbg81+M2md68WGeT40e01n5XQ3aiY5UG5vQ52LI7WTHMh2tyXN2ZJFEwxwUpHhaKyCh6sl7hOM8LY0IW3aOBpjZlEV5Un+gsmk+E0iKcCNxPkzmO1iLjUiaSUKBTU0VbRRkqlJoZMn3/d7BETlp5efEPZEJF4rq2tJwUfxcc5W48iKnEv+oplULFlI3eIQthQtpjc3moHqLHoLEwl2MsTHWpM4V1OiXC2YqqvMFAMton08yIoKojgzmqbabGkZpyOtJWypTmZdUTidNcl0rcimoyaPuuwkEhf5Ezh1Ik6qMnw0ZZTMdqEjNZyKSG/m2WqT4mZCUbAnSyPnET/PlfAZFizytmXeLGcsJ5g9SRcVKaPPoN+fDXkOIFKGn5qqFJ4XCx4L393SUF1qgWWJ8yiI9qM0di5rUkNYlxzAzpJEtpYmkzLbngBnA2pDPSV3uS5sHlF2VsRPcqQsJADhsW2tzGNHRw3HD7axp7ea7V3LGdi5go61S8lNDWGupz2JUyeROs2ZvKmOrA72Zm9uPIerMujMiyI3YDIl8yaRG+BOkv80EvynEOvrIAEyzckCPV3NZwIhB+gPB4iwG8IlFqu1CUCEZ6KrLJP08/IEP4oS/KhPC2FdVhhNCb6sTZ5LR04YFdFexHhYsmtprLS2lQj+7ctNZX3ofBq8PGn09qDJewaVM71ZFxpChYc7y1wnUj3TjeVTnSh1dWb5ZFfa5wfQNs+XrhA/hsWIZW0ep1fks3t5MrWR3lSEziDFZxLhnk5E+k4i3s+Z6DlOOFs9ySyRM/5Z5R8OELF4mUjCFouCyQERfZPZzqYsCXBl+eIAmvMjWL90EWszAlkRN4s1S/xozQ6lOHw6x1blcqW9kusbqrjVUsWlqkJOZKVyLieDG0vzOVVYzbXqVRzNLOBIaibXy8u5VV7GtcJCiXZHhtO7YC7bIgOkAavzDTmcWJHNrtJE6qO8WC7caS9nQmc6s2iOCwtnTSTIwwYLAzWpT/UsIOTH/3CAiKxGaQ6JmiZizSwxwqil8ASQsKnjKU6cR31WCKtyguksiWFT4SJWLfZj1eJ51Mf5MrxuKRd7qrnaWc31tnKuNZVwpWopl0vzOZWVxr3mDXzUtZ2HG3p4a007D1vauFddy+mMDIbiYulYOI9NkfPYnRnBmRXZnFyVw0BNKttKE2hM8pf6HbHeLkTMnUaknztzJ5vgOdEAHSUZyorPth1/WEBEB0q0IiUBiKLSDyprwXRb4mfbE+c3mayFnlQkz6O7MokddWmsiJ9LadB0mhMWMLAmn7M9VVzpq+FGbw1X2ss5VZ/PiYo8jhZlcyotm9ul1dwrq+dKbhmnF+cwGBHHvuBQ9gUHS6vFdaQFS/m7oyuyObG6gIEVWXQWxVEcOYslvpOI8HZmkf8MAmc6Mt1GW+o8GmsqoyZc9mcYc/nxP5yECECklQ/EvxYoKj1ZNFkmY6G3M7VpwXhNHMe8SaZkBE+jPiOYlpxFNC8JpSl+AW1LIjlQl81ISyGnWou40lXJ5U2VDNZksrc0g0NlS9lRlkLrknCqQudSHDBHorJQf6ojAqiKmsuqhAA6Mxexe1k8x2uypUS6wyvyaM5cSNxsJyJnWBHs4UCA12Tc7YyZMkGL5KBZOFmaoSAWMPtHA0RaRlxMnRaAKIukOkUpESJitgv9K/PwdTJnmoUOMbMnkTp/CpkBbrTnxdFfmEFzVCh9mdEcrcvhxKo8zq0v4np3LafXlrGnLIvOzGTyIiYTOdOMEPfxBLnbMmeSBXNcLJnvOZFQXwcao+axvzSN8/WFXKpbymhZFr25sVTE+BPp5UictyP+7rZ4ulpjb6GNh50BSxNDmWwr8q/+AQFRUxHDu8LTUniSCS970vMNnGrNobolrMyJYLK5Km42+sz1dCR0zmSpb9KcFkZthBdFMbPZsDSGY+tLuLipgvPriqXEt6ttVQyWZ1G5MIBAW1Mmm2hJK1s7mRsy1dpUitIGOljQlhzHvjIRAcjh+OoMthTGsjwigIiZLng5mjHF1QzvGQ44GmvhZ21IyYIZVMbOw9ZUS3pvISHCdZfPohJT2aSJOkpKT2bkPid08jyVJu77NL1wtFcev3pWqShTQFkkXCvI0NHSRVFB5G/JcDVTY9fyWLatzGf54gW4WmjhZm9MqK87yUFelMTMJ9nTQWr5Ir60szaDy5013Oiq5/qmGi6tr+J083K6lqYTPc0RV2NNptiY4jjeAHdLY3wmmhHj4cLaqBBGRC5YZynDjam0pIWS6O2Op62ZNO9xyiRjbM01sTdUJ97TmZoIHwkQMWVBxOLEbFv5lGixLUiuxiSQ/miAKCk8GS9RENkpSqooK4l/wJGxyGcS24oi6atOpbM6nZRQT+zN1JlkbURkgDcJ832Y4zCBmdY6RMywoTYpkMOrlnJ322ru71zLrd4mbnQ3sTY9jkh3e9yMtfC0t2SqrbkEiJeNCemBXvSkxnK5rYI7/VXsqk6keOFsAic74jLBFEdrYybZ6mJtpIKPsyWF4XOoCJtJ6UIvQnzc0DPQlqTjSRRbUZpbKEAQUiJKAczzJOB59U9Lh9j+1SVEuI5ylaUoE+KvwtRpk9lYv5TNWQvoKYljS9USehvzJbfTTFcRd8cJzJnmxlQHO/wmW0m2YYmfm2TsBxryuNhZz+WuRs6211MdF0KspwtzHS0JdHdmhvhfERM9PKyMyAyazUBZDmfWlnB0TTZNaYHEeLni7eSAy0RrrCzGYa4lY4bduCdSGelLgd8kSsI8KEwOZ2HofAkQobIECILEtlxCxPbzGP68+t8EEJG/JMRfQ00TQwNTOjauZ8vqMhqjPaSJO1srF9NXn0V7dSbBPi4Y6ygyaaIlU12dmeFgia+rNdGznEnzc6NsoTddhUkcbCxkZ00uOfNnsXCKHZEersTM9sTd3BhXU328JpoS7ulMT3YyvUsTaEoLINHPiVkOFjhbWmJpOg59HWWcjWRE+U4hPzqQ/BAPlvo7syxoCsWJodSWLpMmecpBEOucyBcNkIPzPIY/r/6/HBBVZRUUVMVfGInhXUWSElO5cfMKdXnxrIrxpLsgkp7CaPoqk+lryKapMIH5s5ywMFBnooUxDuYmeDpas3CmK1EejsR6TKRkkQ9N6QupigskI9CTYFdrknw9yJg/j5kTxuPnPJHgGc54OZiS4zuTovA5LAl0w3eKhfQXFuMN9NDXUMJYT4kIT1uyF84hM8yHnAVTqVw4nfwAZ+J8XKkvzsff3/8HKRFTogUDhVEXIEmq7I9mQyQvS1Gk7cvQVNdi754BTpw8xrLEIFoX+9KzNJqu/Aj21KfSVRpLV80SWkoXM9vVEks9VRwmWDDVwZYAd2dCp9uzaKoNiV6OpMxxIXK6DaWxC1g8dzqp87xY4jebefa2RMyaRuTsqdJKDH621oRNd2bBTDumTzLHZrwRhprqmBuo4+lmToqfCxlBniwOnEZu0DSqI6dTHjaVyOn2VGalUVhYiJgOLZcSISECFCEhksT80QARNkRRTXQOZXjN9ObK5Zt0dnWQHxdIS6IPWwrj6V0WzeaCUHbVJdNXk0TPikxaqzLxdbPCUFsXkVLkZmnMXGcLwtytCJ9iKWWTLJxmJbXkmJkuREybxEJ3V4JcnCTVFTDVHu9J5kw1NcfD1pyZbla4OJozTk8LXVUl3O1MSQjzIH2uK1nBM8kN96EgbDpFC5ypifQkxtOBZfFRdHd34+3t/YNUCCAEIJJBV3yy1N/z1NJP1f9slSVfOkPcVL7moNgWbq5UJ0LrCjLp/wXFchpiW5wnXlg8TKpTVZY6WRs6urh25xa7t2+WFghbnxhCX/Eiuoqi6SxfTG9dHn0rl9JTk8XG4jja8xcROMVVSsExUJPhbG3C7BnOeLtOZJadOQFONvi52UqrNPi4TMB/mj3zpjswa5IlHs7muFobYGc7ETtrK1ztbDDRUpGmS0y3NiB+7hQS5riSuWAWmQFTyQ90ozRsKmWLPCmJ8WWBuy0lqbG0rG2itq6SOb5ekpSIYKlQXUJiBDjCJZav7CC+V6gz+b5cmsYy/el9OVjybsN/2suSXygv5TeQsk6E2H6fHiOOC1EWLytA0dFVl6KmcTGJvP7WQ4ZPjHJiZIComZMomuvOYH0y2yvi2F6bTm9tNpurs+hvWMrWuhy6yhazrjSfMK8pGGvI0FeXYWehj6utKVMmmDB1gqkEjvi7I5H2I9J/pjtb4WhpKA2CTbI1wcHWBhMDXek/SozVZUw0UJNC7Rmh3qTNn0FagAdZgdMoCpkuAVIeNUtKqJhlb0JRWjzNq1dKEl1WXoyBgZ4EhGC0YLx8mQ1hS0T/RIAkSN53eZrxz9oey88XBkRJSMhT+UlyURYvJtkPBRkmhkacPHaKGzfvsvfQQc6fP05m+DzSZrpwZEUyO5ZHsbNmCX3V6bQvT6WrOluSlu6aHLY3lVKbHUP4bFccJ+hiqClDV0Um/Y+USAGaZGOBvYUJtmZGONtaSGRpasAEsyf/W2WkpY6GTIappiIulgYEzXAgPcyb7FAv0udPIyNwBgXBHpSGe7I8fBqV0bNJ8nVhqpUh9cW5tLWvo7yihM2d7SxbVoC9/cQfGC9XXwKcpzuN4ri8QT4LCPnxlw6ICK3/cNPvA3HiYWKoVpC7uxtt61p5/d4DTp06IwFy9doFMmPDmKKvzr6KaPZWxLG1LI4tZcl0lqWyvngJbcsz2Vidx8aKJfQ15tFWm0VOfADujqaSpKgry9BRV0RfXZVx2pqY6etiZqSPqaEeRnraGOpqSblhhioyrPTVpNm8kb7upIX4SGAs8Z9CWuAUckO8KI2YTXHoNMojZlIRO4cFruZ4OU2gs7WJ1vVrENIhyg0bWmltXUt2djZ2dnYSMHLGi4YoSIAj79mLRiln/LPKH3j3/cjrcyVk7AU/qKrvvQvJRmg80aPihaTWIZNJunt1UzPvvfce337xDbev3mTk6Cg79u3i2MmjdG9oYYaFKU1xszhYm8KuyiSpk9hfmcbm5UvYVJHF+rJsOsoS6K3PpLchR/K+ipcsJHrBLKY4WqCvrYCesiL6qsoYqquip6KEjooiemrK6GmqYqCtjpejOcEejqSEeCPUVGbwTEkyUv0nS6ppWbg3lXF+FAa7UxM7m9JIH6aO12CBlzu7tnSzrnU1pcsLaVhZS1NTI7293QwPD7Nr1y6WLVvGuHHjfpAYwXShvp5WXc8CQn58LH9fGBBprd6n1iK0NLegpqqah2+/w1+++prX7r/JlXOXOHP0BMeGj7JnYB+7B/dz/fIF8pKSCHXQkdYZObAik11VKWwvT0aAsrUul47SdDrKE9lYlkxbSRIbipNoX57O6uIlLE0OJSJgOr6T7ZluOx43y3E4mOpiZyzyrvRxszFl9hQHYudMJsHPXbIXKfPcWOI/mZyQGeQFT6dw4SyWhc6kOtaPkrDp1MX7khXojr2ujMSFQezo72VFQw1rWlZJxr2xsYH+/i1s27aNvXv3cvbsWQYHB8nNzcXKykoCQtgXuU0RDVTO+GeVLx2Qpz2KJYtTuH/vVf7+7V/44N33uHf7DhcuXuXW5VtcHD3DxbPn6OrvYdvB3Zw6c5qhfQdZ7Osm/QlYd1E8B+sz2VWWwPbyJHbUpNOzfAmba9Lprs6gpyKNzsIk2vLjWJcfS1N+NPV5ERRGzyd9gReJ86YTMWsSYR6ORHi5ED3bjdRgbxLnTCJmlj2L505CSEX2fHcJiKXhMygM96AwbBbVsb6SdFRFe7NomiX2BkqUFWSzY/tWyajX1FbQ1NxATU0VLS2r6evrY/fu3ezcuZOhoSFu3brFyZMnJVVmYmLyg4T8KoBIg0tPublj3WAhnpGRkZw9fYZ//u6f+PjRR7x65y5vvvaAuzdvcfPWPa6cvcLF0VOcPj7KwPAgazs72LZrNxdOn2e4p5XM+R5URvmwtzqVPeXxEihblyeypTSJjTUZbCxbwqbCRLoKEiTqyItmTfZCVqQHU5Uwn/JYf8riAsgJ9iQjcBo5oTNJDXB/Qv6TSZztIElE0SIvloZ5SkAsC5tB8aKZFC/0pibWl1UpgZKU+Nnp4jpeh3Wr6ti/f7+kqiqrlrN2XbMEyNq1ayQJ2bp1Kzt27JBo3759XL16lUePHnHx4kXy8vJ+CLk8SzLkx/83CRE5U8IuCMYLAy1fLlx+opqahpQEJzL3RDKcpqaIgD7Z9vLykezC119+w6ePP+Pe3Vd/oLu3X+HcmfOcOnGSkaPHODo0zJkzZyT9u6W/j76t/ezeu4eR0ydZt7qOGL8prEiex4H6NPqKYtlank7X8ky6y1PYXJpE5/JkNpUksr4gWhofWZcXSV1yAFUJ/tSnBLFiiQDHn9Ko2QjGF4R6SKGQ1LmTpFIcK4n0keqWhc+UyuXRTybl1MbMlgKPGQtccDSQkZ0YyuD+nXR0dLC8rIjGVfUSMLW11Wza1MGePXskUITqEsAItXX8+HFOnTrFq6++yqeffiqps9LSUkxMzJ4k1n3PM5FUKPinLv4oWfZkSXY5r0UpEz/yVi8MtETfu7FSR1BB6EGxZq3ws8XSdoqMH29BbW09n3/+JR88/JB3336Pm9dv8fprb/D2m+9w5dJVLl+8ItGJ0ePcvH6D4SNDkt49duwYe/fvo7u3h527d3Fw6DAjxw6zqamC9PnuNC4O4GBDNj3FSfSXpUl9EQGEoI6ieFrzoxBgrM2NoDkzjJqkQGqT50tlWYwvYuxEMF8wPT9khgSCAEkwXwAi7IY4pzx2rnRNa1oYLWnBLAubRuAkfTzt9GipL2FocD9NTU0I6ahfUS0B0tBQT2fnJuk7hLoSaqu/v5+DBw8yOjrKyMiIBMzNmzd55513ePvtt7l+/SZFRSUYG5s+AUBZFYXveSrSb+X8F4IgTS8XTBf9CFEKYKQMke974eIEISFSLqv4o3kNLZKTU7hx4xbffvtX7t9/wBsP3kRIw7Ur1yXpuH71BmdPn+PGtZvSMaHK3njwOqPHRujt7ZVa0+CRw/Rv24qQlC07drBv3x4unBxiTUUeSb6TaUoNZltZEv2FMU9C85UpdJcn01UmJCWRtmXRrM4KpTkzhJWpQaxImU91gh9l0T6URnqxPMpbopKIWdJMKDEbqjLOV3JpxbY4f3VWuARsS0owK5MDiZlhhbedNlkxAQwf2Mr+PdspKyujrr6KquoyVjbWSfaju7tTkhDh0QLXTwAAHDxJREFUZQnDLtSafFvYEyEpwthfvnyZK1eucOXKNT777AsuXrxMWloGZmbmEjACFIm+/6cFCQchEMJvFj1r0esUnRthpIUxEtvCWxARWkH+fgGcPnmGv/3l77z/3gfcuXX3BzAEKPfvvcbVy9cYPXZcUlUCpPNnL3DtylVefeUe586clT5AtKoDAwfZd2A/XT3d9O/aRXdfP6Ojxzh/YpiyjAQiPWxpSV8gucNirGRrbTo9FYslULbVZdBfk8bm0gQpJ1ioMGFPBDC1Sf4SCVUmtgVITWkLaE4PYnVGsERrMkNYmx1GS1aotN8Q60fBgmkscDEmys+NTWurOXvyCOtb17B06f9X2Hk+13VdVxyxE8eOM4ntTL45jpXEVuwPccayZEeWVSlRbGIDUQgCRGMBwA5CAEgCBNE78BrwGupDBwgCbCDYO9VsyY4iN9nJ2I7jSf6IlfltcnPgGU/yYc+57d17zl57rb3PuRdkiQFC2UtS7+rqUCQSMgDi8bgxBLlym5qa0tzcnIFCm0qldP36TQvg+/ff0kcf/UQXL17S1q3b9Fn+2UB8+0f4+tOP00KaAwAImP/Lm2yTsD//l19QY32TPv7ZL/TTH//MpAnnv//9D3Tn1l0DAjZcOHfRAAM0cgeyRYtc3b19R9evXjONJaKQKmSLPNIVCGpgZFSxgWGrVBamU9q19VVt+taX1Fy0WuGKbCVrChStzlXgSIb6KnMUI89U5ijy5nb1V+xQ4GCGOko2qXX3BrOWXesfO7ypYLXadq2zdTHWxrpKNqpz7xvi+Ims5+0POzO//Xda+9QTOlCYrtRQWHOzEyovP2yAAEZJ6S4re0OhgKLRPgssB4TEzrzk/Pnz1n/YQWKHKQTf1NSMHjx4W7dv39WVK9f04Ycf6eOPf6mxsQmlp2cYKABj/7oQaYHlDWbUtJ//y8+Jd+Dsf+Fzn9eql1/RjcvX9YuffKz/+Pm/22z73fvv6IfvfWDbD27ftzyxvHTZ2AAIsIR97OH+Nd2/e8+YcuXKFev40MiwovGYBoYG1dndpZ7eoKLJISWGhrV08awmByMq3PyStr3wddUXrVbv4W0GAqD0V+0wcGixoZpiWweLlG8XFkbaDmaoq2yL2vduVPuuteoueUO9ZZus7SndKKylcLVOZD6nfa98Q5u+8UXlb3heHfXVmpwa1fTslI4cLTdACovytHtPoS2hMAdJJGKamJiwkpfkTv4gd5BDyI8ULoyTBA9jJienbYViaWnZ2IKEXb16XR988CP95je/1enZBa1+da0tvn6CD0D+7NOfsS/0fKmDlhXSpoZG/eZXv9aPf/SRPnz/X/XWnQe6e+OO3r77lm5fu2Ul67VLV0XOoLoioXsuIZ9gJPZrV67qwb37xhA09datW5qbP63EQFKxRFypkVG1tbUpnhgwi8b7dfHCgsaG+1Velq/KrOd0fMeLai/baEAM1BYaY2ix0bpdGjier3hVrn1kRxspz1Lw0DYFDqYrcmibYkezFS3PslXlrr0b1Fa8RrXZz+vIhm8q7+knlPUvT6qlokSpwahGJ1OamJtRZXWViouLlbMjU6VluxUK9xg7ksm4pqenHwNCyQsblpaWjCGAAzAw5ubNm1pcPKeFhbPGlLNnzxsYgIMtL1/Rz3/6S/3nr3+n4cGUnn7qOxRVD9lB+yef/GNlZ2ZZRP/ut/9l2g8LsDvXb5vdu3lXN6/cMOYAEvkDx59dOKeF+UWTLkDCYMmtGzdNtgCGDlKBXLq8LBJ7T6BXQ4NJDQ4krMREykj2PYFunT1/Rovn5hU6mqm6glVmgSPbFK2GGTnWsj1aW6yB6p1KVOZam6zKU395tiKHMx/agc1KHM006zu4RYHSDerc9brqsp9T+fpvqOA7/6B9G76riUiHFuenFaU/Y2Mqr6xSfn6+duRm2eJiItmvWKxfAELyRrKotMgfDgJAIV8YxwDp0qXLlkNwPnbjxi1rHZBzixd1++Y9/eC9H+qH73+otD/9xKf0ybRP6KtPfEXxSMzWnX72bz/V/Vv3hDzdvH7D5IZcQKQjP+5kkjV55PKlK5Y/HgJw2/IMckVSh6IYnSHBYWjp+fMXFY3GDYBwX0T9sT6F+0KK9IfVHwsrMRA1UM5cWlJN9SEdyl5tL45Gyrdo4Mgm9VVkKFJToMHaPeqvLFTPoRyz2Jv5ildsV/+BTRos36pQ+Q6F+S/x+O8oDq1XuOgVdWY/pxNvfEt5z35Z69e9prqTx3T+3Ly6Olv16qoXVXeyxix/Z65ycnJ08uRJm2+QL8gL5EHkin1kCUYABokcIACMmTuVFjn17u17VgQRpAQv7YN7bz0M2uUrBhoLr4CUBhiFuQUGwP/89r9NomABgFy+sKw7t24bALT37tw16aGUvXxp2faRJoAw51+5bmCQ5HmwHb902WhKpAAK5R+AoKOnT59RW0e7QpGwVVwdXWwHFY1HlByMaX5hVtk7d6p0T6GCdUdsLlG19Rn7nGe8vljhigwF9m9V/M0dSlTnW/7oO5JlS/nD1Ts0UrVdsUoKgO2KVqYrfmSjIsWrVPvGt1X62j9r/5YX1N7WqKWLi5qZHtPuXQXK3ZGlmhPHtK+sREWF+crLy9OJEyfszSETQSSKHAIgzEFgCIB4hUUugSHkEHIJhQ1SDgAELzkWY96Gf+7eva87d+5ZoJL40yK9YcGIX//yV5Yjri9f09WlKyI/kDNgBOxwQJAeZt+AAlMABIY4IKCP8VBYAgDMW2hhBZqKlhINlIDJgSG1d3ZYcocpgBII9WpoJKlIf1Cf/fO/0Ib1a3VhfkL1Fbu05bmvqCLjWUUOb9ZIVZYiBzYrWPaGkKNkBSvGmy2R8xZysDpPA8fylKjMVKx8o0J7Vtl7/EPrn9HBzHUKNddqeiqlmzcuq7enQ5s2rtOe3cUqLipQXm6O9pftU2FhoWpraxUKhYwRsMMBYZaO88khsAJg2IYpMATJovoECKYJMIVgxT8rAQEUL4/TSNgfvPu+5QXAIFfAEOQKc0AoXd+6/8CAWV66pEsXl0zOeNCV5YfUJBoAgoe6wQoHhOQGK2ZnT2tubt6AOX9hSb2BkJpbW6wMJq9093aZhNU3ntKnP/UZPfnk15QcTGhyaljHyot0YPvLqtn+vPpL12mwMkvhsnUKlqzRcMU2RQ9uVGvRa+rZt8mqr8HK7UqWb1V476tqyHhab25+RrXF2xTrbreAuHZ9WeFIr6imsrMztXNnrtatW2cJHSD279+vlpYWAwNWAAjzCyaF7C8uLlplBRiYMwTpsnPnLj4GAEAAAkDwD4EMK2AIss6sPg0WeOKmgnK5ev+dH+ide2+bLAEGcuWSxSTvyvJlA4sKixuT0NFLHgZFiQqrtm7cMlrysAsXlowhMzNzGhlJKZUa18zsaU1Nz6q9o0uNzU02e0fC2jvbBCCbN27Rl5/4e72yboNqmhsUCLarsarU/li0tWiN2vduUORIhsL71ytcslqR0tcVPbzFKq7uQxmK7Fuv2L4N6tz5oupznlfDni1KRdp1984tnTl/WZG+gHLzsvXss99RUVGBdu7cqfT0dJWXl6u9vVP19fW2wuBgjI+PC3NgcDpzD0/stJ7UYQvS7SDgE4DgGMaKhjMDCWc7DVbgeOYbvs3bPewHb3//kdMf2OSOvMFCIXb18hVbagd1bszLJ9dLJo5OSYCAIR4FMAbpoj6fmJjS0PCYziyc1+DQiBqbWowpzOCZn9ScZHbcpdy8fD313e/pjYwsHT9Zq/qaSrVUH1D9wXzV7N2mziM5atuzTj17V6v/4HoDJ1Keqc4D6QrtfV3BXa/rVOYLairerPaqUkWCHcbG7B0F+t4Lz2nj5je0YeN6FRQVKndnnvaWMDNvUKQvasmcpRFfHiF5I0/MPwAFeSJf0Lps+TGbkyxfNdVARQhWjG0AQeYBAh89BoTkDRAAAjDswxLAoKzl/QZSRe4AiKmJSbFgyJIIVRcPQCdZMiFJwY63H7xjkcADSebkCh6KQU0v/QBnavq0RlOTmpk9o0RyUCfr6tXS1molcW3dSVVUHlV9c5O2ZGRq49YMHTpaqb2lJSospCTNVFfjcXXVHNDxgrX211cj1RkKlq1V7/6NClVkq6/kdXXmv6pj6S+p5WCRWuqqtGtPoVa99or+9m++pCe/9lXlF+5U9fEqFe/eZXa0olLdPQGNph4yAXmixIUlXtoCCAmeffIFrCCxAwqAAMby8rIFKX4wNjySc8BA2gEGfxCsBK0ldZwPGxwU8gm5g+MAxMIgeeTCufOaHJ+wt36Ag3x5UgcQJIsHQ0tAcWryENBnLYdtr7QAicS+ePaiYvFBjU/MmHyFwn3GlKaWZgOl+uRxxYcSKi7erZ15Repo79XR6hrll+5TwcFDtvra01yjA9mvqSrjWUUPbzILH96q7oNbFdy1Si05L+tE1lrFmus0kIyqqGSXXl71gtLXr9FTT39Ta9evsYngvgP7VXvylFk40q9EcticjuOdIVRVJHXYgQGAM4RzDgoTQwBBzglUAEHCPXf4MWQcULzyTPv+u+9ZFUWiZs7x7tvvmDz5NgxBnqYnpwwMrmNdirwCIDxg8cxZzc2cNjBghy+hEAme1GEHlRYGGGfOLJqhs0QZgyPKiC5kioQaCAQ0PjurzmBQxaWlaunqUl1zs46fOqWOQEB9yaS6uhrsi5DqqqPa/OIzqs1brcSRdPWUrdOpwtfUm/ui/S1hov2YkgMR7S0/qJfWrtHqNa+pZHeBNm5N1+7SMlWdqFFTW7t6wxFbX+sOhjQ4mtLk1IyisYRJKtvku4nJaQ2PpIxBJHLyCH0HHJct8gos8WoSx7ON41EIjJwBYACF0uC3NOQIxxL1lLbsewJ/7513zfGUuWOjKS3MnzFwAIRrkS2qrNnpOcshJPh33wbgOwbOyrLXZYpOUfZSbWFUJYBAovRZLxLR0dGhpqYmTczNaUdhoZ5ftcpA6IlEDJCahgY1trerN9Bh71Zi/VHty9mq7c9+Rd2FrypYtt7+iLQm80VFqnYr3nnKVmzXpW/VC6+v1p6S3TpafkB1jU1qbu9QZ29A/Ymkraf1hMK2Aj0zf0apsQnLJZTnAAEoY+OTBtDQ8KglcySLMQAKRmKn5AUUVwKkGzAwgpJANVu6/PuArJx5s+1GfsDOLiyaVI2nxmzuAVswGML8BOdPjE1aheXL8EyCAAfkAQBKYnTKK635+QVjCCAQVcgA0sA2EUdpyRu7xtY25RYUKjs3TziKCGa5ngg+2dCo5vY2dQYiGh4aU+XeIq35x79Wx85V6t2zRqdyX1HDkUIluurVWldjs+7nX35V6VnZOtVQp4bGWrV39xgjuDeA8GqZFWj2x6amNTI6ZoBQdDhDxiemDBCO/SGGeA5BtihgMMaOdAMIIHGMFpknn/weQ3AuQAAA7PAWeUrGExodHrGEDouotCh5uYbfkD+mJqbthgBC1UUOwQDLlwQ8KgCIzsASzgEEkoX2wgwM1iBlJNGWjk6FozGF+qMGAhHNNs4DnI7ekNoDMfWG4mqqrFTOd/9JZS99TQ05L6u5aIMifd32CpYvQ9a8vkHp27br2PEaNbQ2qq27RfXNLcYOAABkwOZ53H94bNwYgmQhUcjV9MycAQNQGAFEf30MnkNgDYHFGJl/Md6V+RMfmIw9elVB8CLxJlk+C8fJSBZ5gxzCa9e+cEQzU9MCHCQKQAAGMGjHUxOWQ8gdmKNNbkHOeCiRACBECJ3yCKGT0JvBYFQuJE9aoozBIQ8ekawIB0MRm0h2dfcKyYiNjKknOqRg35BGogmVbVqtZ/4qTXUFGzTWVaOa1kbl7dmtLRnZysrOU+Wbx225v62rU92hHnX09CoSixvIbAMKLBlKjWlkfMLyRCyeNOcjWS5bVGDImQNCizEOWsAAKMZPvkSeGS8BiQGU7V+4ZEB41ZUGGOQQ5Acnk9QxHA8zhgeH7G0feQMAsIfV1XVjzcjQqCUm5h5UWNyY3IEBCCBAVwBBtmgBhA4hWzDBJ1sGwKP6HiljcCvBICLZJ1qpgvr6Ywom4uqKJtU/PKmx0XGVF+boxa9/UT01hxVqP6VN2VnaXlSovMIiVVYdU0dntwLBPnshFuyLKRDpM0AABWb0xROWR0joMATQ+6NxawEAow/0BVBwPmNAunxyyDYMARCc/zDwFmy8jBuQ/BhzN5iBv6i80tzBDghVF0zhHXgiFrd5B2wBBHKHg+FlMBNC9A95wpAq10M7/qjU9aTmeYSOETUwAdnypM42BjgAgkycnl8wJwwMDj+WDk+2HcEOtZFbEkP2kqut/oRqj5bpVG2ldhTmKz1jq/YdKNOxE9Vq625XTyiocDSh0dSUotHhx+xAogBhdGLSgDC5mpwSzwQQggC2rgQEULwQWQkIYwIkEjxrd4yVlvEShOxjbJNDAAQDnDRP0OQO5ApAAGBibFyRUNjKXVgEYLCEFvZQcSFlIIs8gS4MIY/ADNdEJj2eN2hhBxHiVPbOMyBAIbGT0AGFwRKNs3Pz5gwcgoZzzC02FFJbuFfN4X5Fh0c1MT6i0aGoMnNz9NLWbao9/qaOH6tQV3eruoIdCvQFNTw2qYGBcSUSk4omH77TJ3cASnzw4ZtLwEGySNxIFkAQHJ7Y2TfZSqWMJTCFsQACgUTLGKanZ40N5AwA8DU8xg8oDgiTROYqaaxL4WRYgePJHzicMjIejen07PzjWhnn80NeRlHqcg7HwwzA8NYX0ZAvyl3mIJ7QYAid85YOsrbFUgrvmVnfcmN/7vQZcwRgYGg4YHi08qKLDyZ4+8irYd5GHn2zQpu2bFZJWamOn6xTW1e3SRNOR5IAAcP5lofiSXtbifNhBC3RDyv8GM8FAM47GFwzODhsTif6GQP2UIrP2rgAhBzCMYzlIgeDRdYL55e1fOmaLi1dtW0DBBkif/giItEfDoZMsqiifO2FFuMYk0EQBWFAAQSYgbENa0wbr92wCRAAAMpKtqyMGjo+jiSsAIRtZwItDlnpKOYGo2MpezfPgiTfevHxHYBkZGXa2tRKMFYCYmCkxszB5CJkifu5LLrTYQdVlj8bAGEJABEU9BlHI0mMAYfjaABin4DjnIPC9TCDHEIgzs0u6OziRZ07u6Qz8+eU5jN0AEG2kDASeXdnlwYSSXM42obzmVW61rGyi0wBEOxg3uH5g2u4lt9Rd/scxAGhQw4MJSEDYhBEl4MySlk5krIIBgycgJNwCFHLuheO5N0833d1dHWK5Zaunm6xBJKTu0NFu4otR8AKSmXM2eGVFPdiuSYQDJvjAYVnAAAM4TkAQiB4MNAXwOA8fcTpOBwAGANgAIKPDed7pQXrOY4ByPjYtOZPn9XCmfOampxTGvJEznBAWDgM9gbU2d5hHyDAACQKAwBkC0ezD1OQJVgBIJ5LAI9r+K1XVzCEbYBYCYh33COKCIMZDHR4eNQGjQNYpn8clbZKPGqAAAbs4H3KqYZ6WyU+ePiQgVLf2GAlLBUU1RRVFF+2wA7yRXL4oTQBCIuJAExp7bIFGGxzzAGhpT8ABng4mIDC6QQV/XdmsO/soWWMjA1wMM5PT502MBYXLmgsNaU0XjQxvwAQpItk3t7apkBPr81DYADOR6JwMPs4/NzieZudu2R5ycs+YDibAMHWbB69ugUYBwQtdUBWsoRBOiAMHAf4GpKDw3Fkhs+JYAnsqKs/Ze/oAYJ3K+QUAAAQJn4whG/AMI6TuGEfJbR9ihRLGDvc+bAHp/vEEEbwXIKD33EeJuNYIt4BgSWAgjkosAjjesbqjHF2wJDBgVGlsU7la1PIF4m8panZ8gfnKF0BwD+AYxtmOAPYhyUkdHIJQDgoXOeAIF3MSWAI0eSJjY559NBhBoFs0XEYgnOQBwxn0MIUnGPaPpbS2MS4saPqWLWB093bY6+F+YIF5yNTAAJL2Ke0xcanH+YC3nsgWbDB5ZEAYBv5gikOEn3gHP0AEJK65w36zjYfxzEWGAE4SBPnHBBaxmzjnlvU7MwZs2h/Umn8EQ1LIbCEZN7b3WOAsHYFe8gHON0NtgCOO53zgIZcAYznDhhk70gefW0CIL6OAwiAQlTRuZVG539Pth5FJfkDEHAaUYuTsImpSfsK8njNCVUfP2ZfRQJIa3ubgQIY5BAAYakFmWKNKjU5ZeYMARAcDNiwEOMcAPBMDwiXLPpj10/wDoRy92EOhN0OiuWIcZbkZx6D5IxizABG3kiNTmpifEZ9kbjSeOEEM0jmLLG3tbSqtbnlkVxdNmkCBACBFUgXzmaf44CA+UwToMg1XMt1SJTP0EnqVFZ0xJnhg3FQ2CeqGBidd4bgHLapeohoWqKXkpePI2BHQ1OjsaU3GLB3KeQWX4x0QGAIgDDxAxwc68sxAM0+IPAs7v+Hcoi/dqZPsJn+ep/pNwAQ/QSc7ztrGBNs8XGTN5Aq2v6+hNKGhkbMaUQsut3bG1RfX9R0Hn330pQb+E2IBneql7M4m21fpyJPYNx3ZWJnf2Xe8GqDe3Oc33B/Bspg4smEOX16dsacTRKnvCV38GEdZS+vfA+XHzFG8M0wpS/fDdsX9n1RcypOJ3njZJjmzuZaKjM+rmCbz1vJSdwHsGnZ5zns03pfuJ4kjVkBAoADQ+ZH/IPjOe5gcB0S54DBJO8LhQOBlhaPJw1JHIEDurt7FQ73GcJEMQ5zbcRR7OM4zvFQfkc+cGBoyRO+zzWAwL5XWfzeNdSp7loLMHScQMFwEMmZPIFDAAQAcDhgAQoOPXDooFVaHAMIWAJwSBGR76yyqH4kRTiDe3ItbHKn8zy+0OeZHMPx45MTtg1AbNPybAIHB2P0nXHgL8YM62OxhI3HwcHf+NnH6fJLgUJf0/r7Y3ZTnIQj+NIClvADnMlxDKb4sgcgsE1OYBsAPEfgeADCuMYdz3U+D+EY96fDrrdsO8geAAyWweMQnIQBhAPC7Jxz7DMZZC6Ck2APDqb1ZM2AiUIA8XyANOFY2MH1gIBxDDAw2AHI/icUDg7n7NhIyvznwNAyJsYISDifbfzHuAgyrmH8yJcDQoECg9OgECdwhgMCQ9xBMIIo5ubckBuxz0MACjConnhJzxKJJ27A8IqK64gYOsW23887RevXcF+eQ6fp18zcrAGBo3GGO9sZgsOIcADhSxX2YQgOxpEMkjwAQyhvScTs08IQruFDPUABCBjjDMHhPBOjeIAt3N+v41p8Rl9pcT6y7yAwDuSfc4yZc3w+i4wBGuAABEb/jCFcAK24aTI5qObmVgUCIbspNwIoHMYNnI48yAGDEQBC4oYBAAh43A9jH3Y4GNwPp9NB7ueSBoBcx335HZ22daLZGXMIjkC+cDZShHQBDizBmeUVR40hXMdxzgMiuYMoBAzAYRsDFPIIwDKpRPZwPM52QGDB/wcI/SRwaHE4faZlfPgMBcKP+JBrAIg8w28SiQGbjDIhpW/MhdKCwbDQNS6gbW1tN8nipvwAtDFuDhA40bedijDB2cCD6YDnAPZhB+d97sE+vwU4gAIIGLKSHdwD86jEuRiR7xKDMwGAff6eA8kCNKL+8bneoEkVEUgkehUFOzAApUSmVAYIoh5QkEpYwf3/L8nCRz5WfMg2vmNs7lP8h+8IeIIdUCAB27xow/hQsKm5Vf8Lw3/I1SUKttUAAAAASUVORK5CYII=\\" alt=\\"image.png\\" rel=\\"5\\" /></a></p>\\n<p><strong>João Moura</strong> is an AI/ML Specialist Solutions Architect at Amazon Web Services. He is mostly focused on NLP use-cases and helping customers optimize Deep Learning model training and deployment. He is also an active proponent of low-code ML solutions and ML-specialized hardware.</p>\n"}
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亚马逊云科技解决方案 基于行业客户应用场景及技术领域的解决方案
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亚马逊云科技解决方案
基于行业客户应用场景及技术领域的解决方案
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