Machine Learning on Local or Cloud based NVidia or Apple GPUs

[obriensystems]

• Machine Learning on Local or Cloud based NVidia or Apple GPUs
  https://github.com/ObrienlabsDev/machine-learning/issues

[obriensystems]

PMLE Training

• https://www.cloudskillsboost.google/cou

PMLE Notes

• Machine Learning Crash Course https://developers.google.com/machine-learning/crash-course/representation/cleaning-data

• learn gradient ascent and expand the partial derivative section - "the negative of the gradient vector points into the valley" https://developers.google.com/machine-learning/crash-course/reducing-loss/gradient-descent

• deep field before deep learning https://esahubble.org/images/heic0611b/ https://simbad.u-strasbg.fr/simbad/sim-id?Ident=Hubble+Ultra+Deep+Field

• https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software

• tree classifier using area under the curve - https://dmip.webs.upv.es/papers/ICML2002presentation.pdf - the greater AUC means better positive/negative classification

• XGBoost - https://xgboost.readthedocs.io/en/stable/tutorials/model.html https://www.analyticsvidhya.com/blog/2018/09/an-end-to-end-guide-to-understand-the-math-behind-xgboost/#:~:text=XGBoost%20is%20a%20machine%20learning,won%20several%20machine%20learning%20competitions.

• https://medium.com/google-cloud/vertex-ai-pipelines-vs-cloud-composer-for-orchestration-4bba129759de#:~:text=Vertex%20AI%20Pipelines%20is%20serverless,Even%20with%20GPUs%20if%20needed.

• https://cloud.google.com/blog/products/ai-machine-learning/schedule-and-execute-notebooks-with-vertex-ai-workbench

• https://codelabs.developers.google.com/vertex_notebook_executor#0

• https://www.tensorflow.org/guide/tpu#distribution_strategies

• TPU nodes(gRPC)/VMs(ssh) and twisted topology https://cloud.google.com/tpu/docs/system-architecture-tpu-vm

• TPU V4 up to 2048 TPU cores - https://cloud.google.com/tpu/docs/supported-tpu-configurations

• JAX Autograd (automated gradient function) and XLA (Accelerated Linear Algebra - see cuBLAS) https://jax.readthedocs.io/en/latest/

• https://neptune.ai/blog/retraining-model-during-deployment-continuous-training-continuous-testing

• hashing or homomorphic encryption https://fastdatascience.com/sensitive-data-machine-learning-model/

• TensorFlow Data Validation and Pandas https://www.tensorflow.org/tfx/data_validation/get_started

• TensorFlow from Google Brain https://en.wikipedia.org/wiki/TensorFlow#TensorFlow

• Batch and Streaming data processing https://beam.apache.org/

• https://medium.com/mlpoint/pandas-for-machine-learning-53846bc9a98b

• training with mini-batch gradient descent https://towardsdatascience.com/batch-mini-batch-stochastic-gradient-descent-7a62ecba642a

• https://en.wikipedia.org/wiki/Regularization_%28mathematics%29

• training with L1 regularization (prevent overfitting) https://towardsdatascience.com/regularization-in-deep-learning-l1-l2-and-dropout-377e75acc036

• small normalized wide dataset (reduce feature scaling for training) https://developers.google.com/machine-learning/data-prep/transform/normalization

• PCA https://www.analyticsvidhya.com/blog/2022/07/principal-component-analysis-beginner-friendly/

• reduce ML latency https://cloud.google.com/architecture/minimizing-predictive-serving-latency-in-machine-learning#optimizing_models_for_serving

• https://en.wikipedia.org/wiki/Decision_tree_pruning

• https://en.wikipedia.org/wiki/Confusion_matrix

• https://www.tensorflow.org/guide/keras/serialization_and_saving

• https://www.tensorflow.org/guide/saved_model

• https://cloud.google.com/vertex-ai/docs/model-registry/introduction

• https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc

• https://colab.research.google.com/github/Fraud-Detection-Handbook/fraud-detection-handbook/blob/main/Chapter_4_PerformanceMetrics/ThresholdBased.ipynb

• https://colab.research.google.com/github/Fraud-Detection-Handbook/fraud-detection-handbook/blob/main/Chapter_4_PerformanceMetrics/ThresholdFree.ipynb

• https://cloud.google.com/storage/docs/encryption

• https://cloud.google.com/vertex-ai/docs/workbench/managed/schedule-managed-notebooks-run-quickstart

• https://cloud.google.com/vertex-ai/docs/pipelines/run-pipeline

• https://cloud.google.com/architecture/setting-up-mlops-with-composer-and-mlflow

• https://cloud.google.com/tpu/docs/intro-to-tpu#when_to_use_tpus

• https://www.tensorflow.org/guide/distributed_training

• https://www.tensorflow.org/tutorials/distribute/multi_worker_with_ctl

• https://cloud.google.com/dlp/docs/transformations-reference#transformation_methods

• https://cloud.google.com/blog/products/identity-security/next-onair20-security-week-session-guide

• https://cloud.google.com/dlp/docs/creating-job-triggers

• https://www.tensorflow.org/tfx/guide/tfdv

• https://cloud.google.com/tensorflow-enterprise/docs/overview

• https://cloud.google.com/architecture/ml-modeling-monitoring-analyzing-training-server-skew-in-ai-platform-prediction-with-tfdv

• https://developers.google.com/machine-learning/crash-course/representation/cleaning-data

• https://developers.google.com/machine-learning/testing-debugging/metrics/interpretic

• https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

• https://developers.google.com/machine-learning/crash-course/feature-crosses/video-lecture

• https://cloud.google.com/blog/products/ai-machine-learning/building-ml-models-with-eda-feature-selection

• https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

• https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview

• https://cloud.google.com/vertex-ai/docs/vizier/overview

• https://google-cloud-pipeline-components.readthedocs.io/en/google-cloud-pipeline-components-1.0.7/google_cloud_pipeline_components.v1.hyperparameter_tuning_job.html#google_cloud_pipeline_components.v1.hyperparameter_tuning_job.HyperparameterTuningJobRunOp

• https://developers.google.com/machine-learning/testing-debugging/common/model-errors#establish-a-baseline

• https://cloud.google.com/automl-tables/docs/evaluate#evaluation_metrics_for_regression_models

• https://developers.google.com/machine-learning/glossary#baseline

• https://cloud.google.com/ai-platform/training/docs/training-at-scale

• https://cloud.google.com/ai-platform/training/docs/machine-types#scale_tiers

• https://cloud.google.com/vertex-ai/docs/training/distributed-training

• https://cloud.google.com/ai-platform/training/docs/overview#distributed_training_structure

• https://github.com/tensorflow/mesh

• https://cloud.google.com/vertex-ai/docs/featurestore/overview#benefits

• https://cloud.google.com/architecture/ml-on-gcp-best-practices#model-deployment-and-serving

• https://cloud.google.com/memorystore/docs/redis/redis-overview

• https://cloud.google.com/vertex-ai/docs/experiments/tensorboard-overview

• https://cloud.google.com/vertex-ai/docs/ml-metadata/introduction

• https://cloud.google.com/vertex-ai/docs/pipelines/visualize-pipeline

• https://cloud.google.com/architecture/ml-modeling-monitoring-automating-server-data-skew-detection-in-ai-platform-prediction

• https://cloud.google.com/vertex-ai/docs/model-monitoring/overview

• https://cloud.google.com/automl-tables/docs/problem-types

• https://cloud.google.com/blog/products/gcp/predicting-community-engagement-on-reddit-using-tensorflow-gdelt-and-cloud-dataflow-part-2

• https://www.tensorflow.org/tutorials/keras/regression

• https://www.tensorflow.org/tutorials/keras/classification

• https://cloud.google.com/architecture/best-practices-for-ml-performance-cost

• https://www.tensorflow.org/lite/performance/model_optimization

• https://www.tensorflow.org/tutorials/images/transfer_learning

• https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data

• https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/structured_data/imbalanced_data.ipynb

• https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/calibration/calibration-node-classification.ipynb

• https://developers.google.com/machine-learning/glossary#calibration-layer

• https://developers.google.com/machine-learning/testing-debugging/common/overview

• https://developers.google.com/machine-learning/crash-course/regularization-for-simplicity/l2-regularization

• https://developers.google.com/machine-learning/crash-course/regularization-for-sparsity/l1-regularization

• https://cloud.google.com/bigquery-ml/docs/preventing-overfitting

• https://www.tensorflow.org/tutorials/keras/overfit_and_underfit

• https://www.tensorflow.org/tensorboard/get_started

• https://cloud.google.com/architecture/guidelines-for-developing-high-quality-ml-solutions#guidelines_for_model_quality

• https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning#data_and_model_validation

• https://cloud.google.com/architecture/implementing-deployment-and-testing-strategies-on-gke

• https://cloud.google.com/architecture/application-deployment-and-testing-strategies#choosing_the_right_strategy

• https://cloud.google.com/vertex-ai/docs/general/deployment

• https://docs.seldon.io/projects/seldon-core/en/latest/analytics/routers.html

• https://cloud.google.com/blog/topics/developers-practitioners/add-preprocessing-functions-tensorflow-models-and-deploy-vertex-ai

• https://www.tensorflow.org/tutorials/customization/custom_layers

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/Lambda

• https://developers.google.com/machine-learning/guides/rules-of-ml#rule_32_re-use_code_between_your_training_pipeline_and_your_serving_pipeline_whenever_possible

• https://cloud.google.com/vertex-ai/docs/pipelines/lineage

• https://cloud.google.com/vertex-ai/docs/ml-metadata/tracking

• https://cloud.google.com/vertex-ai/pricing

• https://cloud.google.com/architecture/ml-on-gcp-best-practices#operationalized-training

• https://cloud.google.com/architecture/ml-on-gcp-best-practices#organize-your-ml-model-artifacts

• https://www.tensorflow.org/lite/performance/post_training_quantization#:~:text=Post%2Dtraining%20quantization%20is%20a,little%20degradation%20in%20model%20accuracy.