Automated Machine Learning
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The document discusses automated machine learning (AutoML). It defines AutoML as providing methods to make machine learning more efficient and accessible to non-machine learning experts. AutoML aims to automate tasks like data preprocessing, feature engineering, algorithm selection and hyperparameter optimization. This can reduce costs, increase productivity for data scientists and democratize machine learning. The document also lists several AutoML tools that provide hyperparameter tuning, full pipeline optimization or neural architecture search.
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Automated Machine Learning
- 1. AUTOMATED MACHINE LEARNING • Automated ML approach • Difference between traditional ML and Automated ML • Automated ML tools
- 2. WHAT IS MACHINE LEARNING ? Machine Learning, at its most basic form, is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.
- 3. MACHINE LEARNING TECHNIQUES • Supervised Learning • Classification • Regression • Unsupervised Learning • Clustering • Association • Deep Learning • CNN • ANN • Reinforcement Learning
- 7. CRISP-DM
- 8. WHAT IS AUT. ML. ? • Automated Machine Learning provides methods and processes to make Machine Learning available for non-Machine Learning experts, to improve efficiency of Machine Learning and to accelerate research on Machine Learning.
- 9. AUTOMATED MACHINE LEARNING INCLUDES Automated ML solutions aim to automate some or all steps of the machine learning process which includes: • Data pre-processing • Feature engineering • Feature extraction • Feature selection • Algorithm selection & hyperparameter optimization
- 10. WHAT IS BENEFIT OF AUT. ML. ? • Cost reductions • Increased productivity for data scientists • Democratization of machine learning reduces demand for data scientists • Increased revenues and customer satisfaction • Rolling out more models with increased accuracy improves business results
- 11. DIFFERENCE BETWEEN TRADITIONAL ML. AND AUOTMATED ML.
- 12. WHY IS AUT. ML. IS IMPORTANT? • Manually constructing a machine learning model is a multistep process that requires domain knowledge, mathematical expertise, and computer science skills – which is a lot to ask of one company, let alone one data scientist (provided you can hire and retain one)
- 13. WHY IS AUT. ML. IS IMPORTANT? • Automated machine learning enables organizations to use the baked-in knowledge of data scientists without having to develop the capabilities themselves, simultaneously improving return on investment in data science initiatives and reducing the amount of time it takes to capture value
- 14. TOOLS Hyperparameter optimization and model selection • H2O AutoML • provides automated data preparation, hyperparameter tuning via random search, and stacked ensembles in a distributed machine learning platform.) • Mir • a R package that contains several hyperparameter optimization techniques for machine learning problems.
- 15. TOOLS Hyperparameter optimization and model selection • In machine learning, hyperparameter optimization ortuning is the problem of choosing a set of optimalhyperparameters for a learning algorithm. ... These measures are called hyperparameters, and have to be tuned so that the model can optimally solve the machine learning problem.
- 16. TOOLS Full Pipeline Optimization • The goal of this ML pipeline is to gather data on inventory, users, and advertiser information, and to train ML models to predict the likelihood of someone clicking on an ad, at the time of auction. This is one of the core optimization parts of the Ad Tech business.
- 17. TOOLS Full Pipeline Optimization • AutoWeka • is a Bayesian hyperparameter optimization layer on top of WEKA. • Auto-Sklearn • is a Bayesian hyperparameter optimization layer on top of scikit-learn. • Firefly.ai • a Cloud-Based system for automatic generation of machine learning models • TPOT • is a Python library that automatically creates and optimizes full machine learning pipelines using genetic programming. • TransmogrifAI • is a Scala/SparkML library created by Salesforce for automated data cleansing, feature engineering, model selection, and hyperparameter optimization • RECIPE • is a framework based on grammar-based genetic programming that builds customized scikit-learn classification pipelines.
- 18. TOOLS Deep neural network search • Neural architecture search (NAS) uses machine learning to automate the design of artificial neural networks. Various approaches to NAS have designed networks that compare well with hand-designed systems. The basic search algorithm is to propose a candidate model, evaluate it against a dataset and use the results as feedback to teach the NAS network
- 19. TOOLS Deep neural network search • Devol • is a Python package that performs Deep Neural Network architecture search using genetic programming. • Google AutoML • for deep learning model architecture selection. • Auto Keras • is an open-source python package for neural architecture search.
- 20. TOOLS Automated Machine Learning Tools • DataRobot • https://www.youtube.com/watch?v=_JOKlSwKJ10Google AutoML • IBM Watson • https://www.youtube.com/watch?v=Kgb4QmojBN4
- 21. SAFA ÇİMENLİ Email: safacimenli@gmail.com Linkedin : https://www.linkedin.com/in/safa-çimenli-42b52484