Automated Machine Learning (AML) - AutoML
- AutoML.org ...ML Freiburg ... GitHub and ML Hannover ...
- Neural Architecture Search
- Automatic Machine Learning (AutoML) Landscape Survey | Alexander Allen & Adithya Balaji - Georgian Partners...
- Google AutoML automatically build and deploy state-of-the-art machine learning models
- Ludwig - a Python toolbox from Uber that allows to train and test deep learning models
- TPOT a Python library that automatically creates and optimizes full machine learning pipelines using genetic programming. Not for NLP, strings need to be coded to numerics.
- H2O Driverless AI for automated Visualization, feature engineering, model training, Hyperparameter optimization, and explainability.
- MLBox Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…). Prediction with models interpretation. Primarily Linux.
- auto-sklearn algorithm selection and hyperparameter tuning. It leverages recent advantages in Bayesian optimization, meta-learning and ensemble construction.is a Bayesian Hyperparameter optimization layer on top of scikit-learn. Not for large datasets.
- Auto Keras is an open-source Python package for neural architecture search.
- ATM -auto tune models - a multi-tenant, multi-data system for automated machine learning (model selection and tuning). ATM is an open source software library under the Human Data Interaction project (HDI) at MIT.
- Auto-WEKA is a Bayesian Hyperparameter optimization layer on top of Weka. Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. Weka contains tools for data pre-processing, classification, regression, clustering, association rules, and visualization.
- TransmogrifAI - an AutoML library for building modular, reusable, strongly typed machine learning workflows. A Scala/SparkML library created by Salesforce for automated data cleansing, feature engineering, model selection, and Hyperparameter optimization
- RECIPE - a framework based on grammar-based genetic programming that builds customized scikit-learn classification pipelines.
- AutoMLC Automated Multi-Label Classification. GA-Auto-MLC and Auto-MEKAGGP are freely-available methods that perform automated multi-label classification on the MEKA software.
- Databricks MLflow an open source framework to manage the complete Machine Learning lifecycle using Managed MLflow as an integrated service with the Databricks Unified Analytics Platform.
- DataRobot build highly accurate predictive models with full transparency
- SAS Viya automates the process of data cleansing, data transformations, feature engineering, algorithm matching, model training and ongoing governance.
- Pipelines & AIOps
- Graphical Tools for Modeling AI Components
- Automated Machine Learning (AutoML) | Wikipedia
- Feature Exploration/Learning
- Other codeless options, Code Generators, Drag n' Drop
- Software Development
- AI Software Learns to Make AI Software
- The Pentagon Wants AI to Take Over the Scientific Process | Automating Scientific Knowledge Extraction (ASKE) | DARPA
- Hallucinogenic Deep Reinforcement Learning Using Python and Keras | David Foster
- Automated Feature Engineering in Python - How to automatically create machine learning features | Will Koehrsen - Towards Data Science
- Why Meta-learning is Crucial for Further Advances of Artificial Intelligence? | Pavel Kordik
- Assured Autonomy | Dr. Sandeep Neema, DARPA
- Automatic Machine Learning is Broken | Piotr Plonski - KDnuggets
- Why 2020 will be the Year of Automated Machine Learning | Senthil Ravindran - Gigabit
- Meta Learning | Wikipedia
- Google Natural Language
- World Models
- Evaluation Measures - Classification Performance
- Service Capabilities
- AI Marketplace & Toolkit/Model Interoperability
- TransmogrifAI - workflows on Spark | Salesforce ... GitHub
- Graphpipe | Oracle
- Platforms: Machine Learning as a Service (MLaaS)
- Journey to Singularity
- Digital Twin
- Inside Out - Curious Optimistic Reasoning
- Evolutionary Computation / Genetic Algorithms
- Apprenticeship Learning - Inverse Reinforcement Learning (IRL)
- Imitation Learning
- Simulated Environment Learning
- Differentiable Programming
- Particle Swarms for Dynamic Optimization Problems | T. Blackwell, J. Branke, and X. Li
An emerging class of data science toolkit that is finally making machine learning accessible to business subject matter experts. We anticipate that these innovations will mark a new era in data-driven decision support, where business analysts will be able to access and deploy machine learning on their own to analyze hundreds and thousands of dimensions simultaneously. Business analysts at highly competitive organizations will shift from using visualization tools as their only means of analysis, to using them in concert with AML. Data visualization tools will also be used more frequently to communicate model results, and to build task-oriented user interfaces that enable stakeholders to make both operational and strategic decisions based on output of scoring engines. They will also continue to be a more effective means for analysts to perform inverse analysis when one is seeking to identify where relationships in the data do not exist. 'Five Essential Capabilities: Automated Machine Learning' | Gregory Bonnette
H2O Driverless AI automatically performs feature engineering and hyperparameter tuning, and claims to perform as well as Kaggle masters. AmazonML SageMaker supports hyperparameter optimization. Microsoft Azure Machine Learning AutoML automatically sweeps through features, algorithms, and hyperparameters for basic machine learning algorithms; a separate Azure Machine Learning hyperparameter tuning facility allows you to sweep specific hyperparameters for an existing experiment. Google Cloud AutoML implements automatic deep transfer learning (meaning that it starts from an existing Deep Neural Network (DNN) trained on other data) and neural architecture search (meaning that it finds the right combination of extra network layers) for language pair translation, natural language classification, and image classification. Review: Google Cloud AutoML is truly automated machine learning | Martin Heller
New cloud software suite of machine learning tools. It’s based on Google’s state-of-the-art research in image recognition called Neural Architecture Search (NAS). NAS is basically an algorithm that, given your specific dataset, searches for the most optimal neural network to perform a certain task on that dataset. AutoML is then a suite of machine learning tools that will allow one to easily train high-performance deep networks, without requiring the user to have any knowledge of deep learning or AI; all you need is labelled data! Google will use NAS to then find the best network for your specific dataset and task. AutoKeras: The Killer of Google’s AutoML | George Seif - KDnuggets
Automatic Machine Learning (AML)
DARTS: Differentiable Architecture Search
- DARTS: Differentiable Architecture Search | H. Liu, K. Simonyan, and Y. Yang addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, the method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent.
- Neural Architecture Search | Debadeepta Dey - Microsoft Research