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Using Machine Learning to Build Better Machine Learning

#artificialintelligence

Automated machine learning(AutoML) is becoming one of the most popular topics in modern data science applications. Often, people see AutoML as a mechanism to use out-of-the-box machine learning models without the need of sophisticated data science knowledge. While theoretically, this argument makes sense the reality if a bit different. In the current stage of artificial intelligence(AI), most real world applications require some level of machine learning knowledge. The scenarios that you can solve with a vanilla API like the Watson Developer Cloud or Microsoft Cognitive Services are very basic and represent only a small percentage of the broader spectrum of machine learning scenarios.


Using Machine Learning to Build Better Machine Learning

#artificialintelligence

Automated machine learning(AutoML) is becoming one of the most popular topics in modern data science applications. Often, people see AutoML as a mechanism to use out-of-the-box machine learning models without the need of sophisticated data science knowledge. While theoretically, this argument makes sense the reality if a bit different. In the current stage of artificial intelligence(AI), most real world applications require some level of machine learning knowledge. The scenarios that you can solve with a vanilla API like the Watson Developer Cloud or Microsoft Cognitive Services are very basic and represent only a small percentage of the broader spectrum of machine learning scenarios.


AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

arXiv.org Machine Learning

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers. Experiments reveal that our multi-layer combination of many models offers better use of allocated training time than seeking out the best. A second contribution is an extensive evaluation of public and commercial AutoML platforms including TPOT, H2O, AutoWEKA, auto-sklearn, AutoGluon, and Google AutoML Tables. Tests on a suite of 50 classification and regression tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is faster, more robust, and much more accurate. We find that AutoGluon often even outperforms the best-in-hindsight combination of all of its competitors. In two popular Kaggle competitions, AutoGluon beat 99% of the participating data scientists after merely 4h of training on the raw data.


State of the Art in Automated Machine Learning

#artificialintelligence

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Aerospike is the global leader in next-generation, real-time NoSQL data solutions for any scale. Aerospike's patented Hybrid Memory Architecture delivers an unbreakable competitive advantage by unlocking the full potential of modern hardware, delivering previously unimaginable value from vast amounts of data at the edge, to the core and in the cloud. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important?


State of the Art in Automated Machine Learning

#artificialintelligence

In recent years, machine learning has been very successful in solving a wide range of problems. In particular, neural networks have reached human, and sometimes super-human, levels of ability in tasks such as language translation, object recognition, game playing, and even driving cars. Prevent out-of-control infrastructure and remove blockers to deployments. With this growth in capability has come a growth in complexity. Data scientists and machine learning engineers must perform feature engineering, design model architectures, and optimize hyperparameters. Since the purpose of the machine learning is to automate a task normally done by humans, naturally the next step is to automate the tasks of data scientists and engineers. This area of research is called automated machine learning, or AutoML. There have been many exciting developments in AutoML recently, and it's important to take a look at the current state of the art and learn about what's happening now and what's coming up in the future. InfoQ reached out to the following subject matter experts in the industry to discuss the current state and future trends in AutoML space. InfoQ: What is AutoML and why is it important? Francesca Lazzeri: AutoML is the process of automating the time consuming, iterative tasks of machine learning model development, including model selection and hyperparameter tuning.