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Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools

arXiv.org Machine Learning

--There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers remains a fundamental challenge. Automated machine learning (AutoML) has emerged as a way to save time and effort on repetitive tasks in ML pipelines, such as data pre-processing, feature engineering, model selection, hyperparameter optimization, and prediction result analysis. In this paper, we investigate the current state of AutoML tools aiming to automate these tasks. We conduct various evaluations of the tools on many datasets, in different data segments, to examine their performance, and compare their advantages and disadvantages on different test cases. Automated Machine Learning (AutoML) promises major productivity boosts for data scientists, ML engineers and ML researchers by reducing repetitive tasks in machine learning pipelines. There are currently a number of different tools and platforms (both open-source and commercially available solutions) that try to automate these tasks. The goal of this paper is to address the following questions: (i) what are the available ML functionalities provided by the tools; (ii) how the tools perform when facing a wide spectrum of real world datasets; (iii) find the tradeoff between optimization speed and accuracy of the results; and (iv) the reproducibility of the results (a.k.a.


Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About

#artificialintelligence

A few days ago, Amazon announced the release of AutoGloun, a new toolkit that simplifies the creation of deep learning models with just a few lines of code. The release marks Amazon's entrance in the ultra-competitive Automated machine learning(AutoML) space which is becoming one of the hottest trends for enterprise machine learning platforms. With some many news around the AutoML ecosystem, sometimes it becomes hard to differentiate signal from noise. Today, I would like to explore some of the most innovative AutoML stacks in the market that don't receive that much publicity. AutoML is becoming one of the most popular topics in modern data science applications.


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.


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.