AutoML (41)

• AutoML: An Introduction Using Auto-Sklearn and Auto-PyTorch - Oct 11, 2021.
  
  AutoML is a broad category of techniques and tools for applying automated search to your automated search and learning to your learning. In addition to Auto-Sklearn, the Freiburg-Hannover AutoML group has also developed an Auto-PyTorch library. We’ll use both of these as our entry point into AutoML in the following simple tutorial.

  Automated Machine Learning, AutoML, Python, PyTorch, scikit-learn

• KDnuggets™ News 21:n36, Sep 22: The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python - Sep 22, 2021.
  
  The Machine & Deep Learning Compendium Open Book; Easy SQL in Native Python; Introduction to Automated Machine Learning; How to be a Data Scientist without a STEM degree; What Is The Real Difference Between Data Engineers and Data Scientists?

  Automated Machine Learning, AutoML, Books, Data Engineer, Data Scientist, Machine Learning, Python, SQL

• Introduction to Automated Machine Learning - Sep 15, 2021.
  
  AutoML enables developers with limited ML expertise (and coding experience) to train high-quality models specific to their business needs. For this article, we will focus on AutoML systems which cater to everyday business and technology applications.

  Automated Machine Learning, AutoML, Machine Learning, Python

• How to Create an AutoML Pipeline Optimization Sandbox - Sep 9, 2021.
  
  In this article, we will implement an automated machine learning pipeline optimization sandbox web app using Streamlit and TPOT.

  Automated Machine Learning, AutoML, Python, Streamlit

• Top 18 Low-Code and No-Code Machine Learning Platforms - Sep 8, 2021.
  
  Machine learning becomes more accessible to companies and individuals when there is less coding involved. Especially if you are just starting your path in ML, then check out these low-code and no-code platforms to help expedite your capabilities in learning and applying AI.

  AutoML, Data Science Platforms, Low-Code, Machine Learning, No-Code

• Fast AutoML with FLAML + Ray Tune - Sep 6, 2021.
  
  Microsoft Researchers have developed FLAML (Fast Lightweight AutoML) which can now utilize Ray Tune for distributed hyperparameter tuning to scale up FLAML’s resource-efficient & easily parallelizable algorithms across a cluster.

  Automated Machine Learning, AutoML, Hyperparameter, Machine Learning, Microsoft, Python, Ray

• Overview of AutoNLP from Hugging Face with Example Project - Jun 21, 2021.
  
  AutoNLP is a beta project from Hugging Face that builds on the company’s work with its Transformer project. With AutoNLP you can get a working model with just a few simple terminal commands.

  Automated Machine Learning, AutoML, Hugging Face, NLP

• Binary Classification with Automated Machine Learning - May 17, 2021.
  
  Check out how to use the open-source MLJAR auto-ML to build accurate models faster.

  Automated Machine Learning, AutoML, Classification, Open Source

• Automated Text Classification with EvalML - Apr 6, 2021.
  
  Learn how EvalML leverages Woodwork, Featuretools and the nlp-primitives library to process text data and create a machine learning model that can detect spam text messages.

  Automated Machine Learning, AutoML, NLP, Python, Text Analytics, Text Classification

• Easy AutoML in Python - Apr 1, 2021.
  
  We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

  Automated Machine Learning, AutoML, Machine Learning, Open Source, Python

• Google’s Model Search is a New Open Source Framework that Uses Neural Networks to Build Neural Networks - Mar 1, 2021.
  
  The new framework brings state-of-the-art neural architecture search methods to TensorFlow.

  Automated Machine Learning, AutoML, Google, Neural Networks, Open Source

• Easy, Open-Source AutoML in Python with EvalML - Feb 16, 2021.
  
  We’re excited to announce that a new open-source project has joined the Alteryx open-source ecosystem. EvalML is a library for automated machine learning (AutoML) and model understanding, written in Python.

  Automated Machine Learning, AutoML, Machine Learning, Open Source, Python

• Can you trust AutoML? - Dec 23, 2020.
  
  Automated Machine Learning, or AutoML, tries hundreds or even thousands of different ML pipelines to deliver models that often beat the experts and win competitions. But, is this the ultimate goal? Can a model developed with this approach be trusted without guarantees of predictive performance? The issue of overfitting must be closely considered because these methods can lead to overestimation -- and the Winner's Curse.

  Accuracy, AutoML, Cross-validation, Machine Learning, Model Performance, Overfitting

• Main 2020 Developments and Key 2021 Trends in AI, Data Science, Machine Learning Technology - Dec 9, 2020.
  
  Our panel of leading experts reviews 2020 main developments and examines the key trends in AI, Data Science, Machine Learning, and Deep Learning Technology.

  2021 Predictions, AI, AutoML, Bill Schmarzo, Carla Gentry, COVID-19, Doug Laney, GPT-3, Kirk D. Borne, Machine Learning, MLOps, Predictions, Ronald van Loon, Tom Davenport, Trends

• Algorithms for Advanced Hyper-Parameter Optimization/Tuning - Nov 17, 2020.
  
  In informed search, each iteration learns from the last, whereas in Grid and Random, modelling is all done at once and then the best is picked. In case for small datasets, GridSearch or RandomSearch would be fast and sufficient. AutoML approaches provide a neat solution to properly select the required hyperparameters that improve the model’s performance.

  Automated Machine Learning, AutoML, Hyperparameter, Optimization, Python

• Uber Open Sources the Third Release of Ludwig, its Code-Free Machine Learning Platform - Oct 13, 2020.
  
  The new release makes Ludwig one of the most complete open source AutoML stacks in the market.

  Automated Machine Learning, AutoML, Machine Learning, Open Source, Uber

• Build Your Own AutoML Using PyCaret 2.0 - Aug 20, 2020.
  
  In this post we present a step-by-step tutorial on how PyCaret can be used to build an Automated Machine Learning Solution within Power BI, thus allowing data scientists and analysts to add a layer of machine learning to their Dashboards without any additional license or software costs.

  Automated Machine Learning, AutoML, Power BI, PyCaret, Python

• Autotuning for Multi-Objective Optimization on LinkedIn’s Feed Ranking - Aug 19, 2020.
  
  In this post, the authors share their experience coming up with an automated system to tune one of the main parameters in their machine learning model that recommends content on LinkedIn’s Feed, which is just one piece of the community-focused architecture.

  Automated Machine Learning, AutoML, LinkedIn, Machine Learning, Optimization

• Top Google AI, Machine Learning Tools for Everyone - Aug 18, 2020.
  
  Google is much more than a search company. Learn about all the tools they are developing to help turn your ideas into reality through Google AI.

  AI, AutoML, Bias, Data Science Platforms, Datasets, Google, Google Cloud, Google Colab, Machine Learning, TensorFlow

• GitHub is the Best AutoML You Will Ever Need - Aug 12, 2020.
  
  This article uses PyCaret 2.0, an open source, low-code machine learning library in Python to develop a simple AutoML solution and deploy it as a Docker container using GitHub actions.

  Automated Machine Learning, AutoML, GitHub, PyCaret, Python

• Wrapping Machine Learning Techniques Within AI-JACK Library in R - Jul 17, 2020.
  
  The article shows an approach to solving problem of selecting best technique in machine learning. This can be done in R using just one library called AI-JACK and the article shows how to use this tool.

  Automated Machine Learning, AutoML, Machine Learning, Modeling, R

• Top KDnuggets tweets, May 13-19: Linear algebra and optimization and machine learning: A textbook - May 21, 2020.
  
  Also: Everything you need to become a self-taught #MachineLearning Engineer ; SQL Cheat Sheet (2020) - a useful cheat sheet that documents some of the more commonly used elements of SQL;

  AutoML, Cheat Sheet, Linear Algebra, Machine Learning Engineer, SQL, Top tweets

• Automated Machine Learning: The Free eBook - May 18, 2020.
  
  There is a lot to learn about automated machine learning theory and practice. This free eBook can get you started the right way.

  Automated Machine Learning, AutoML, Free ebook, Machine Learning

• Will Machine Learning Engineers Exist in 10 Years? - May 8, 2020.
  
  As can be common in many technical fields, the landscape of specialized roles is evolving quickly. With more people learning at least a little machine learning, this could eventually become a common skill set for every software engineer.

  Advice, AutoML, Career, Machine Learning Engineer, Trends

• State of the Machine Learning and AI Industry - Apr 16, 2020.
  
  Enterprises are struggling to launch machine learning models that encapsulate the optimization of business processes. These are now the essential components of data-driven applications and AI services that can improve legacy rule-based business processes, increase productivity, and deliver results. In the current state of the industry, many companies are turning to off-the-shelf platforms to increase expectations for success in applying machine learning.

  AI, AutoML, Data Science Platform, Industry, Machine Learning

• When Will AutoML replace Data Scientists? Poll Results and Analysis - Mar 16, 2020.
  
  Will AI always be 5-10 years away? The majority of respondents to this poll think that AutoML will reach expert level in 5-10 years. Interestingly, it is about the same as 5 years ago. We examine the trends by AutoML experience, industry, and region.

  Automated Data Science, AutoML, Humans vs Machines, Poll, Trends

• Hands on Hyperparameter Tuning with Keras Tuner - Feb 28, 2020.
  
  Or how hyperparameter tuning with Keras Tuner can boost your object classification network's accuracy by 10%.

  Automated Machine Learning, AutoML, Keras, Python

• The Death of Data Scientists – will AutoML replace them? - Feb 20, 2020.
  
  Soon after tech giants Google and Microsoft introduced their AutoML services to the world, the popularity and interest in these services skyrocketed. We first review AutoML, compare the platforms available, and then test them out against real data scientists to answer the question: will AutoML replace us?

  AutoML, Data Scientist, Trends

• Practical Hyperparameter Optimization - Feb 13, 2020.
  
  An introduction on how to fine-tune Machine and Deep Learning models using techniques such as: Random Search, Automated Hyperparameter Tuning and Artificial Neural Networks Tuning.

  Automated Machine Learning, AutoML, Deep Learning, Hyperparameter, Machine Learning, Optimization, Python, scikit-learn

• Amazon Gets Into the AutoML Race with AutoGluon: Some AutoML Architectures You Should Know About - Jan 30, 2020.
  
  Amazon, Microsoft, Salesforce, Waymo have produced some of the most innovative AutoML architectures in the market.

  Automated Machine Learning, AutoML, Deep Learning, Machine Learning

• AutoML Poll results: if you try it, you’ll like it more - Jan 27, 2020.
  
  The results of latest KDnuggets Poll on AutoML are quite interesting. While most respondents were not happy with AutoML performance, the opinions of those who tried it were higher than those who did not.

  Automated Machine Learning, AutoML, Poll

• Top 10 Technology Trends for 2020 - Jan 16, 2020.
  
  With integrations of multiple emerging technologies just in the past year, AI development continues at a fast pace. Following the blueprint of science and technology advancements in 2019, we predict 10 trends we expect to see in 2020 and beyond.

  2020 Predictions, AI, AutoML, Baidu, Blockchain, IoT, NLP, Quantum Computing, Research

• H2O Framework for Machine Learning - Jan 6, 2020.
  
  This article is an overview of H2O, a scalable and fast open-source platform for machine learning. We will apply it to perform classification tasks.

  Automated Machine Learning, AutoML, H2O, Machine Learning, Python

• Automated Machine Learning: How do teams work together on an AutoML project? - Jan 2, 2020.
  
  In this use case, available to the public on GitHub, we’ll see how a data scientist, project manager, and business lead at a retail grocer can leverage automated machine learning and Azure Machine Learning service to reduce product overstock.

  Automated Machine Learning, AutoML, Azure ML, Data Science Team, Microsoft, Microsoft Azure

• The 4 Hottest Trends in Data Science for 2020 - Dec 9, 2019.
  
  The field of Data Science is growing with new capabilities and reach into every industry. With digital transformations occurring in organizations around the world, 2019 included trends of more companies leveraging more data to make better decisions. Check out these next trends in Data Science expected to take off in 2020.

  2020 Predictions, Automated Data Science, AutoML, Cloud Computing, Data Science, NLP, Privacy, Security, Trends

• AutoML for Temporal Relational Data: A New Frontier - Oct 30, 2019.
  
  While AutoML started out as an automation approach to develop optimal machine learning pipelines, extensions of AutoML to Data Science embedded products can now enable the processing of much more, including temporal relational data.

  AutoML, KDD, Temporal Data, Time Series

• Can we trust AutoML to go on full autopilot? - Jul 31, 2019.
  
  We put an AutoML tool to the test on a real-world problem, and the results are surprising. Even with automatic machine learning, you still need expert data scientists.

  Automated Machine Learning, AutoML, Overfitting, Time Series

• 3 Reasons Why AutoML Won’t Replace Data Scientists Yet - Mar 6, 2019.
  
  We dispel the myth that AutoML is replacing Data Scientists jobs by highlighting three factors in Data Science development that AutoML can’t solve.

  Automated Machine Learning, Automation, AutoML, Data Scientist, Feature Engineering, Reinforcement Learning

• Automatic Machine Learning is broken - Feb 19, 2019.
  
  We take a look at the arguments against implementing a machine learning solution, and the occasions when the problems faced are not ML problems and can perhaps be solved using optimization, exploratory data analysis tasks or problems that can be solved with simple statistics.

  Automated Machine Learning, AutoML, Data Preparation, Deployment

• Building AI to Build AI: The Project That Won the NeurIPS AutoML Challenge - Jan 23, 2019.
  
  This is an overview of designing a computer program capable of developing predictive models without any manual intervention that are trained & evaluated in a lifelong machine learning setting in NeurIPS 2018 AutoML3 Challenge.

  AI, Automated Machine Learning, AutoML, Gradient Boosting, Hyperparameter, NeurIPS

• Automated Machine Learning in Python - Jan 18, 2019.
  
  An organization can also reduce the cost of hiring many experts by applying AutoML in their data pipeline. AutoML also reduces the amount of time it would take to develop and test a machine learning model.

  Automated Machine Learning, AutoML, H2O, Keras, Machine Learning, Python, scikit-learn