AutoML is a type of machine learning (ML) in which the tasks and processes for developing learning models for machines are automated rather than iteratively built by developers. As enterprises collect more data than any human could tackle, autoML helps by building ML models quickly and at scale. These tasks have become increasingly complex, and as more businesses adopt ML applications, the demand for ML experts and data scientists far outpaces enterprises' ability to hire them. Out-of-box autoML solutions, such as Auto-sklearn and Auto-PyTorch, have thus become more commonplace because autoML is more accessible to those with little or no coding experience. Since autoML automates tasks involved to optimize machine learning models and develop deep neural networks, this reduces the chance of error from human intervention.

I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020.

I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020.

I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020.

I do really appreciate these helpful on-hand labs and lectures. Passed the DP-100 in Dec 2020.

Machine learning has been used successfully in many disciplines that increasingly depend on it. However, the success relies on human machine learning experts to perform many tasks, according to an account on AutoML.org, a website of the community. These tasks include: Preprocessing and cleaning the data; selecting and constructing appropriate features; selecting an appropriate model family; optimizing model hyper parameters; post-processing machine learning models; and critically analyzing the results. The growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used more easily and without necessarily expert knowledge. The goal is to progressively automate these manual tasks in what is being called AutoML.

This past semester we have been collaborating on a machine learning Capstone Project with Columbia University's Master of Science in Applied Analytics: capstone projects are applied and experimental projects where students take what they have learned throughout the course of their graduate program and apply it to examine a specific area of study. Capstone projects are specifically designed to encourage students to think critically, solve challenging data science problems, and develop analytical skills. Two group of students built an end-to-end data science solution using Azure Machine Learning to accurately forecast sales. Azure Machine Learning is a cloud-based environment that you can use to train, deploy, automate, manage, and track ML models. Azure Machine Learning can be used for any kind of machine learning, from classical machine learning to deep learning, supervised, and unsupervised learning.

At last week's Microsoft Ignite conference in Orlando, our team delivered a series of 6 talks about AI and machine learning applications with Azure. The videos from each talk are linked below, and you can watch every talk from the conference online (no registration necessary). Each of our talks also comes with a companion Github repository, where you can find all of the code and scripts behind the demonstrations, so you can deploy and run them yourself. If you'd like to see these talks live, they will also be presented in 31 cities around the world over the next six months, starting with Paris this week. Check the website for Microsoft Ignite the Tour for event dates and further information.

Automated machine learning pre-processing steps (feature normalization, handling missing data, converting text to numeric, etc.) become part of the underlying model. When using the model for predictions, the same pre-processing steps applied during training are applied to your input data automatically.

This will enable more people in your organization to leverage machine learning and most importantly allow domain experts to rapidly prototype ML solutions and validate their hypothesis before involving data scientists. If you are an experienced data scientist, automated ML will let you improve productivity and save time by eliminating the need to manually perform the tedious and repetitive tasks of feature engineering, algorithm selection and hyperparameter tuning. You can even start by generating a model with automated ML as a starting point and tune it further. Organizations can also use automated ML to benchmark their models. Many Fortune 500 customers are benefiting from using automated ML. These include a global oil & refinery enterprise that's using automated ML to forecast reservoir production and a medical devices company that's using automated ML for predictive maintenance. Automated ML also powers Microsoft Power BI's AI capabilities, where business analysts can build machine learning models without writing a single line of code. Azure Machine Learning service's automated ML capability is based on a breakthrough from our Microsoft Research division and different from competing solutions in the market. The approach combines ideas from collaborative filtering and Bayesian optimization to search an enormous space of possible machine learning pipelines intelligently and efficiently.