Goto

Collaborating Authors

New automated machine learning capabilities in Azure Machine Learning service Blog Microsoft Azure

#artificialintelligence

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.


Insights on Data Science Automation for Big Data and IoT Environments - DZone IoT

#artificialintelligence

Data Science sits at the core of any analytical exercise conducted on a Big Data or Internet of Things (IoT) environment. Data science involves a wide array of technologies, business, and machine learning algorithms. The purpose of data science is just not doing machine learning or statistical analysis but also to derive insights out of the data that a user with no statistics knowledge can understand. In a fast paced environment such as Big Data and IoT where the type of data might vary over the course of time, it becomes difficult to maintain and recreate the models each and every time. This gap calls up for an automated way to manage the Data Science algorithms in those environments.


Data Science Automation For Big Data and IoT Environments

#artificialintelligence

Data science sits at the core of any analytical exercise conducted on a big data or Internet of Things (IoT) environment. Data science involves a wide array of technologies, business, and machine-learning algorithms. The purpose of data science is not only to do machine learning or statistical analysis, but also to derive insights out of the data that a user with no statistics knowledge can understand. In a fast-paced environment such as big data and IoT, where the type of data might vary over the course of time, it becomes difficult to maintain and re-create the models each and every time. This gap calls for an automated way to manage the data-science algorithms in those environments.


GitHub Repo Raider and the Automation of Machine Learning - KDnuggets

#artificialintelligence

GitHub is a clearinghouse for all sorts of open source projects, including those for machine learning, automated and otherwise. More specifically, automated machine learning is the use of automated techniques, be they learned methods or simple heuristics, used for algorithm selection, hyperparameter tuning, architecture design, or any other conceivable portion of a machine learning implementation. Switching gears, Indiana Jones is one of the greatest characters to ever grace the silver screen. Raiders of the Lost Ark, the first movie in which the character was featured, is a personal favorite, film adored by millions. The rest of the (current) quadrilogy movies run alternately hot and cold, but even the poorest quality Indiana Jones is better than 95% of available cinema.


Automated Machine Learning for Professionals - Updated

#artificialintelligence

Summary: As the Automated Machine Learning (AML) movement got underway a few years back there was an early branch between proprietary platforms and open source platforms. Since they continue to require fluency in Python or R we label them "professional". As the Automated Machine Learning (AML) movement got underway a few years back there was an early branch between proprietary platforms and open source platforms. Today, the primary difference between these is that the proprietary entries are largely code-free so that citizen data scientists / business analysts can use them in addition to data scientists. The open source versions are still reliant on your ability to code, or at least to copy code.