An automation learning system is a rather modern twist in artificial intelligence lookout that represents a shift in the system and how organizations approach machine learning with data sciences changing continuously. Application of traditional old school machine learning methodologies and learning models has a time consuming, resource intensive challenges when applied in real scenarios. This makes the processing very difficult and requires much professional data scientist. But with the help of automation machine learning, this can very much change.
This makes building and using of machine learning models easy in the actual practice through running systemic process on the raw data and selecting the model which can pull off the most relevant data and segregate them. Automation learning system incorporates the best practices in the field of machine learning to make the available data more accessible throughout the organization like social media. Automation of different steps enables developers to have the powerful ability to solve problems and utilize them in the data science so that desires result come up without extensive programming knowledge.
What is automation learning
An automation learning system is basically a trainable machine learning system which is self-adjusting in nature, also whose basic control algorithm keeps on changing with the evolution of the results so that if the time progresses the machine can improve itself in terms of functioning, features, and quality. The basic technological system in automatic learning is designed with the data of prior information of the process that is occurring within the system. This in turn, accompanies the system operations and uses it to distinguish the effects on it. Whenever this initial information is in the complete form it becomes possible to know the value in the most precise way so that it can be ensured that the performance is up to the desired level. In this case, there is no repetitive necessity to train the machine. But if the initial information or path is not defined and is incomplete then the formation of principle system is required to be incorporated with the training of system during the development phase.
Automation machine learning is a general system that involves the automating of any part within the process of application in machine learning like robots. Automation machine learning is also known as automated machine learning.
Why is automated learning so crucial?
When we construct a machine learning module manually then it has very cumbersome multi-step within it, the whole process requires domain knowledge, programming skills, and mathematical skills. That is a huge amount of knowledge to expect from a single person. Not only is this but in this whole process, there is always a space of human errors and biases which hampers the accuracy of the system model. An automated machine learning system enables a particular organization to utilize the knowledge of data scientist without actually developing the capabilities within them. This helps in improving the result and reduces the time, energy and finances in a project.
Uses of automatic learning system are increasing in many sectors and its collateral industries like healthcare and pharmaceutical or Fintech and banking. It’s no more restricted to only big industries that have larger resources and ability to process the database. Automated machine learning has allowed better penetration of artificial intelligence and machine learning on a larger scale. By automating the system we can revolutionize most of the manual modeling system and task which are quite important in terms of developing better machine learning protocols, this enables the business fraternity to use the machine learning and its solution in the complex problems too.
When to use automated machine learning
The basic fundamental idea of using automated machine learning is to create a mode which empowers the user in such a manner that whether they have a data scientist or not they will be able to have issue detection and identification of end to end problems.
- When the implementation of machine learning solutions are limited and without any extensive programming knowledge.
- When there are limited resources and time, need to be finished in a given deadline.
- Leverage of the data science within the parameters of best available practices.
- Provide agile solution for the problems.
How does it work
Automated machine learning system works 30% faster than the simpler machine learning system. An automated machine learning system can analyze the data and come up with the solution which is better and faster almost 100 times more than humans. This will ultimately help in the business potentially exploring the task completion faster and easier.
- Identify the problem, classify them and have a regression.
- Specify the main source and the format of the training data that is labeled.
- Configure the target for the model training system such as azure learning, remote VM.
- Configure the parameters of automated machine learning which ultimately determine the intentions of the working of different models, settings of the hyper-parameters, preprocessing which is much advanced in nature and understanding what are the required metrics to find out the best working model. You can also configure the required settings for the training module in the Azure portal.
- Submit the training
During training, a machine learning module creates a number of parallel pipelines along with a combination of different algorithms and parameters. This can be stopped once you hit the exit.
Tools for automated machine learning
According to a survey, more than 45% of the data by the year 2020 will be automated which will lead to increase productivity of data Scientist as well as an analyst. An automated tool is useful for the multi-purpose as they offer point to click interference for loading of the data and building up a better machine learning module. More and more tools are being developed on the basis of focus on model building rather than entire process automation. This allows better business-oriented customer analytical system.
As far as machine learning is concerned it worked on two major concepts that are neural architect and transfer learning
1. Neural architecture search
This is the search process of automating the design of neural networks. Mostly this concept utilizes the reinforcement learning or evolutionary algorithm to design the networks. This model is not much known for its accuracy and strives to larger data files comparatively.
2. Transfer learning
As the name suggests, this one is the technique used for the pre-trained models to just transfer what is recently learned or added while applying the model to build a new model but with a similar database. This enables the model to have high accuracy with less computation time as well as power. This is good for the modules which require the formation of the new architect while transferring the learned files similar to the one used while pertaining the previous model.
How companies are using it
Many larger companies are nowadays using the automation module to automate the internal system processes specifically the machine learning models. For instance, Facebook and Google.
Facebook trains and tests a staggering numbers of machine learning models every month which approximately ranges to more than 3,00,000. The company is building up an automated machine learning system to assemble a line of action to deal with multiple models in one go. Facebook has taken a step ahead and had developed and introduced its own automated machine learning system having own engineers that work to improve the existing models of automatically generated machine learning projects.
Also, Google is developing its own automated machine learning techniques and technology for automating the older version of machine learning models. This also includes the process of optimizing methods and their applications. Company is working hard with its engineers to develop a machine learning enable architects.
The major application of automated learning is to make machine learning more understandable by automatically generating data which may include pre-processing of the data and feature engineering long with the procedural parameters and protocol of machine learning, this makes the use of powerful technology accessible for those who wish to use the data.
Some of the automated learning techniques and their tools are designed in such a way that they can aggregate the data from various sources. Processing of this data and information is the main attraction of automated machine learning however extraction in yet another dynamic feature of it which cannot be ignored. Both collectively make the utilization of machine learning broader and easier. No doubt automated machine learning is in boon currently and lot more will continue to develop in upcoming years too.
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