Last updated on January 14, 2020
Machine Learning is nothing but one of the subdomains of science that deals with computers or applications that are not explicitly coded to perform the task. The combination of machine learning, cognitive technology, and AI will make a lot more smooth the processing of big chunks of data and information.
Machine Learning is an application of AI (Artificial Intelligence) which ables the machines or software to adapt, learn from itself, provided the data is resourceful and sensible. Simply saying the efforts are implying to develop expert systems.
Since it delivers at a faster rate with better and more accurate results, machine learning is brought into practice. The engineers work day and night to predict, classify, cluster the data. The player Machine Learning is sent on the pitch of data, and Big Data to handle the problems.
Automated Machine Learning
The word automation in English means that to do work involving more than one task timely and precisely and unleashes or relieves us from mundane tasks. Machine Learning is all about making systems to know their potential to overcome these rote tasks and to enhance the power of ML, there landed up AutoML. Automated machine learning figures out the optimized techniques to the results of learning the question, ‘How?’ to accomplish those repetitive duties.
This study involves a mixture of more than one Machine Learning model into one. For classification, we may have a random forest, decision trees, or SVC (Support Vector Classification) merged into one to have better outcomes.
The whole machinery revolves around one bright star equation:
With developing more models all the time, it’s becoming hectic for thinkers to choose. The choice is getting difficult, so the merging of some best-known algorithms into one is being done to achieve the goals.
ML and AutoML algorithms make the best use of Python which ultimately gives the best end results. However, Python and R, are also popular to work with Data Science as well. Creative minds can also choose Python for Data Science course for tonnes of reasons as the learning will be a big plus.
It’s a general-purpose language, can be used and dwell into any framework and makes a good match with the robust technologies (ML, AutoML, Data Science, Web Development etc.) of today whether it is to deal with data prediction, classification, and clustering.
The problems are answered way before one bangs the door. In no time the solution is presented on your table, and most of the times the solution is garnished well.
Having acknowledged with the basics, now let me familiarize with some positives and negatives of Automated Machine Learning which come along with it.
Pros and Cons of Automated ML
- Automated machine learning provides the solution and shoots to automate few or all the steps of ML. This enables the seeker to implement supervised learning, which involves recognizing patterns from the labeled data.
- Automated ML does take care of the quality and accuracy of the model (algorithms) so developed after applying autoML techniques. The chances of a mistake or the error occurring are reduced indeed. Thus, AutoML provides a higher amount of satisfaction rates.
- It comes with one more benefit of enhanced cycle time. The data processing time is reduced and is saved, so it's a sigh for the developers to invest this time in some other phases, like taking care of the optimization functions in the AutoML model.
- Simplicity and flexibility are another plus in AutoML. Obviously, it’s crystal clear that once the hectic task of mining, wrangling or processing data is over, the job becomes a bit relaxing, simple, and flexible.
- Don’t forget the great control and handling of your supercar AutoML. Intelligent automation brings in a better solution to the mundane task of data handling because the labor remains in-house and gives the least chances of rework.
- Automated Machine Learning helps process the datasets by selecting, extracting and engineering the features of the dataset, along with hyperparameter optimization.
- The AutoML methods enables data science to make proper use of machine learning, in order to invent powerful technologies to handle Big Data.
- Accuracy is measured well in machine learning but automated machine learning is one step ahead and fine-tunes the data more effectively and reduces the error rate more precisely.
- Additionally, AutoML:
- Is going to be cost-effective.
- Spikes the number of developers as Data Scientists.
- Generates higher profits and better revenues for companies along with high customer satisfaction.
- Uses fewer resources to uphold the performance, saves a lot of GPUs and CPUs resulting in Power-Efficiency.
Some Benign Cons:
- Congruence to flexible specifications:
Most AutoML tools emphasize the performance but in the real world, that’s just one aspect being covered in machine learning projects. So the companies can’t compromise the computing plus storage specification sheet.
- Model Performance: Again you simply can’t turn your face away or show your back to the human intelligence embedded in machine learning models alone. On Kaggle, there are a number of developers who beat the programming of the latest AutoML tools with their unbeatable wisdom.
- Congruence to flexible specifications:
Real World Applications of AutoML
The idea behind the concept is if any two data sets are found with some correlation or having similarities. For this, we require at least two data sets only then we’d be able to figure out similarities.
Unlike Azure AutoML, Google has kept enclosed in braces, it's not an open source but huge yes to cloud-based. Has the support of few classification algorithms CNN, RNN, LSTM.
It is an Open Source environment with no availability of cloud services. Supports CNN, RNN, LSTM in classification field. The technique applied in this auto model is, “Efficient Neural Architecture Search with Network Morphism.” The training framework, of course, it’s the same Keras.
The ideology of building this framework is the same as that of Google AutoML. A candidate architecture in RNN controller is trained with samples. The child model is then trained, to measure the performance of the desired tasks.
This is open source but not cloud-based. Here regression and classification are both incorporated. The techniques used in Auto-sklearn are Bayesian optimization and automated ensemble construction. The sklearn framework keeps playing the game. The CASH definition holds the roots of this plant, i.e., Combined Algorithm Selection and Hyperparameter optimization.
The concept of Auto-sklearn is same as that of Azure Automated ML. Simultaneously, selecting a learning algorithm, along with setting its hyperparameters is considered to the problem. The major difference between both of them is that it incorporates a meta-learning step in the starting and an automated ensemble construction step at the end.
The Final Say
The nascent AutoML has just started its course. While faced with some small imperfections, I believe that they are only ephemeral and AutoML will win the game soon.
Human Wizards are in their final year at Hogwarts school of machine learning. Once graduated they will make remarkable contributions in the digital world by changing the industry norms, thus benefiting the whole mankind.
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