The way to go-Data Analysis
MUST KNOW! Features of Automated Machine Learning
When you are building with the models of machine learning, you follow some best practices which are time-consuming but the important process. There are so many things for doing ranging from: data is being prepared, algorithms are selected and trained, preparing algorithms, understanding how decisions are being made, all the way down to deploying models to production.
But, if you are willing to save time, increase accuracy, and reduce risk, you will not manually go through the entire machine learning process in order to build my machine learning models. And, to get the most out of automated machine learning, for automating each and every one of the 10 steps go through the information. So, here's the guide to what to look for in an automated machine learning system.
Step 1: Preprocessing of Data
This data mining technique involves the raw data transformation into a format which is understood. Each algorithm has different working and the requirement of the data is also different. For example, some algorithms need numeric features to be normalized, and some do not. Then comes the complicated text, where splitting is necessary for the words and the phrases and some languages also like Japanese which is difficult. The platform of machine learning which is automated, look for that which already knows how to prepare data for multiple algorithms, the text is being prepared and recognized and for the partitioning data it follows best practice.
Step 2: Feature Engineering
In this process, data is being altered for helping the algorithms of machine learning to have a better working, which is often time-consuming and costly. Business rules and the domain knowledge is needed for some of the feature engineerings as most feature engineering is generic. Look for an automated machine learning platform that can automatically engineer new features from existing numeric, categorical, and text features.
Step 3: Diverse Algorithms
Unique information is contained by every dataset which demonstrates the individual events and characteristics of a business. Due to the multiple situations and conditions, every single possible business problem or the dataset cant be solved by one algorithm. Because of this, we need access to a diverse repository of algorithms to test against our data, in order to find the best one for our particular data. The machine learning platform having the hundreds of algorithm, go for it. Ask how often new algorithms are added.
Step 4: Algorithm Selection
Having multiple algorithms is really nice but sometimes you do not have much time to try each and every algorithm on your data or many times people are not that much patience. There are algorithms which do not suit your data, and some are there which suites the data sizes and many are unlikely to work well on your data. Look for an automated machine learning platform that knows which algorithms make sense for your data and runs only those. That way you will get better algorithms, faster.
Step 5: Training and Tuning
For the software of machine learning, it is quite standard to train the algorithm on your data. There is a hyperparameter tuning to worry about. Then there is the selection of the feature, for improving the speed as well as the accuracy of the model. The machine learning platform using the smart hyperparameter tuning, not the brute forces and knows the leading hyperparameters for tuning the particular tuning. Check whether the platform has the information about including the feature and which ones are to be left out and which feature selection method works well for the multiple algorithms.
Step 6: Ensembling
In data science jargon, teams of algorithms are called ensembles or blenders. Each algorithms strengths balance out the weaknesses of another. Ensemble models typically outperform individual algorithms because of their diversity. Look for an automated machine learning platform that finds the optimal algorithms to blend together, includes a diverse range of algorithms, and tunes the weighting of the algorithms within each blender.
Step 7: HeadtoHead Model Competitions
You won't know in advance which algorithm performs best on your data. So, you need to compare the accuracy and speed of different algorithms on your data, regardless of which programming language or machine learning library they came from. You can think of it as being like a competition amongst the models, where the best model wins! Look for an automated machine learning platform that builds and trains dozens of algorithms, compares the results, and ranks the best algorithms based on your needs. The platform should compare accuracy, speed, and individual predictions.
Step 8: human-friendly Insights
Over the past few years machine learning and artificial intelligence have made massive strides forward in predictive power but at the price of complexity. For machine learning, it is not enough to score well on the accuracy and the speed. You also have to trust the answers it is giving. In regulated industries, you have to justify the model to the regulator. And in marketing, you need to align the marketing message with the audience the model has chosen. Look for an automated machine learning platform that explains model decisions in a human-interpretable manner. The platform should show which features are most important for each model and show the patterns fitted for each feature.
Step 9: Easy Deployment
A recent Harvard Business Review article described a team of analysts that built an impressive predictive model, using the latest in machine learning algorithms. But, the business lacked the infrastructure needed to directly implement the trained model in a production setting, and the model was too complex for the IT team.
Look for an automated machine learning platform that offers easy deployment, including one-click deploy, that can be operated by a business person. Also, check whether the vendor has a large technical support team located all around the world that can provide data science and engineering support 24 hours per day.
Step 10: Model Monitoring and Management
In a constantly changing world, your AI applications need to keep up to date with the latest trends. Look for an automated machine learning platform that proactively identifies when a modelĂ?Â˘??s performance is deteriorating over time, making it easy to compare predictions to actual results, simplifying the task of training a new model on the latest data.