Gathering Machine Learning Data
Data gathering is one of the most critical processes in the machine learning workflows. The usefulness and accuracy of your project are determined by the quality of the data you collect during data collecting.
Data pre-processing
You must first identify your sources before combining data from various sources into a single dataset. Streaming data from the Internet of Things sensors, collecting open-source data sets, or constructing a data lake from multiple files, for example, are all possibilities.
Building Datasets
The processed data is separated into three datasets during this step: training, validating, and testing:
Training set – It was created to educate the algorithm on how to analyze data and train it. The parameters define model classes in this collection.
Validation set – This is used to calculate the accuracy of the model. The parameters of the model are adjusted using this dataset.
Test set – It is used to assess the accuracy and performance of the models. The goal of this collection is to draw attention to any defects or mistrainings in the model.
Training and refinement
You may start training your model after you have your datasets. This requires giving your algorithm its training data to learn the correct classification parameters and features.
You may utilize your validation dataset to improve the model once it has been trained. Changing or eliminating variables and fine-tuning model-specific settings (hyperparameters) may be necessary until an acceptable level of accuracy is obtained.
Machine Learning Evaluation
Finally, once you’ve found a good set of hyperparameters and improved the accuracy of your model, you can put it to the test. Testing takes use of your test dataset to confirm that your models have accurate features. Based on the feedback you get, you may return to training the model to improve accuracy, change output parameters, or deploy the model as needed.
Automating Machine Learning Workflow
Teams can speed up some of the repetitive tasks involved with model construction by automating machine learning operations. This is also known as autoML, and there are various modules and platforms available for it.
What is Automated Machine Learning?
To develop new models, AutoML applies existing machine learning algorithms. Its purpose isn’t to completely automate the modelling process. Instead, the goal is to reduce the number of interventions humans must do for development to be successful. Basically we are coming close to the era of ‘machines making machines’ How cool is that?
Developers can start and finish projects considerably faster with AutoML. It may also enhance deep learning and unsupervised machine learning training processes, allowing created models to self-correct.
What can you Automate?
While it would be great to automate every aspect of machine learning, this is currently not possible. The following are examples of things that can be reliably automated:
Hyperparameter Optimization – To test and decide the optimal combination of pre-defined parameters, it uses techniques such as grid search, random search, and Bayesian approaches.
Model Selection – In this strategy, the same dataset is run through many models with default hyperparameters to see which one is most suited to learning from your data.
Feature Selection – The tools select the most critical aspects from pre-defined sets of features in this stage.
3 Frameworks You Can Use to Automate Machine Learning Workflows
Featuretools
Featuretools is an open-source feature engineering automation platform. It can transform structured temporal and relational information using a Deep Feature Synthesis approach. Using primitives, this approach gathers or converts data into usable attributes (operations like sum, mean, or average). This system is based on Max Kanter and Kalyan Verramachaneni’s Data Science Machine project at MIT.
DataRobot
The DataRobot platform automates data preparation, feature engineering, model selection, training, testing, and deployment. It may be used to find new data sources, implement business rules, and reorganize and regroup data.
You may develop your model implementation using the DataRobot platform’s selection of open source and proprietary models. It also includes a graphics-based dashboard for analyzing your model and comprehending forecasts
tsfresh
tsfresh is a Python open-source module for calculating and extracting time series data characteristics. It enables the extraction of features that may then be used in training using scikit-learn or pandas.
The versatility of the Machine Learning Workflow sets it apart. Instead of pursuing clearly defined goals, the feasibility evaluation provides the choices. Until recently, it has been unclear which results are possible with the data provided and which requirements the implementation entails. As a result, each phase has its own set of challenges and opportunities. Moreover, if you are really interested in Machine Learning and looking to pursue it as a career then you can try out this Ärtificial Intelligence and Machine Learning E-Degree” to get a deep dive into the subject. We wish you all the best!
Also Read: Machine Learning vs Deep Learning – What Makes Them Different
