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  • Writer's pictureDhruv Parmar

Preparing Data for AWS Machine Learning Models

Introduction Data is the new oil of the modern business world. Organizations that can transform huge volumes of business data into meaningful insights can be successful in achieving their goals. Similarly, with machine learning solutions, quality data is a crucial building block for the project’s success. Quality data helps build the right foundation for developing and finetuning the machine learning (ML) model. AWS Machine Learning Engineers work closely with the IT team and data management team members to ensure the data is prepared with utmost care. In this write-up, you will find out what data preparation is and why it is essential to prepare data before feeding it to the AWS Machine Learning model. You are also introduced to AWS training and certification courses to teach professionals to build machine learning solutions in the cloud.

What is data preparation, and why is it important? Talk to any machine learning expert, and you will understand the significance of the data preparation phase in the machine learning projects. The accuracy of your machine learning models depends on both the quality and quantity of the data fed to the system. Data preparation involves problem definition, data collection, cleaning and validation of data, data structuring, and feature engineering. Through the process, IT professionals and data management professionals convert raw data into a readable and understandable form to the machine learning algorithm. There are several reasons why data must be prepared before it is fed to your machine learning algorithm for training purposes. Three main reasons are listed below:

  • Identify incomplete records in a dataset and clean them up before it is fed to the algorithm. It improves the accuracy of the results.

  • Validate data to remove records with unexpected values from the dataset. It reduces the chances of misleading outcomes from the machine learning model.

  • The team may have to structure the data, especially when it collects data from different sources. Structuring can make the data more readable and understandable to the machine learning algorithm.

If you are new to the machine learning concept and want to learn about the fundamentals, take the AWS Machine Learning Basics course. Learn about the machine learning pipeline and how Machine Learning Specialists apply it to real-world problems. How do AWS Machine Learning Engineers prepare data for ML Models? Data is unique for every machine learning project, and so is the process for preparing data. Since the data collection sources can be different for each project, the data process chosen for preparing data can differ from one case to another. Generally, data preparation or data preprocessing with AWS involves the following phases.

  • Cleaning: In this phase, the AWS Machine Learning Engineer ensures that the anomalies, irrelevant data, and records with missing data are cleaned up to reduce the gap between the expected and actual outcome.

  • Segregation: The IT or data management team responsible for preparation segregates the data into trains and validates them. The Machine Learning Engineer needs to prevent data leakage and ensure quality.

  • Scaling: It is necessary to maintain the varying magnitudes of data in the dataset. Through scaling, the machine learning engineer can see that the ML model gives equal importance to each feature in the dataset.

  • Balancing: It is important to prevent biases and inaccuracies in predicted outcomes by addressing the issues with data or algorithms.

  • Augmentation: This process artificially boosts the data available to the machine learning model by synthesizing new data.

Machine learning models must be monitored continuously to ensure that they are predicting outcomes as expected. Read the blog on the importance of AWS Machine Learning monitoring to learn more. How to start a career as an AWS Machine Learning Specialist? Machine learning has linear algebra, probability, and statistics at its core, but today, thanks to cloud computing and software, IT teams can develop ML models with limited knowledge of the core subjects. Modern-day Machine Learning Engineers, Machine Learning Specialists, and Data Scientists require proper technical training and skills to build machine learning models and knowledge of core machine learning concepts. Preparing for AWS certification through proper training can help individuals acquire the ability to succeed in machine learning careers. Some of the fundamental skills of machine learning specialists today are: • Good understanding of data engineering principles • Sound knowledge of data preparation methodologies • Ability to understand the mathematical aspects of the algorithms • Strong knowledge of various machine learning models and their applications AWS certified Machine Learning Specialty certification is one of the industry’s highly recognized machine learning credentials in the today. Aspirants for the certification must take the MLS-C01 examination and pass it to get AWS certified. The exam covers 4 domains of machine learning: data engineering, exploratory data analysis, modeling and machine learning implementation and operations. Professionals must have at least two years of hands-on experience in developing machine learning solutions in the AWS cloud. Besides, professionals must also have experience in hyperparameter optimization and implement the best practices in ML training and deployment. Professionals from the development and data science backgrounds can take the Machine Learning Pipeline on AWS to start preparing for a career in Machine Learning specialization. The 4-days course helps professionals to apply machine learning knowledge and skills to real-world problems. They get exposure to various ML approaches, ML model training, evaluation, and deployment processes and learn the best practices for scaling and optimizing machine learning pipelines.

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