As a Data Scientist, undergoing a Machine Learning Engineer training can enable you to learn how to deploy machine learning models into production. Discover the reasons behind this and how to embark on this training journey with DataScientest.
Machine Learning (ML) and artificial intelligence (AI) are increasingly utilized across businesses in various sectors, serving automation of manual tasks and data analysis purposes. Algorithms play a pivotal role in addressing classification, recommendation, and anomaly detection challenges.
In this landscape, organizations seek professionals capable of harnessing this emerging technology. Data Scientists are among the experts equipped with relevant knowledge, proficient in developing ML and Deep Learning algorithms. However, they might not always possess the skills to deploy models into production.
For this reason, more and more companies are turning to Machine Learning Engineers or ML Engineers. This new specialization within the realm of Big Data emerged with the rise of ML, focusing on managing infrastructures, monitoring data pipelines, and computational resources. It lies at the intersection of Data Engineering and Data Science.
Similar to Data Scientists, these experts optimize and enhance ML algorithms and artificial neural networks. However, they also take charge of deploying these algorithms into production. Their role extends to creating automated and scheduled ML pipelines.
Machine Learning Engineer Training: The DataScientest Choice
To pursue a career as a Machine Learning Engineer, you can consider the DataScientest curriculum. Our training is primarily designed for professional Data Scientists who aim to learn the deployment aspects of their projects.
It’s essential to have a firm grasp of Machine Learning algorithms and libraries before entering this program. Solid programming skills are also a prerequisite.
Through this Machine Learning Engineer training, you will gain a deeper understanding of software engineering, learning how to maintain production systems more effectively, and gaining insights into the challenges related to deployment and data pipeline obsolescence. You’ll also acquire skills in creating and using containers within distributed environments.
The primary strength of DataScientest’s training programs such as the Machine Learning Engineer training lies in their unique hybrid format. Our courses are divided into 15% in-person sessions and 85% remote learning. This allows you to benefit from the flexibility of remote learning through a personalized coaching platform while staying engaged with mandatory masterclasses.
The curriculum is structured into different blocks, each further divided into modules, totaling 100 hours of training. You can complete this program through continuous learning, dedicating 8 to 10 hours per week for three months.
Upon program completion, you’ll receive a diploma that will significantly enhance your prospects of securing a Machine Learning Engineer position in the industry of your choice. Don’t wait any longer – reach out to our educational advisors for more information about the Machine Learning Engineer training and to begin the enrollment process.
Now you know how and why to start a Machine Learning Engineer training with DataScientest. Explore our comprehensive guides on Machine Learning and Data Science.
