How much does AWS pay certified ML engineers?

The salary of a certified machine learning (ML) engineer at AWS can vary based on factors such as experience, location, job level, and specific responsibilities. It’s important to note that I, as an AI language model, do not have access to real-time salary data, and the information I provide is based on general industry knowledge up until September 2021. Therefore, it’s advisable to consult up-to-date salary surveys, job postings, or reach out to AWS or relevant job marketplaces for the most accurate and current information on compensation.

That being said, ML engineers in general command competitive salaries due to their specialized skills and high demand in the industry. In the United States, the salary for a certified ML engineer at AWS can range from approximately $100,000 to well over $200,000 per year, depending on experience and location. Senior ML engineers or those with more years of experience can earn higher salaries within this range or even higher. Additionally, factors such as bonuses, stock options, and other benefits can also contribute to the overall compensation package.

It’s worth noting that salaries can vary significantly across different countries and regions, so the compensation for an AWS certified ML engineer may be different outside of the United States. Factors like cost of living, local market conditions, and demand for ML expertise in a specific region can influence salary ranges accordingly.

If you are specifically interested in working as an ML engineer at AWS, it’s recommended to explore job listings, connect with industry professionals, and conduct thorough research to gain a better understanding of the current salary landscape for ML engineers at AWS.

Which AWS service provides machine learning?

AWS provides several services that cater to different aspects of machine learning. Here are some key AWS services for machine learning:

Amazon SageMaker

Amazon SageMaker is a fully managed service for building, training, and deploying machine learning models. It offers a complete set of tools and infrastructure to facilitate the entire machine learning workflow, from data preprocessing and model training to deployment and hosting.

AWS Deep Learning AMIs

The AWS Deep Learning AMIs (Amazon Machine Images) are pre-configured environments with popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. These AMIs make it easier to set up and run deep learning applications on AWS. If we required AWS course in Chandigarh.

Amazon Rekognition

Amazon Rekognition is a service that leverages deep learning algorithms for image and video analysis. It provides capabilities like facial recognition, object detection, scene understanding, and content moderation.

Amazon Comprehend

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to extract insights and meaning from text. It can perform tasks like sentiment analysis, entity recognition, keyphrase extraction, and language detection.

Amazon Forecast

Amazon Forecast is a fully managed service for time series forecasting. It uses machine learning techniques to generate accurate forecasts based on historical data and related variables.

Amazon Personalize

Amazon Personalize is a service for creating personalized recommendations for applications. It utilizes machine learning algorithms to analyze user behavior and generate tailored recommendations in real-time.

Amazon Lex

Amazon Lex is a service for building conversational interfaces using voice and text. It provides natural language understanding (NLU) capabilities and allows developers to create chatbots, virtual assistants, and voice-controlled applications.

Amazon Polly

Amazon Polly is a text-to-speech service that employs machine learning to convert text into lifelike speech. It supports multiple languages and offers customization options for voice characteristics.

Amazon Transcribe

Amazon Transcribe is an automatic speech recognition (ASR) service that converts spoken language into written text. It is suitable for tasks like transcription of audio recordings, real-time captioning, and voice-controlled applications.

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