A machine learning team has several large CSV datasets in Amazon S3. Historically, models built with the Amazon SageMaker Linear Learner algorithm have taken hours to train on similar-sized datasets. The team's leaders need to accelerate the training process.
What can a machine learning specialist do to address this concern?
An ML engineer needs to implement a solution to host a trained ML model. The rate of requests to the model will be inconsistent throughout the day.
The ML engineer needs a scalable solution that minimizes costs when the model is not in use.
The solution also must maintain the model's capacity to respond to requests during times of peak usage.
Which solution will meet these requirements?
A gaming company needs to deploy a natural language processing (NLP) model to moderate a chat forum in a game. The workload experiences heavy usage during evenings and weekends but minimal activity during other hours.
Which solution will meet these requirements MOST cost-effectively?
A company regularly receives new training data from a vendor of an ML model. The vendor delivers cleaned and prepared data to the company's Amazon S3 bucket every 3-4 days.
The company has an Amazon SageMaker AI pipeline to retrain the model. An ML engineer needs to run the pipeline automatically when new data is uploaded to the S3 bucket.
Which solution will meet these requirements with the LEAST operational effort?
An ML engineer needs to use an ML model to predict the price of apartments in a specific location.
Which metric should the ML engineer use to evaluate the model's performance?
