Amazon Web Services (AWS) has been a leading provider of cloud-based services since its inception in 2002, offering a wide range of solutions that include data storage and analytics, computing power, machine learning (ML), and more. One of the most in-demand services is AWS Machine Learning Certification, a program that provides individuals and organizations with the tools and knowledge necessary to create and manage ML models using AWS.
The cost of AWS Machine Learning Certification varies depending on the level of certification, the training provider, and other factors. In this article, we'll explore the different types of AWS Machine Learning Certifications available, their costs, and some code examples to give you a better understanding of the program.
Types of AWS Machine Learning Certifications
There are currently four levels of AWS Machine Learning Certification:
- AWS Certified Machine Learning – Specialty
This certification is designed for AWS professionals who want to demonstrate their ability to create and deploy ML models using AWS. It covers topics like data engineering, data preparation, feature engineering, and machine learning algorithms. The exam costs $300 and contains 65 multiple-choice questions that must be completed within 180 minutes.
- AWS Certified Data Analytics – Specialty
This certification is designed for data analysts and data scientists who want to demonstrate their ability to design and implement data analysis solutions using AWS. It includes topics like data acquisition, data processing, data visualization, and machine learning. The exam costs $300 and contains 65 multiple-choice questions that must be completed within 180 minutes.
- AWS Certified Advanced Analytics – Specialty
This certification is designed for AWS professionals who want to demonstrate their ability to build complex analytics solutions using AWS. It includes topics like data architecture, data engineering, data analysis, and machine learning. The exam costs $300 and contains 65 multiple-choice questions that must be completed within 180 minutes.
- AWS Certified Alexa Skill Builder – Specialty
This certification is designed for developers who want to build and deploy voice-controlled applications using Alexa, Amazon's virtual assistant. It includes topics like Alexa skill development, Alexa skill testing, and Alexa skill publishing. The exam costs $300 and contains 65 multiple-choice questions that must be completed within 180 minutes.
AWS Machine Learning Certification Cost
As mentioned earlier, the cost of AWS Machine Learning Certification varies depending on the level of certification and the training provider. Training providers like A Cloud Guru, Linux Academy, and Udemy offer courses that prepare individuals for the certification exams. The cost of these courses ranges from $15 to $500, depending on the provider and level of certification.
Code Examples
To give you a better understanding of what you can expect to learn from AWS Machine Learning Certification, let's look at some code examples.
- Predictive Analysis with Amazon SageMaker
Amazon SageMaker is a fully managed service that allows you to build, train, and deploy ML models quickly and easily. Here's an example of predictive analysis using Amazon SageMaker:
import boto3
import pandas as pd
import numpy as np
import io
s3 = boto3.client('s3')
bucket_name = 'my-bucket'
object_key = 'my-data.csv'
response = s3.get_object(Bucket=bucket_name, Key=object_key)
data = response['Body'].read().decode('utf-8')
df = pd.read_csv(io.StringIO(data))
df.head()
import sagemaker
from sagemaker import get_execution_role
role = get_execution_role()
region = boto3.Session().region_name
container = sagemaker.image_uris.retrieve('xgboost', region, '1.2-1')
estimator = sagemaker.estimator.Estimator(container,
role,
instance_count=1,
instance_type='ml.m4.xlarge',
output_path='s3://{}/output'.format(bucket_name),
sagemaker_session=sagemaker.Session())
estimator.set_hyperparameters(max_depth=5,
eta=0.2,
gamma=4,
min_child_weight=6,
subsample=0.8,
silent=0,
objective='reg:linear',
num_round=100)
train_input = sagemaker.inputs.TrainingInput('s3://{}/train'.format(bucket_name), content_type='csv')
validation_input = sagemaker.inputs.TrainingInput('s3://{}/validation'.format(bucket_name), content_type='csv')
estimator.fit({'train': train_input, 'validation': validation_input})
predictor = estimator.deploy(initial_instance_count=1,
instance_type='ml.m4.xlarge')
df_validation = pd.read_csv('s3://{}/validation/validation.csv'.format(bucket_name), header=None)
df_validation.head()
predict_input = np.array(df_validation.iloc[:, :-1])
predictor.predict(predict_input)
- Natural Language Processing (NLP) with Amazon Comprehend
Amazon Comprehend is a natural language processing service that can identify key phrases, entities, and sentiment in text. Here's an example of NLP using Amazon Comprehend:
import boto3
comprehend = boto3.client(service_name='comprehend', region_name='us-west-2')
text = 'I love Amazon Comprehend!'
print('Input Text: ' + text)
sentiment_response = comprehend.detect_sentiment(Text=text, LanguageCode='en')
print('Sentiment: ' + sentiment_response['Sentiment'])
entities_response = comprehend.detect_entities(Text=text, LanguageCode='en')
print('Entities: ')
for entity in entities_response['Entities']:
print('{}: {}'.format(entity['Type'], entity['Text']))
key_phrases_response = comprehend.detect_key_phrases(Text=text, LanguageCode='en')
print('KeyPhrases: ')
for phrase in key_phrases_response['KeyPhrases']:
print(phrase['Text'])
Conclusion
AWS Machine Learning Certification is a valuable tool for anyone looking to build and manage ML models using AWS. The cost of certification varies depending on the level and training provider, but the investment is worth it given the increased demand for machine learning professionals and the potential for higher salaries. The code examples provided in this article offer a glimpse into what you can expect to learn from the AWS Machine Learning Certification program, and the skills you'll develop will be valuable in a wide range of industries.
AWS Machine Learning Certification is a comprehensive program designed to provide individuals and organizations with the tools and knowledge necessary to create and manage ML models in the AWS environment. The training is available online, and it covers a range of topics, including data preparation, feature engineering, algorithms, data architecture, data engineering, data analysis, and publishing.
In addition to the four levels of AWS Machine Learning Certification, the program offers various pathways that allow you to specialize in specific areas of ML, depending on your interests and career goals. These pathways include:
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AWS Certified Developer – Associate: This pathway is ideal for developers who want to learn how to build ML applications on AWS.
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AWS Certified Solutions Architect – Associate: This pathway is designed for professionals who want to learn how to design and deploy ML solutions in AWS.
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AWS Certified Big Data – Specialty: This pathway is intended for individuals who want to learn how to use big data technologies like Hadoop and Spark to build ML solutions.
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AWS Certified Security – Specialty: This pathway is designed for security professionals who want to learn how to build secure ML solutions on AWS.
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AWS Certified Cloud Practitioner: This pathway provides an overview of basic AWS concepts and is ideal for professionals who are new to the AWS environment.
The cost of AWS Machine Learning Certification varies depending on the level of certification, the training provider, and other factors. But considering the increased demand for machine learning professionals and the potential for higher salaries, the investment is worth it. The program provides you with a competitive edge in the field of ML and prepares you for the challenges of a rapidly evolving technology landscape.
The code examples provided in this article offer a glimpse into what you can expect to learn from the AWS Machine Learning Certification program. Predictive analysis with Amazon SageMaker allows you to build, train, and deploy ML models quickly and easily, while natural language processing with Amazon Comprehend enables you to identify key phrases, entities, and sentiment in text. These and other examples provided in the program offer practical, hands-on experience that prepares you for real-world scenarios.
In conclusion, AWS Machine Learning Certification is an excellent investment for individuals and organizations that want to build and manage ML models in the AWS environment. With a range of certification levels and pathways to choose from, the program provides you with the tools and knowledge necessary to succeed in the field of ML. The code examples provided in this article offer a glimpse into what you can expect to learn, and the skills you'll develop will be in high demand across a wide range of industries.
Popular questions
- What are the four levels of AWS Machine Learning Certification?
The four levels of AWS Machine Learning Certification are AWS Certified Machine Learning – Specialty, AWS Certified Data Analytics – Specialty, AWS Certified Advanced Analytics – Specialty, and AWS Certified Alexa Skill Builder – Specialty.
- What is the cost of AWS Machine Learning Certification?
The cost of AWS Machine Learning Certification varies depending on the level of certification and the training provider. The certification exams cost $300, and training courses cost between $15 and $500, depending on the provider and level of certification.
- What is Amazon SageMaker, and what is an example of its use in predictive analysis?
Amazon SageMaker is a fully managed service that allows you to build, train, and deploy ML models quickly and easily. An example of predictive analysis using Amazon SageMaker involves importing data from an Amazon S3 bucket, then using a built-in XGBoost algorithm to train and deploy a model.
- What is Amazon Comprehend, and what is an example of its use in Natural Language Processing?
Amazon Comprehend is a natural language processing service that can identify key phrases, entities, and sentiment in text. An example of NLP using Amazon Comprehend involves using the service to detect the sentiment and identify entities and key phrases in a given input text.
- Is AWS Machine Learning Certification worth the investment?
Yes, AWS Machine Learning Certification is worth the investment, considering the increased demand for machine learning professionals and the potential for higher salaries in this field. The program provides you with practical, hands-on experience that prepares you for real-world scenarios and provides you with a competitive edge in the field of ML.
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