5 Must-Have AI/ML Tools for AWS Certified AI Practitioner
The AWS Certified AI/ML Practitioner exam is the top certification for AI/ML solutions. Let's get started with some points to help you on you pass.
Published Jan 19, 2025
I going to start with you guys just explaining how the generative AI works, and after this I will introduce 5 AWS AI tools you can use to pass on AWS AI Practitioner certification. All those tools is essential to learn, so the exam will cover some points of view and the usage in AI context.
The AWS Certified AI Practitioner certification validates the required knowledge of artificial intelligence, machine learning (ML), and generative AI concepts and use cases. Improve your competitive advantage and position yourself for professional growth and greater earnings.
To fully understand the capabilities and potential of generative AI, it is crucial to understand its relationship to the broader fields of AI, machine learning, and deep learning. By examining the similarities and differences between these concepts, we can develop a more comprehensive understanding of the technology landscape and the synergies driving innovation in this rapidly evolving domain.
- Generative AI: is a subset of deep learning because it can adapt models created using deep learning, but without retraining or fine-tuning. Generative AI systems are capable of generating new data based on the patterns and structures learned from the training data.
- Deep learning: uses the concept of neurons and synapses, similar to how our brains work. An example of a deep learning application is Amazon Rekognition, which can analyze millions of images, streaming, and stored videos in seconds.
- Machine Learning: is a type of AI that involves understanding and creating methods that enable machines to learn. These methods use data to improve the computer's performance on a set of tasks.
- Artificial Intelligence: AI is a broad field that encompasses the development of intelligent systems capable of performing tasks that normally require human intelligence, such as perception, reasoning, learning, problem-solving, and decision-making.
AWS offers a comprehensive set of ML and generative AI services that can help you unlock the full potential of these transformative technologies for the Cloud IA Practitioner exam.
- Amazon Bedrock
The Amazon Bedrock you can access to ready-made generative AI models, you can integrate with text and image models from external providers, and it allows you to create chatbots, text summarization and content automation.
AWS Bedrock - Amazon SagerMaker
With SageMaker, you can build, train, and deploy ML models for any use case with fully managed infrastructure, tools, and workflows. SageMaker removes the complex work from every step of the ML process, making it easy to develop high-quality models. SageMaker provides all the components used for ML in a single toolset, so models can reach production faster, with much less effort and at lower cost.
Amazon SageMaker - Amazon Comprehend
The Amazon Comprehend uses ML and natural language processing (NLP) to help you discover insights and relationships in your unstructured data. This service performs the following functions:
- Identifies the language of text
- Extracts key phrases, places, people, brands, or events.
- Understands how positive or negative text is.
- Analyzes text using tokenization and parts of speec.
- And automatically organizes a collection of text files by topic.
Amazon Comprehend
- Amazon Polly
Amazon Polly is a service that turns text into lifelike speech, enabling you to create applications that talk and build entirely new categories of speech-enabled products. Amazon Polly is a Text-to-Speech (TTS) service that uses advanced deep learning technologies to synthesize speech that sounds like a human voice.
Amazon Polly - Amazon Lex
Amazon Lex is a fully managed AI service for designing, building, testing, and deploying conversational interfaces for any application using voice and text. Amazon Lex provides advanced deep learning capabilities for automatic speech recognition (ASR) to convert speech to text, and natural language understanding (NLU) to recognize intent from text.
Amazon Lex
When working with AI and ML services on AWS, it’s essential to understand the various cost considerations involved. The tradeoffs can affect factors such as responsiveness, availability, redundancy, performance, regional coverage, pricing models, throughput, and the ability to use custom models. It is important to carefully evaluate your specific requirements and workload when choosing AWS services.
AWS has harnessed the power of its vast partner ecosystem to help strategize, develop, deploy, and manage AI solutions. As generative AI rapidly evolves, AWS is poised to lead innovation in this space. I hope you find this content insightful as you dive into the world of Generative AI. Always be mindful of resource management to optimize costs while ensuring a secure environment. Enjoy the read! 🚀🚀🚀