My favourite tools for everything Machine learning and Artificial Intelligence on AWS are the SageMaker, BedRock, Amazon S3.
Now, lets break it down to see why i like these tools:
AWS BEDROCK: This has a plethora of foundational models (FM’s) using a single API, along with a broad set of capabilities you need to build generative AI applications with security, privacy, and responsible AI.
Using Amazon Bedrock, you can easily experiment with and evaluate top foundational models that can be customised with an organisation data using techniques such as fine tuning and Retrieval Augmented Generator (RAG).
What’s even amazing about this tool, is that it is serverless, you don’t have to manage any infrastructure, and you can securely integrate and deploy generate AI capabilities into your applications.
AWS SAGEMAKER: With the Sage Maker tool, you get a comprehensive machine learning service provided by amazon web services. it’s designed to simplify the process of building, training and deploying machine learning models at scale. Sagemaker offers a range of features and tools that facilitate every stage of the machine learning workflow, including data labelling, model training, tuning and hosting.
KEY COMPONENTS OF AWS SAGEMAKER
- Notebook Instances
- Data Labelling
- Model Training
- Model Tuning
- Model Deployment
- Hosting Services
In Summary, Sagemaker aims to streamline the machine learning workflow, making it more accessible and efficient for developers, data scientists, and businesses to leverage the power of machine learning in their applications and services.
Amazon S3 (Simple Storage Service): This is an object storage service provided by AWS. It allows individuals and businesses to store and retrieve large amount of data over the internet. S3 offers scalability, high availability, security features, and various storage classes to cater to different needs.
Developers or data scientists will be able to upload data in the form of objects (files) to S3 buckets, which act as containers for these objects. Each object is accessed via HTTP or HTTPS protocols.
S3 also provides various features such as versioning (which is keeping multiple versions of an object), lifecycle management (automating the movement of objects to different storage tiers), security and access controls and event notofications.
Developers are currently utilizing Amazon S3 for building and running big data analytics , artificial Intelligence (AI), Machine Learning (ML), and high performance computing (HPC) applications to unlock data insights. They also build scalable and reliable applications as well as deploying application, backing up structured and unstructured data, storing logs, due to its durability, scalability and ease in integration with AWS Bedrock and AWS SageMaker
