This blog brings you the essential ML concepts for a candidate to know to pass the AWS AI Practitioner(AIF-C01) exam. As AI gets integrated into many aspects of our daily lives, it’s time for everyone to understand ML to use AI responsibly. Without going in-depth into technicality, AIF-C01 will help you learn the practical aspects of ML and AI. Scroll down to explore it.
AIF-C01 Highlights
Without any formal prerequisites, AIF-C01 is the perfect exam for anyone looking to solidify their foundational understanding of AWS AI, ML, and generative AI (gen AI) technologies and applications. Professionals in roles such as business leaders, sales and marketing professionals, and product managers can take advantage of the knowledge of this exam. The foundational AWS Machine Learning Pipeline covered in this course can become your stepping stone to continue your AWS ML journey with the following two exams:
Key Machine Learning Concepts
Here are some machine learning fundamentals covered in AIF-C01, which you can apply while using AWS ML/AI services.
The AWS AI/ML stack
The AWS AI/ML stack is a conceptual hierarchy of services and features organized into layers, based on user expertise and needs. Knowledge of this stack is important both from exam point of view and also from real-life applications. You leverage this knowledge to:
- Distinguish between AI and ML services
- Optimize AI/ML workflows
- Select the right service for the right task
- Ensure security and compliance
Components of the AWS AI/ML stack
The AWS AI/ML stack can be categorized into four layers:
- ML infrastructure
- ML Frameworks
- AI/ML services
- Generative AI
ML infrastructure layer: This is the bottom layer that provides foundational compute, storage, and networking for AI/ML workloads. This is the most sophisticated layer and is designed for use by ML experts and practitioners.
ML Frameworks: This layer provides tools to effectively build, train, and run large language models (LLMs) and other foundation models (FMs) efficiently and cost-effectively. Amazon SageMaker is the core of the AWS ML framework. Similar to the bottom layer, this layer is intended for ML experts and practitioners (developers and data scientists).
AI/ML services: provides ML services for data scientists and developers without requiring extensive infrastructure management or specialized expertise. Users are developers with little knowledge of ML
Generative AI: this is the top layer that offers tools and services specifically designed for generative AI tasks.
This structured approach allows AWS customers to choose between customization and complexity based on their AI/ML workloads. Moving the crack and reducing complexity, different layers can be used independently or together.
ML Life Cycle and Pipeline
Machine learning is a broad umbrella that includes many subcategories.
ML workflow: The life cycle of a machine learning project can be structured into a multi-phase flow. The ML workflow includes sequential stages designed to develop, deploy, and maintain machine learning models efficiently.
ML Pipeline: ML pipelines automate the workflow through coded pipeline tasks.
ML Operations (MLOps): It refers to the practices and tools used to streamline and optimize the machine learning lifecycle. The key principles of MLOps include
- Automation
- Version control
- Continuous integration (CI)/continuous delivery (CD)
- Model governance
The course covers how to use Amazon SageMaker to perform all the steps in the ML workflow.
Sources of Foundation Models
A foundation model (FM) is an ML model pre-trained on a vast quantity of data at scale, which can be easily adapted for specific use cases (such as summarization, translation, code generation). without having to build a new ML model from scratch for each use case.
By providing FM models, both Amazon Bedrock and SageMaker JumpStart simplify the development of AI applications. Bedrock provides a range of ready-to-use AWS and third-party FMs. JumpStart provides pre-trained, open-source models that can be customized for different use cases.
Model Training: Supervised, Unsupervised, and Reinforcement Learning
A model must be trained before it can make predictions. Common model training methods include:
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised learning: Models are trained with labelled data learning patterns and make accurate predictions about new, unseen data.
Unsupervised learning: In this method, the models are trained with unlabelled data and are tasked to find hidden patterns, relationships, and structures within that data.
Reinforcement learning: This is based on trial-and-error interaction with the environment.
The SageMaker supports many built-in algorithms to supervise, unsupervise, and reinforcement learning tasks
Model Evaluation Metrics
After a model is trained, it must be evaluated to check how well it performs. How you evaluate a machine learning model depends on what kind of ML problem you’re working with. Here are common classification and regression metrics:
SageMaker provides built-in metrics to measure model performance.
Model Deployment
Model deployment is the integration of the model and its resources into a production environment so that it can be used to create predictions. You can use SageMaker to deploy a model to get predictions in several ways:
- Real-time
- Batch transform
- Asynchronous
- Serverless
ML Security and Compliance
Security and compliance best practices ensure your ML environment is secure from threats and resulting financial losses, and compliant with fraud prevention regulations. AWS offers various services to protect ML environments:
- AWS Identity Access Management (AWS IAM): control access to ML resources using fine-grained access control, least privilege principle, role-based access control, and temporary security credentials.
- AWS Key Management Service (AWS KMS): manage encryption keys to protect sensitive data in ML workflows.
- SageMaker security features: safeguard your ML environment from external threats by running training jobs and deploying models in an isolated network environment (Amazon Virtual Private Cloud – VPC). Integrate your model with CloudTrail and CloudWatch to detect unusual activities.
Monitoring and Maintenance
After a model is deployed, it must be monitored and maintained for smooth functioning. AWS offers several services for monitoring and maintenance:
- Amazon SageMaker Debugger: get insights during training to identify and resolve issues.
- Amazon CloudWatch: monitor models and receive logs metrics from models and infrastructure, showing performance and operational health.
- Amazon SageMaker Model Monitor: check monitoring reports on deployed models for data drift and performance degradation, with features to automatically retrain models if necessary.
AWS AI Services
Amazon AI services add extra value to AWS ML deployments, providing advanced functionalities and ease of integration that complement the core ML capabilities. These AI services enhance the overall experience and effectiveness of deploying machine learning solutions on AWS.
- Amazon Comprehend, analyzes and extracts insights from text. It then integrates large volumes of unstructured text data, such as customer feedback or social media posts into ML workflows for better decision-making.
- Amazon Transcribe converts spoken language into written text and extracts textual data from audio sources like customer service calls, interviews, or lectures, which can be used to train models.
- Amazon Lex helps in creating chatbots and virtual assistants and automates customer interactions with real-time responses.
- Amazon Polly will convert text into lifelike speech in multiple languages and voices and enhance accessibility by providing spoken content for applications.
- Amazon Rekognition can identify objects, people, text, scenes, and activities in images and videos for advanced visual analysis and processing
Final thoughts
ML drives the practical application of AI by acting as a workhorse behind the scenes. As ML techniques are used to implement AI solutions, understanding ML fundamentals is required to deep dive into AI. AWS Certified AI Practitioner (AIF-C01) provides an opportunity to understand the correlation in AI, ML, and deep learning concepts, and address their common use cases. As AI becomes all-pervasive, the course lays the foundation to understand how our world works now and how it will work in the future.
