AWS CERTIFICATION NOTES - Part 1

Introduction to Machine Learning: Art of the Possible

What is machine learning?

Machine learning (ML) is the process of training computers, using math and statistical processes, to find and recognize patterns in data. After patterns are found, ML generates and updates training models to make increasingly accurate predictions and inferences about future outcomes based on historical and new data. For example, ML could help determine the likelihood of a customer purchasing a particular product based on previous purchases by the user or the product's past sales history.

Building ML applications is an iterative process that involves a sequence of steps.?To build an ML application, follow these general steps:

What are the key terms in machine learning?

1. Model :The output of an ML algorithm trained on a data set; used for data prediction
2. Training :The act of creating a model from past data
3. Testing :Measuring the performance of a model on test data
4. Deployment : Integrating a model into a production pipeline

What is the history of Amazon machine learning?

Amazon has over 20 years of experience with machine learning. In addition, Amazon uses ML to sell more than 4,000 products per minute on Amazon.com, and also in completing the first autonomous Prime Air Delivery in 2016. In July 2020, AWS was recognized as a leader in the Gartner Magic Quadrant for cloud AI developer services. The ML platform Amazon SageMaker received the highest rating among its peer group (84/100) on Gartner's "Solution Scorecard for Amazon SageMaker."

What is the Amazon approach to machine learning?

The Amazon flywheel was an idea that Amazon founder Jeff Bezos sketched on the back of a napkin. It illustrates how investing in specific key business operations can reinforce other processes and create a positive feedback loop. When Amazon focused on improving the customer experience, more customers joined. Higher customer traffic led to larger vendor pools and broader product selections, which resulted in lower prices and lower cost structures. Amazon reinvested in improving the customer experience, which leads to platform growth, and the flywheel reinforcement continues.

The Amazon ML flywheel uses data collected from parts of a business operation, uses a model to predict future outcomes, and provides ways to continuously improve efficiency and develop new operational capabilities and business practices. With ML, increasing predictions improve growth and efficiency. This leads to more usage and data, completing the feedback loop and reinforcing all parts of the flywheel.

How is Amazon using machine learning in products?

• Amazon uses browsing and purchasing data to provide tailored product recommendations and promotions.?
• Amazon uses ML to facilitate billions of voice interactions per week with Alexa devices using natural language processing (NLP).
• Amazon uses ML to ship 1.6M packages per day.

How is machine learning helping AWS customers?

AWS machine learning services have provided solutions for a variety of customer use cases. AWS ML customers have extracted and analyzed client document data to help speed up critical business decisions and the identification of fraudulent online activities. AWS ML customers forecast their key demand metrics to meet customer demand and reduce waste. These customers have also generated personalized recommendations to maximize customer engagement. Below is a quick overview of various AWS AI, ML, and platform services that customers are using to accelerate business outcomes.

What are some examples of machine learning being used today?

• In healthcare, machine learning is analyzing large amounts of clinical data to help suggest treatment for patients.
• The trucking industry uses machine learning to automate logistics
• Ride-sharing apps use data to update wait times, demand prediction, and price setting.
• Manufacturers use ML to predict product defects earlier and with greater precision, resulting in substantial cost savings.
• The finance industry is investing in ML to enable automated threat intelligence, prevention systems, and fraud analysis and investigation.
• In the energy sector, ML helps companies intelligently access document data, make smarter decisions faster, improve operations, and boost productivity.

What other industries are using machine learning?

Industrial companies use AI and ML services for asset management. This includes using computer vision for equipment monitoring and defect detection, or analyzing operational machine behavior data to enable predictive maintenance. Customer service organizations use ML to transcribe and analyze live and archived calls for sentiment scores. ML can also help prioritize based on categorized customer feedback, and enable software to provide agents with answers to questions as they are being asked.

How does the NFL use AWS machine learning?

AWS helps the NFL to leverage the power of its data through sophisticated analytics and ML. The NFL uses training data from traditional box-score statistics. The NFL also uses data collected from the stadium to create new stats, improve player health and safety, and provide a better experience for fans, players, and teams—all in real time. ML models built on the Next Gen Stats (NGS) platform ingest the data. This continually trains and refines the models to help boost accuracy, speed, and insights while reducing the time to get results.

How can machine learning help me?

Machine learning can continuously improve results, which means training models can become a part of almost any decision-making process. Machine learning can ingest limitless amounts of data, produce timely analysis and assessment, identify trends and patterns, and generate predictive forecasts.

• Make predictions.
• Drive efficiencies.
• Enable automation.
• Accelerate decision-making.

What else is possible?

The use of machine learning in the future of business and technology is virtually boundless. ML can become an automated part of software engineering by implementing algorithms in low- or no-code development environments. ML can also become a catalyst for quantum computing by increasing data processing speeds or rapidly accelerating model training.