The ClearScale Cloud Newsline - The AI/ML Issue

Machine Learning Basics and a Real-World Application

From fraud detection and email filtering to self-driving cars and patient diagnosis, the potential use cases of machine learning seem almost endless. But if you want to deploy a machine learning (ML) application, where do you begin? Do you build it in-house or outsource it? What resources do you need? What platform should you build it on?

In this blog post, we delve into the crucial factors to consider when developing and deploying ML applications.

Understanding Machine Learning: The Basics

Before diving into the intricacies of ML applications, it’s crucial to grasp the fundamental concept of machine learning. While the definition may vary based on the source, machine learning is just what the name suggests. It’s the process of a machine learning something.

In layman’s terms, machine learning is an aspect of data science related to artificial intelligence (AI) that involves feeding data into an algorithm. With each iteration, the algorithm learns from the data, enhancing its ability to predict outcomes or solutions. The end product of an ML algorithm processing data is a model. This model encapsulates what the ML algorithm learned, including any rules, numerical values, or other algorithm-specific data structures necessary for making predictions.

The ML Process

Briefly, the ML process typically includes these steps:

• Problem framing. The ML process starts with problem framing. Determine what you want to predict and what kind of observation data you need to make those predictions.
• Data collection. Next, it collects data that contains the answer or solution you want to predict. There are a few general requirements for data. It should:Include large, diverse data sets integrated from multiple sources and concerning…

Continue reading to learn more about deploying a machine learning application.

Did You Know?

• 35% of companies report using AI in their business, and an additional 42% of respondents say they are exploring AI. (IBM)
• 42% of companies stated that the profitability of their ML and AI initiatives exceeded their expectations, while only 1% said it didn’t meet expectations. (Accenture)

Tales From The Trenches

PBS Offers Viewers Personalized Experience with Amazon Personalize

PBS wanted to develop a smart recommendation engine on AWS to offer a truly personalized experience to its millions of viewers while preserving brand and content uniqueness. ClearScale helped PBS set up the data operations MLOps platform it needed to make high-quality recommendations to viewers, and a demo UI to test the platform before going live. Read the case study.?

Staffing Company Leverages AWS Machine Learning to Identify Best Applicants

Core Group Resources (CGR) was struggling to keep up with staffing demand, as employees had to sift through thousands of CVs by hand to find high-quality matches for job vacancies. ClearScale built an automated ML scoring system that could evaluate individual resumes against specific job descriptions and present an ordered list back to CGR employees. Read the case study.?

Nonstop Uses Machine Learning Technology to Improve Forecasting Accuracy?

Nonstop wanted to build a new ML algorithm for its cost estimation process but had limited ML experience on the cloud. ClearScale created an effective ML solution for Nonstop and set the team up to manage the algorithm going forward. Read the case study.?

Quotable

"Just like the way a beautiful butterfly can’t come into life without its transformation cycle from egg to larva, caterpillar to pupa and finally to a brilliant creation, to become a successful digitally transformed organisation, similar transformational stages are essential.” - Enamul Haque?

Technology Trends?

4 Thoughts About GenAI and What Lies Ahead

Generative AI, or GenAI, is rapidly becoming a cornerstone in the digital ecosystem. Yet, navigating its evolution presents challenges, including understanding its capabilities beyond the hype and integrating it seamlessly into daily operations. However, the benefits of GenAI are many, including offering innovative solutions and enhancing creativity through controlled "hallucinations". Discover how GenAI is reshaping the landscape of technology and work. Read the Article

From Neural Networks to Transformers: The Evolution of Machine Learning and the Future of AI Applications

From neural networks to groundbreaking transformers, AI's evolution has catalyzed advancements in natural language processing and beyond. Transformers, known for their efficiency in parallel processing and self-attention, underpin large language models like GPT, facilitating complex tasks such as text generation and contextual understanding. Learn how these developments have broadened AI's applicability and set the stage for future innovations. Read the Article

How to Create Tailored User Experiences With Amazon Personalize

Amazon Personalize, powered by AWS, enables businesses to deploy sophisticated recommendation engines effortlessly, transforming user experiences with personalized content. This service processes vast data volumes to generate custom recommendations, crucial for enhancing engagement on digital platforms. Find out how Amazon Personalize can boost customer satisfaction and engagement, underscoring the importance of leveraging AI/ML for business innovation. Read the Article

Featured Cloud Resource

Modern organizations are embracing machine learning technology for its ability to provide actionable intelligence for real-time decision-making. Unfortunately, many machine learning projects fail to deliver due to insufficient data strategies or limited technical expertise.

Transform Your Business With Machine Learning explores the common elements of successful machine learning projects. This eBook explores the options for deploying machine learning technology that generates powerful business results and explains how to architect a solution that meets your needs.

Get the eBook

Learn From the Experts

Webinar: Decoding the Digital Future - A Comprehensive Guide to ML, AI, and Gen AI Technologies

In the evolving tech landscape, terms like machine learning (ML), artificial intelligence (AI), and Generative AI (Gen AI) are tossed around frequently. But what exactly do they mean, and how do these technologies compare? Watch this on-demand webinar to simplify the complexities and highlight the potential of ML, AI, and Gen AI.?

Watch It On Demand

Cloud Computing Terms Defined

Machine Learning: Machine learning is a branch of artificial intelligence concerned with building smart computer algorithms that improve over time. Organizations use machine learning to identify patterns in massive datasets and use those insights to enhance performance. Machine learning is responsible for many software services, including recommendation engines, social media feeds, and voice assistants. Read more in the Glossary of Cloud Computing Terms.?

Learn More About AI/ML

When executed well, artificial intelligence (AI) and machine learning (ML) enable businesses to transform products, services, and processes by automating much of what engineering teams do manually. ClearScale can help you realize the full potential of AI/ML across your business using powerful, purpose-built solutions from Amazon Web Services (AWS).

Visit ClearScale’s AI/ML Services page for more information.