Extended Abstract, Intetics Inc.
4851 Tamiami Trail N, Suite 200, Naples, FL 34103-3098, United States
Keywords: Machine Learning, ML, Artificial Intelligence, AI, ML algorithms, AI/ML Use Cases, Challenges, ML Tech Architectures, Industry Standards, ML Certifications
Efficient data analysis and processing are critical for success in today’s business landscape, and machine learning offers a powerful solution. Through automated algorithms, ML can quickly and accurately process vast amounts of data, and this White Paper delves into the impact of this technology on how organizations operate. The material is a must-read for business leaders looking to leverage ML for enhanced agility, more accurate decision-making, superior user experience, and optimized costs.
You’ll learn the top seven AI/ML use cases worldwide for businesses, as well as the main challenges that come with deployment. The problems range from IT governance and security to senior buy-in and version control.
This section explores machine learning and its most common terminology (regression, clustering, classification, etc.). It also highlights some of the main players in the industry, from IBM to Microsoft, and their software platforms for data science, data mining, and analytics.
The timeline of ML showcases the evolution of technology. This brief history highlights key achievements and benchmarks in the field, building a foundation of knowledge as we move into the Fourth Industrial Revolution
The global machine learning market has experienced rapid expansion due to technological advancements. This has driven impact across various industries, including the automotive sector and its customer-centric connected experiences. Here, you’ll learn how much the market is predicted to grow further.
This abstract offers a concise overview of ML and its five core steps (sourcing raw data, preprocessing, training, model validation, and improvement), as well as the four main algorithm types (supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning). It also explains the concept of data labeling and its importance in supervised learning.
Here, you’ll learn about the challenges faced in machine learning and how Transfer Learning can help address those challenges.
This section contains an overview of the key infrastructure layers needed to build ML applications. Additionally, you’ll read about the importance of each layer and examples of popular tools and frameworks used at each stage.
Machine learning has become a driving force behind significant disruptions and advancements in industries that collect and process data. This article highlights the impact and value of ML in the automotive, healthcare, and digital section.
This is an overview of some of the most important standards and best practices in use for ML (ONNX, ISO/IEC JTC 1/SC 42, etc.). Understanding and adopting these standards is essential for creating systems that are fair, transparent, and respectful of individual privacy rights.
Find out who some of the key figures and organizations in the field of AI and ML are, as well as how they have contributed to the industry. There are a lot of players, from leading research teams at Google and Facebook to influencers like Olivio Sarikas and Matt Wolfe.
Then, the White Paper goes into some of the available certifications, such as Google’s CMLE and Microsoft’s Azure AI Engineer Associate.
Successful implementation requires a thorough assessment of an organization’s readiness and need for the technology. Here are the key questions to ask.
If you’re looking to deepen your knowledge of machine learning, there are plenty of resources available to help you, including books, guides, and online resources.
Finally, the White Paper will sum up the value and impact of machine learning in various industries, as well as the recent advancements in technology.