ML challenges

Over the years, I have worked on many real-world problems using ML solutions and encountered different challenges faced by the different industries during ML adoptions.I often get the same question when working on ML projects: We have a lot of data – can you help us figure out what insights we can generate using ML? I refer to companies with this question as having the business use case challenge. Not being able to identify business use cases for ML is a very big hurdle for many companies. Without a properly identified business problem and its value proposition and benefit, it becomes difficult to initiate an ML project.In my conversations with different companies across their industries, data-related challenges emerge as frequent issue. This includes data quality, data inventory, data accessibility, data governance, and data availability. This problem affects both data-poor and data-rich companies and is often exacerbated by data silos, data security, and industry...

ML solutions architecture

When I initially worked with companies as an ML solutions architect, the landscape was quite different from what it is now. The focus was mainly on data science and modeling, and the problems at hand were small in scope. Back then, most of the problems could be solved using simple ML techniques. The datasets were also small, and the infrastructure required was also not demanding. The scope of the ML initiative at these companies was limited to a few data scientists or teams. As an ML architect at that time, I primarily needed to have solid data science skills and general cloud architecture knowledge to get the job done.In the more recent years, the landscape of ML initiatives has become more intricate and multifaceted, necessitating involvement from a broader range of functions and personas at companies. My engagement has expanded to include discussions with business executives about ML strategies and organizational design to faciliate the broad adoption of AI...

Testing your knowledge

Great job! You have reached to the end of the chapter. Now, let's put your newly acquired knowledge to the test and see if you've understood and retained the information presented.Take a look at the list of the following scenarios and determine which of the three ML types can be applied (supervised, unsupervised, or reinforcement):

1. There is a list of online feedback on products. Each comment has been labeled with a sentiment class (for example, positive, negative, neutral). You have been asked to build an ML model to predict the sentiment of new feedback.
2. You have historical house pricing information and details about the house, such as zip code, number of bedrooms, house size, and house condition. You have been asked to build an ML model to predict the price of a house.
3. You have been asked to identify potentially fraudulent transactions on your company's e-commerce site. You have data such as historical transaction data, user information, credit...

Summary

You now have a solid understanding of various concepts such as AI, ML, and the essential steps of the end-to-end ML life cycle. Additionally, you have gained insight into the core functions of ML solutions architecture and how it plays a crucial role in the success of an ML project. With your newfound knowledge, you can differentiate between different types of ML and identify their application in solving business problems. Moreover, you have learned that it is crucial to have a deep understanding of business and data to achieve success in an ML project, besides modeling and engineering. Lastly, you have gained an understanding of the significance of ML solutions architecture and how it fits into the ML life cycle.In the upcoming chapter, we will dive into various ML use cases across different industries, such as financial services and media and entertainment, to gain further insights into the practical applications of ML.

ML use cases in manufacturing

The manufacturing industry is a vast sector that is responsible for creating a wide range of physical products, such as consumer goods, electronics, automobiles, furniture, building materials, and more. Each sub-sector of manufacturing requires a specific set of tools, resources, and expertise to successfully produce the desired products.

The manufacturing process generally involves several stages, including product design, prototyping, production, and post-manufacturing service and support. During the design phase, manufacturers work on conceptualizing and planning the product. This includes defining the product’s features, materials, and production requirements. In the prototyping stage, a small number of products are created to test their functionality and performance.

Once the product design has been finalized, manufacturing and assembling takes place. This is the stage where raw materials are transformed into finished products. Quality...

ML use cases in retail

The retail industry is a sector that sells consumer products directly to customers, either through physical retail stores or online platforms. Retailers acquire their merchandise from wholesale distributors or manufacturers directly. Over the years, the retail industry has undergone significant changes. The growth of e-commerce has outpaced that of traditional retail businesses, compelling brick-and-mortar stores to adapt and innovate in-store shopping experiences to remain competitive. Retailers are exploring new approaches to enhance the shopping experience across both online and physical channels. Recent developments such as social commerce, augmented reality, virtual assistant shopping, smart stores, and 1:1 personalization have become key differentiators in the retail industry.

The retail industry is currently undergoing a transformation fueled by AI and ML technologies. Retailers are utilizing these technologies to optimize inventory, predict consumer...

ML use cases in the automotive industry

The automotive industry has undergone significant transformation in recent years, with technology playing a key role in shaping its evolution. AI and ML have emerged as powerful tools for automakers and suppliers to improve efficiency, safety, and customer experience. From production lines to connected cars, AI and ML are being used to automate processes, optimize operations, and enable new services and features.

Autonomous vehicles

One of the most significant applications of AI and ML in the automotive industry is in autonomous driving. Automakers and tech companies are leveraging these technologies to build self-driving vehicles that can safely navigate roads and highways without human intervention. AI and ML algorithms are used to process data from sensors, cameras, and other inputs to make real-time decisions and actions, such as braking or changing lanes.

The system architecture of an autonomous vehicle (AV) consists of 3 main...

Summary

Throughout this chapter, we have explored various industries and the ways in which they are utilizing ML to solve business challenges and drive growth. From finance and healthcare to retail and automotive, we have seen how ML can improve processes, generate insights, and enhance the customer experience. The examples within this chapter have hopefully sparked ideas you can now bring to stakeholders to kickstart an ML roadmap discussion and think creatively about the potential high-impact applications in your own organizations.

As we move into the next chapter, we will delve deeper into the mechanics of ML, exploring the fundamental concepts behind how machines learn and some of the most widely used algorithms in the field. This will provide you with a solid foundation for understanding how ML is applied in practice to solve various ML problems.

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