How do FANG (now MANG)companies use Machine Learning (ML)?
Introduction –
Artificial Intelligence has become more ubiquitous as both fortune 100 companies, as well as tiny companies, are leveraging it.ML is one of the essential ingredients that is thriving in the AI market. In this modern era of technology, along with machine learning startups, top MNCs are also innovating rapidly towards AI & ML success. With the combination of technology and data, it has become possible to create intelligent systems and solve critical problems. Machine learning is the study of computer algorithms that helps to automate the improvement mechanism in a system. Machine learning leverages the development of computer programs that can use data to learn by themselves. This domain has shown tremendous upliftment in the field of innovation and investment in the last five years. So today I came up with a new article that will help you know how top MNCs are using Machine Learning / Artificial Intelligence in their projects.
Large companies like Facebook, Amazon, Netflix, and Google (FANG) are leveraging machine learning and deploying more and more AI-related solutions. All these solutions perform in the background to interact with the customers.
Various other Indian startups are growing in different verticals by implementing machine learning models. Names of some other popular startups are:
Discovery AI
Niki.ai
Doxper
CropIn
Arya.ai
Ma Street Den
But in this article, we will not talk about startups….
Top MNCs like MMAAG, FANG, FAANG, are utilizing Machine Learning (ML) Techniques
Applying machine learning for different business problems caters to unique solutions scaling the business and improving overall growth with time. This article will make you understand how companies like Facebook, Amazon, Netflix, and Google leverage ML & AI to build intelligent business solutions. As usual, the industry giants are at the frontline developing & researching new ML frameworks and techniques to gain a competitive advantage plus make their products and services better. Let us discuss how the top companies like Facebook, Amazon, Netflix, and Google utilize machine learning.
· Facebook (now Meta):
What comes to mind when someone talks about social networking and messenger apps? Of course, Facebook! This social networking application enjoys 2.41 billion active users monthly. Tons of machine learning algorithms run in the background, making it possible for Facebook to handle a huge user count. Let us dig deep and understand the various elements of Facebook that use different forms of machine learning models.
You might have seen that Facebook recommends tagging other users when you upload a group photo. It is because Facebook has a robust facial recognition program running behind the scene. It happens when you have the face recognition and or tag suggestion turned on. It utilizes machine learning techniques to parse the pixels from the face in the image. The algorithm then generates a template (a string of numbers) of each individual and maps the template with the tagged account. Apart from all these, Facebook also uses DeepText to understand the context and emotions behind a particular post. Its news feed and target advertising also utilize machine learning techniques.
Metaverse is the new action that has taken the technology market trend. Following Zuckerberg’s introductory note at the event, leader of Facebook AI, Jérôme Pesenti, and co-managing director at Facebook AI Research, Joelle Pineau, drilled into how Meta wants to unlock the metaverse with AI in a session titled “Unlocking the Metaverse with AI and Open Science.” Pesenti noted that AI is one of the keys to the metaverse. He said the mission of Meta AI is to bring the world closer together by advancing AI through AI research breakthroughs and improving Meta products through them. Meta AI researchers have managed to get self-supervised techniques that work remarkably well for images, where they take an image divided into small patches, ring 80% of these patches, and ask the AI to reconstruct the image, Pesenti said. He added that Meta AI researchers have shown that this new self-supervised technique, combined with a minimum amount of annotated data, is competitive against traditional approaches that use a lot more human supervision.
· Amazon:
Amazon is one of the largest online marketplaces & e-commerce companies. No wonder it is using machine learning to show product recommendations, understand user needs and choices. You can also use its voice assistant to place your items in the cart or ask your queries to the chatbot assistant. These solutions use different machine learning techniques with natural language processing, neural networks, etc., to synthesize user requirements and demands. The search box where you search your item or product is not just a regular search box. It uses machine learning for predicting the context for its customers’ search queries. It aids in enhancing the overall shopping experience of each user. Another massive approach that Amazon has done is utilizing ML solutions for AWS. AWS cloud is smart enough to understand user storage and computation requirements. Its smart home device is another product that recognizes user patterns and choices through machine learning. Lastly, Amazon also caters to the web series and movie recommendations depending on your previous tastes and preference.
· Netflix:
When you hear about on-demand streaming platforms, the most prominent company that comes to your mind is Netflix. Personalization of movie or web-series recommendation is what it does very well. Netflix is well-known for this feature. This feature leverages machine learning, where it does the clustering of data such as users who watched movie A will more likely watch B. It analyzes the watch history and the genre; its algorithm tries to understand user taste. Accordingly, it proposes the recommendation to keep the user in constant touch with the app. Netflix also plugs an ML algorithm that takes the previous viewing data as input to predict the bandwidth and the region where streaming needs a regional cache server. Every country does not have the same internet speed, and the data transfer rate also varies. Netflix has its algorithm that decides where to use the cache servers for quicker load time to reduce latency during peak requests.
· Google:
Machine learning has been a part of Google for quite a long time now. Google knew that machine learning solutions would be the future to solve advanced problems and the only way to stay ahead of the contenders. That is why in 2014, Google bought the startup DeepMind at $400 million. Machine learning has been a part of Google for quite a long time now. Google knew that machine learning solutions would be the future to solve advanced problems and the only way to stay ahead of the contenders. That is why in 2014, Google bought the startup DeepMind at $400 million. Google uses AI for a large number of routine tasks. The search engine uses ML to understand user behavior and search needs. The product Gmail uses ML for various checks, especially to check and eliminate spam emails. In the last decade, the company introduced a new ML framework called TensorFlow. It allows developers to train ML models more effectively. Google Duplex and Google assistant uses ML along with natural language processing to understand human voice and task.
Devices like Google Home, Android, and other Google products use Google Assistant to make everyday tasks efficient and fast through voice commands. Here it uses ML to understand user pronunciation and parse it into meaningful statements through NLP. Google Translate is another product that uses ML. It leverages Statistical machine translation (SMT) that reviews millions of already-translated sentences and words from one language to another. It uses those translated documents as datasets and treats them as input to train its translation model.
Conclusion –
ML projects can be reused. There is an awesome marketplace for this. Visit ProjectPro — an awesome marketplace for reusable ML projects. All the big companies are taking an interest in developing reusable, impactful machine learning solutions. AI-driven products might not be a luxury but a must-have technological breed. It helps in driving the companies to stay ahead of the competitive business environment. That is why most MNCs and midsized companies are implementing ML in different spheres of business development.
