The presence of ML and AI in today’s society is becoming more and more ubiquitous. Large companies such as Netflix, Amazon, Facebook, Spotify, and many others are adopting artificial intelligence and machine learning solutions that interact directly (often backstage) with consumers daily. In this article, we will present five companies that implement MLOps and benefit from this ML-based methodology. MLOps consulting companies can support you in the development of presented solutions.
Shortly put, MLOps is a methodology consisting of practices that help deploy and manage ML models efficiently.
What are the benefits of implementing MLOps for companies?
Companies benefit enormously from implementing MLOps. The most important benefits are:
- Efficiency – MLOps allows data teams to develop models more quickly, increase the quality of models, and improve production speed
- Risk reduction – MLOps allows a faster response to the request and greater compliance with regulations
- Scalability – MLOps enables close cooperation between data teams and thus reduces conflicts with IT specialists
Companies implementing MLOps
NETFLIX
Today, probably everyone knows the Netflix application. While many people may not realize it, Netflix implements many machine learning models in several areas. Overall, Netflix’s use of ML focuses on UX optimization through personalization. Here are some use cases for machine learning in Netflix:
- Catalog composition.
- Optimization of the quality and transmission of the content stream: Using past viewership data to predict bandwidth usage helps Netflix decide when to cache regional servers for faster load times during peak demand.
- Personalized movie recommendations: users who like movie A are likely to watch movie B – that’s how personalization works. This is perhaps the most prominent feature of Netflix. Netflix uses like-minded users’ viewing history to recommend what you might be most interested in watching next. This solution helps keep users engaged.
- Detection of anomalies in the user registration process.
It is widely believed that Google is the world’s most advanced company in the field of ML. Google uses ML in Gmail, Google Assistant, Google Translate, Google Music, and Google Adsense.
Let us now look at some of the Google-derived MLOps examples:
Google Translate
If you want to translate a document without knowing the target language quickly, Google Translate is a tool to help you with that. It might not translate the text 100% accurately, but it does an excellent job of converting text from one language to another in real-time. It uses ML for this – it analyzes millions of already translated documents and searches for common patterns and basic vocabulary.
Google Assistant
It is an intelligent personal assistant created by Google, available for mobile devices and smart home devices. We can have a normal conversation with it, ask it questions, and give it instructions. Actually, it’s designed as a chatbot. This technology combines Google Knowledge Graph, image recognition, and a popular machine learning field called natural language processing (NLP).
Google Cloud
Google Cloud Platform is an excellent environment for the development of applications that use machine learning (ML) and artificial intelligence (AI). When using GCP, you have access to many universal tools and services. They can be useful regardless of the industry in which you operate. Here are some of the solutions provided by GCP:
- Google BigQuery (data analysis)
- Google Cloud Storage (virtual data storage disk)
- Google Cloud loT (allows you to manage devices located in different parts of the world in real-time)
MICROSOFT
With machine learning, Microsoft recognizes patterns, improves operations, and develops better products. As a result, Microsoft enables users to find new ways to incorporate computing into their daily lives. Some Microsoft products that use ML are:
- Outlook: It’s an email application. It uses machine learning to suggest to users when to read the email. Moreover, it can read it for the user and even suggest quick responses to received messages.
- PowerPoint: here, Microsoft uses ML for the analysis of the structure of text and slides, content recommendation, and arrangement proposals.
It is one of the most popular social media platforms. Facebook uses machine learning algorithms to:
- Determine what users see in their feed
- Determine what ads to display
- Predict the type of search users are interested in
- Enable face detection (Facer)
- Detect anomalies and classify content (Sigma)
UBER
Uber is a tool that connects drivers and passengers who need a ride. When you’re in a city where Uber is available and you need a ride, just use the app to find the nearest Uber-supported driver quickly. A large part of Uber’s services is based on machine learning solutions. For example, Uber’s ML algorithms:
- Estimate travel time
- Optimize maps
- Determine the location of the driver
- Determine the optimal toll
Conclusion
Modern AI-centric organizations should have MLOps as a part of their IT strategies. This article describes how the leading companies use MLOps. We hope that it will motivate you to start your adventure with machine learning operations.
