As the field of AI and machine learning continues to evolve, pre-trained language models like ChatGPT/GPT-4 have emerged as powerful tools for natural language processing tasks. In a recent tweet, Daniel Bourke posed the question, "Why bother building your own custom ML models when ChatGPT/GPT-4 will be better?" It's a valid question, given the impressive capabilities of pre-trained language models like ChatGPT/GPT-4. However, there are still several compelling reasons to build custom ML models, even in the face of such impressive technology. If you are interested in learning more about AI and machine learning, you may want to check out Daniel Bourke's Twitter and Medium accounts, as well as his YouTube channel.

With over 210 million daily active users, Snapchat remains one of the top applications that people use to enjoy augmented reality experiences. The app's wide variety of AR features lets users do everything, from giving their digital selves some doggie ears to adding realistic 3D effects and transformations. Snap has always been proactive about letting people come up with new ideas and designs, with Lens Studio allowing developers to "create, publish, and share Lenses" across the platform. To date, Snap reports over 1 million custom Lenses created. With the Lens Studio 3.0 update, Snap has now introduced new features, such as voice command search for Lenses, using custom ML models and ML templates within Lens Studio, and gathering visual data from Snaps for 3D geographic mapping.

Each day at Zulily we add 9,000 products to our online store and process more than 5 billion clicks from online shoppers. That is more virtual inventory than you'll find in the warehouses of many retailers, and it's by design. We've built a supply chain where we hold only some goods: most of the time, we don't purchase inventory until our customers have, so we are able to pass down savings from our unique supply chain down to our customers around the world. To the customer, that means a constantly changing and new shopping experience. Our system works only if we can ensure that both shoppers and suppliers move quickly.

A look at how Zulily is using the latest tools in artificial intelligence, machine learning, and cloud computing to innovate and serve its customers with purpose. Each day at Zulily we add 9,000 products to our online store and process more than 5 billion clicks from online shoppers. That is more virtual inventory than you'll find in the warehouses of many retailers, and it's by design. We've built a supply chain where we hold only some goods: most of the time, we don't purchase inventory until our customers have, so we are able to pass down savings from our unique supply chain down to our customers around the world. To the customer, that means a constantly changing and new shopping experience.

One year ago, we launched our Nuxeo Insight service and became the first and, at that time, only Content Services Platform to offer a trainable cloud service for machine learning. For those who might not be familiar with this unique service, Nuxeo Insight is an AI offering that enables our clients to use their own data and content to train custom, machine-learning (ML) models. Custom ML models can be used for a variety of business purposes, including enriching content with new metadata, auto-classifying vital records, identifying products and talent, and even automating forms processing. But the critical thing with Nuxeo Insight is that, because these models are trained with each customer's own data, they are much more accurate, insightful and therefore valuable than the commodity AI services that are available as public cloud offerings. If you are interested in learning more about the distinction between custom and commodity ML models, please refer back to a previous Nuxeo blog posting entitled, "The Difference Between Generic & Contextual AI." Today, we are very pleased to announce another first for Nuxeo Insight.

As the industry moves towards no-code artificial intelligence model training platforms, AI is being positioned as a tool for the masses. It, therefore, comes as no surprise to find that the industry bellwethers are releasing no-code or low-code platforms to build custom machine learning models that can be used with ease and security. Joining the big tech giants is China's internet major Baidu that launched an AI platform designed to make building custom ML models easier and it rules out the need for algorithmic programming. Known as EZDL, the service platform enables developers to build custom ML models with a drag-and-drop interface, Yongkang Xie, tech lead of Baidu EZDL, said in a company statement. He further emphasised developers can build deep learning models which are specific to their business needs only in four steps.

Yesterday, tech giant Google announced its latest solution, the Cloud AutoML, that will enable developers, even those that lack machine learning expertise, to build image recognition models. It is said to be a part of the company's initiative to democratize AI learning and provide a simple approach that anyone can easily understand. "Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses," Fei-Fei Li, Google Cloud AI chief scientists, and Jia Li, Google Cloud AI Head of R&D, wrote in the company blog. According to the duo, their latest solution would help businesses with limited machine learning expertise build "their own high-quality custom models by using advanced techniques like learning2learn and transfer learning from Google." The two believe that Cloud AutoML will make experts in artificial intelligence more productive and take the technology to greater heights while helping less-skilled engineers build more powerful machine learning systems.

When we both joined Google Cloud just over a year ago, we embarked on a mission to democratize AI. Our goal was to lower the barrier of entry and make AI available to the largest possible community of developers, researchers and businesses. Our Google Cloud AI team has been making good progress towards this goal. In 2017, we introduced Google Cloud Machine Learning Engine, to help developers with machine learning expertise easily build ML models that work on any type of data, of any size. We showed how modern machine learning services, i.e., APIs--including Vision, Speech, NLP, Translation and Dialogflow--could be built upon pre-trained models to bring unmatched scale and speed to business applications.

IBM Watson Developer Cloud (WDC) services put the power of machine learning technology in the hands of developers to extract insights from unstructured data (text, speech, and images). To serve developers and enable them to tackle a wide spectrum of applications ranging from general consumer applications to various enterprise-specific applications, the IBM Watson team offers several pre-trained services as well as a rich set of customization capabilities. For the pre-trained services, the IBM Watson team has taken on the responsibility of acquiring the right data to train these services, generating trained machine learning (ML) models and providing out-of-the-box functionality for developers. Natural Language Understanding (NLU), Personality Insights (PI), Tone Analyzer (TA), Speech-to-text (STT), Language Translator (LT), and Visual Recognition (VR) are some of the pre-trained WDC services. Developers like these services because they're intuitive, easy-to-use, require no extra ML training effort and work well for applications tackling a general domain such as enriching web URLs, image tagging or analyzing sentiment of social media posts.