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At the recent Microsoft Build conference, alongside 50+ AI updates, emerged a drinking game — take a sip every time Satya Nadella says “AI” or “Copilot”. And Twitter users warned of a potent hangover, as Microsoft integrated Copilot into almost every offering. Nevertheless, the tech giant also unveiled upgrades like ChatGPT integration to Bing, Microsoft Fabric, AI Hub, Azure AI studio, plugins and more.
Out of all, Azure AI received quite a lot of upgrades consisting of Azure AI Studio, provisioned throughput model, and plugins for integrating external data sources. Its content safety feature can detect harmful content in images and texts.
At Google I/O 2021, Microsoft’s friendly nemesis launched Vertex AI to build, deploy, and scale machine learning models faster and easier. Azure AI is a similar cloud-based service platform offering similar features. Now, let’s look at the difference between the two.
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Vertex AI Vs Azure AI
One of Vertex AI’s key advantages is its support for MLOps practices. It provides tools and features for scaling, managing, monitoring, and governing ML workloads, ensuring efficient and responsible ML development. Additionally, Vertex AI optimises infrastructure, reducing training time and costs. The platform offers comprehensive ML tooling, including APIs, foundation models, and open source models through the Model Garden. It also provides end-to-end MLOps capabilities, automating and standardising ML projects throughout the development life cycle.
On the other hand, Azure ML is a key component in providing developers with tools to construct, train, and implement machine learning models. It supports popular programming languages like Python and R, enabling efficient model building and deployment. Vertex AI emphasises the integration of data and AI, seamlessly integrating with popular tools like BigQuery, Dataproc, and Spark.
This integration allows users to leverage BigQuery ML and facilitates accurate data labelling through Vertex Data Labelling. On the engineering side, Azure Databricks allows effective processing and analysis of large datasets. It supports Python, R, and Scala, facilitating data manipulation and preparation for accurate machine learning models. Just like Azure, Vertex AI caters to users with varying expertise levels through its low-code and no-code tooling.
Another feature that Microsoft brings to the table is its Azure Bot Service, tailored for chatbot and conversational AI applications. It simplifies development and deployment, improving customer service and automating processes.
Similarly, Google’s Dialogflow service is a cloud platform for constructing and launching conversational AI agents. It boasts a comprehensive suite of capabilities, including an adept natural language comprehension mechanism, a dialogue management engine capable of generating and answering diverse prompts and inquiries, a collection of pre-designed intents and entities for swift and effortless creation of intricate chatbots, as well as integrations with various Google Cloud Platform services like Firebase Cloud Storage.
Finally, Vertex AI offers an open and flexible AI infrastructure, supporting various ML infrastructure and model deployment options. It seamlessly integrates with MLOps tools, enabling users to scale model deployment, reduce inference costs, and effectively manage models in production.
Big Bets on AI Studio
Google launched Generative AI Studio at its recent developer conference that allows users to interact, fine-tune, and deploy foundational models. It offers features like chat interface, prompt design, and model weight adjustment. Their Model Garden provides enterprise-ready foundation models, task-specific models, and APIs. Users can utilise models directly, fine-tune them in the Generative AI Studio, or deploy them to data science notebooks.
Following Google’s suit, Microsoft also launched Azure AI Studio, enabling developers to create personalised AI chatbot copilots using OpenAI models and their own data. This new feature enhances Microsoft’s Azure OpenAI Service by extending its AI capabilities. In January, Microsoft officially released Azure OpenAI Service, and in March, it announced the availability of OpenAI’s GPT-4 within the service.
Who is Winning?
Just like in any market, having a variety of options is good for the consumer. Both choices are equally valid, but they encounter a challenge in gaining widespread acceptance among businesses. Vertex AI entered the market as the pioneering product, yet it hasn’t yet gained significant traction among enterprises. The current resistance stemming from established data frameworks hinders companies from transitioning directly to Vertex. It is likely that Microsoft’s offering will encounter similar obstacles.
Increasing competition is yet another obstacle, as according to several users, AWS leaves Azure and Vertex behind. AWS may be the better choice if your work involves Linux programming, while Azure aligns well with Microsoft users. AWS provides greater depth and control but has a higher learning curve. It also boasts a larger online community. Azure is favoured by established companies, while AWS is popular among start-ups. Along similar lines, many believe AWS and Google Cloud Platform have more value than Azure, but Azure is still useful. But with the integration of AI in Azure products, the scenario might be changing.
Nevertheless, both these services reduce the obstacles for developers who are eager to delve into the AI-driven data science ecosystem.
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