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How to pitch Vertex AI in 2023?

Updated: Jul 1, 2023

In 2023, AI and ML consulting firms, such as TensorOps, are experiencing a high demand due to the increasing volume of announcements about AI, which is leaving businesses overwhelmed. Following OpenAI's successful execution of the biggest AI-powered marketing campaign in human history, companies are keen to understand the implications of AI on their respective domains and how they can capitalize on it.

A critical decision point for businesses that implements AI is selecting the right AI platform. It's worth noting that TensorOps, along with Google Cloud vendors, operate in a "Geek-to-Geek" (G2G) business model. In such a model, establishing professional trust plays a crucial role in selling a cloud platform. To shed light on the "why GCP for AI?" question, we would like to present some genuine and persuasive arguments.

Main takeaways: 1. GCP has a wide range of services 2. Vertex AI is designed for developers 3. Access to low cost hardware 4. Google's AI roadmap 5. Advanced data solutions 6. Consulting ecosystem

GCP is a real cloud

Hidden Technical Debt in Machine Learning Systems. Source: 2015 Sculley et al

While it may be relatively easy to build a simple AI model that can produce impressive results, the real complexity of ML systems lies in the supporting infrastructure and the full product design.

To create a successful ML system, it's not enough to simply train a model on some data and call it a day. Your customers need to ensure that the data they are using is of high quality and representative of the problem you're trying to solve together. They also need to have a robust infrastructure in place to support the deployment and operation of the model.

This includes things like monitoring, logging, and alerting to detect and resolve issues with your system in real-time. It also includes having a comprehensive data pipeline to handle data ingestion, transformation, and storage. Additionally, you need to consider the overall product design, including user experience, security, and scalability.

Platforms like Google Cloud, and specifically Vertex AI, offer a wide range of tools and services that can help you build and manage a successful ML system. From data storage and processing to model training and deployment, these platforms provide a comprehensive set of services that can help you create a robust and scalable AI solution.

Choosing a platform that integrates well many essential services for development will pay off later when your R&D runs into the real technical debt of AI systems.

Generative AI studio is made for developers

Google's Generative AI Studio is a closed beta offering that's designed specifically for developers who want to build custom AI models using Google's proprietary models. One of the key benefits of the Generative AI Studio is the level of customization it offers. Developers can feed hints, customize models, and change parameters to create AI solutions that are tailored to their specific needs. This level of customization allows for more reliable results and can be crucial when trying to build AI solutions with actual business impact.

Overall, the Generative AI Studio is a valuable offering for developers who want to build custom AI solutions that can have a real business impact. By leveraging Google's proprietary models and infrastructure, developers can create powerful and reliable AI models that can help to solve a wide range of business problems.

Google's Generative AI Studio allows tuning models for business needs. Source: screenshot

Google's specialized AI hardware reduces costs

Back in 1969, the Concorde was a groundbreaking airplane that could travel at speeds greater than the speed of sound, enabling commercial flights from Paris to New York City in just 3.5 hours - half the time it typically took other commercial planes to make the same journey.

However, despite the impressive speed and capabilities of the Concorde, issues related to cost and reliability eventually led its operators to give up on it. While the Concorde could make the journey faster than any other plane, it was also much more expensive and less reliable than other commercial airplanes. Similarly, Large Language Models (LLMs) like GPT-3 and GPT-4 are like airplanes that can fly from Paris to NYC in just one hour. These models have the potential to provide significant benefits in natural language processing and other fields, but they also come with challenges related to cost and reliability.

By using dedicated AI hardware like TPUs, Google can offer more cost-effective AI services that make use of LLMs. While LLMs still require significant computational resources to train and run, using TPUs can help to reduce the overall cost of using these models.

Alphabet will continue to drive innovation

Looking back at the last few years, many of the major breakthroughs in AI can be attributed to two subsidiaries of Alphabet: DeepMind and Google Brain. These groups are responsible for creating revolutionary technologies like AlphaGo, DeepQLearning, PaLM, and groundbreaking articles such as "Attention is All You Need," which laid the groundwork for GPT. While it may seem like there is a new AI model released every other day, it's important to note that Alphabet is the only cloud vendor with such leading AI research institute in-house. Many other research institutes rely on Alphabet's publications and open-source software like TensorFlow and JAX for their own progress.

Attention is all you need, Google's phenomenal article that laid the foundations to GPT. Source: arxiv 706.03762

For companies looking to build long-lasting solutions, betting on the latest short-term trend in AI can be risky. Instead, it may be wiser to invest in a company like Alphabet that has a strong foundation for continued innovation and a track record of producing groundbreaking technology through their AI research subsidiaries.

BigQuery and Matching Engine

If you're implementing AI, it's crucial to have excellent data solutions. BigQuery and Matching Engine are two of the most advanced tools for serving AI apps.

Matching Engine allow for the storage of vectors, which are the actual outputs of AI models before they are translated back into other media such as text or images. Vector databases power some of the most impressive AI applications, including advanced search engines, recommendation engines, and question-answering apps.

Example architecture for search using GCP

Google's Matching Engine is a cloud-native vector database that implements the state-of-the-art algorithm called ANN. Thanks to this algorithm, the Matching Engine can retrieve results with unparalleled efficiency, which greatly reduces the cost of performing queries.

On the data warehouse side, BigQuery is an indispensable tool for customers using Google Cloud Platform. This powerful tool can efficiently store and process massive amounts of data, reaching petabyte scale, with exceptional performance. Beyond the standard data analytics and processing features necessary for any AI/ML application handling tabular and events data, BigQuery also provides specialized features for machine learning, such as model training and explainability. With these additional capabilities, users can easily build and deploy machine learning models on their large datasets, and understand the reasoning behind the models' predictions. This makes BigQuery a highly versatile tool, ideal for any technical professional looking to develop sophisticated AI/ML applications.

Google has an amazing consulting ecosystem

Although the Google Cloud platform is often self-explanatory and the documentation for many services is superb, the learning curve for GCP can be steep. After all, becoming an expert cloud engineer takes time and effort, and it's not something that can be achieved overnight.

Gad Benram, CTO @ TensorOps teaching a Google Cloud AI workshop

That's why Google has invested in multiple channels to help its users find the assistance they need. Google Developer Experts create and publish plenty of online content in multiple languages, providing valuable resources to help users learn and understand GCP.

In addition, Google offers to connect businesses with excellent partners for reselling, training, and technical support. Working with a partner that has helped hundreds of customers build similar apps can reduce project risks and speed up the adoption of innovative technologies, helping businesses achieve their goals more quickly and efficiently.


TensorOps is a leading AI consulting partner. For more details about how to work with us leave us a message on the website or at


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