Google is the world's leading Artificial Intelligence company. This enterprise has become a benchmark in this technology thanks to its strong commitment in recent years. Not only through internal innovation, but also thanks to the acquisition of around twenty startups that offered specific solutions for different areas within AI. 

In particular, its Machine Learning tools have reached a high level of development and efficiency. As a result, Google offers an enormous catalogue of ML tools that gives highly satisfactory results for specific use cases. 

Google's technology is available for companies all over the world to adapt them to their realities in order to optimize their processes. However, although they can do this directly, these tools require a high level of qualification. For this reason, it is best to rely on Google's partners: companies that are experts in its solutions and know how to get the most out of Machine Learning for each specific situation. 

Google has a partner directory that lists both the technical experience and the specializations they possess. Thus, if a company needs an ML provider, it simply has to search in the directory and select, considering the characteristics best suited to its requirements.

Why choose a Google Cloud Machine Learning Specialist?

Not all partners are certified as Machine Learning Specialists by Google Cloud. Achieving it involves going through a validation process that takes several months. And not all companies that undergo this certification manage to complete it with a positive result.

The ML specialization awarded by Google is not just a sign that the partner knows the catalogue of tools or theoretical knowledge of Machine Learning. In order to achieve this certification, it is necessary to have a significant track record of success. There are only 42 companies in the world with this specialization. And of all of them, only one is 100% Spanish: Emergya.

But what does it mean to be a Google Cloud Machine Learning specialist? Achieving this certification involves having successfully passed a verification process in which both the partner's knowledge and the results achieved by its customers are demonstrated. It also guarantees that other requirements are met, such as having a business plan based on this specialization.

Highly qualified staff in ML

One of the preliminary requirements of the specialization is related to staff training. In fact, at least four people must hold the Google Cloud Data Engineer certification. This guarantees that, within the partner, there is a group of professionals who have in-depth knowledge of all Google's ML tools and solutions. 

Those with this certification can design, compile, operate, secure and monitor data processing systems. This matter takes into account issues such as security, scalability, efficiency, portability and flexibility. It must be able to make data-driven decisions by collecting, transforming and publishing data.

Customer success stories

Being a specialist in Machine Learning by Google Cloud does not just mean having theoretical knowledge of ML and Google tools. It means knowing how to get the most out of them. Therefore, an essential part of the certification process concerns the projects that the partner has developed. Thus, they must provide a minimum of three cases in which, thanks to the application of Machine Learning techniques, they helped their clients to achieve success.

Business plan

Specialization implies a long-term commitment of the partner to Google ML technology. In this way, the process evaluates what has been done so far and also the future goals. The partner must present a Business Plan that contemplates investment and recruitment in the Machine Learning area on an ongoing basis.

Google Cloud ML technical training

To summarize what has been explained so far: becoming a Google Cloud Machine Learning specialist is not a simple process. Any of the 42 global partners listed in the directory are there because they have proven to have more than enough skills and experience to carry out ML projects. In other words, they are capable of:

• Explore and process data
• Evaluate and implement models
• Perform online forecasting
• Make use of Google's machine learning APIs

In short, you should have an in-depth understanding of the products and tools housed in Vertex AI - which until May 2021 was known as AI Platform Unified. Among them, the best known are TensorFlow, Cloud Natural Language API, Cloud Video Intelligence API and Cloud Vision API, among others.

In addition, validation is carried out by an external auditor. This company verifies that each of the requirements demanded by Google to grant the ML specialization are met. During this validation, the partner has to provide the required documentation and also three practical demonstrations. The approach, technical design and code are assessed. 

ML's specialization also puts a lot of emphasis on everything related to the security of the projects. In other words, the partner must demonstrate knowledge of how to develop them and how to deal with privacy and security. It is valued, for example:

• How they ensure that confidential training data stored in GCP is well protected. 
• How the processes of protection (masking, grouping, etc.) of datasets are carried out, etc... 

Security and anonymization of data provided by customers are also taken into account.

In conclusion, as Google explains on its partner portal, "specializations are the ultimate indicator of knowledge and experience in the Google Cloud". Not only do they represent recognition, but they are also a seal of guarantee that very few companies can boast worldwide.