I recently earned Google Professional Machine Learning (ML) Certification after weeks long of studying and solving mock exams. In this article, I will share with you the importance of the certification, six services you must master to pass the exam, and a few tips that helped me prepare for the certificate.

Why is Google Professional Machine Learning Certification Important

Earning Google Cloud Platform (GCP) Professional Machine Learning Certification is a prestigious accomplishment, especially considering Google’s leadership in the field of machine learning. This certification validates your expertise in designing, developing, and deploying advanced ML models using the Google Cloud Platform. You'll establish yourself as a skilled and knowledgeable ML professional by demonstrating your mastery of Google Cloud tools and services for solving complex business challenges. With this certification, you’ll have a competitive edge over others in the field and be well-positioned for exciting opportunities in the ever-growing domain of machine learning.

Photo by Pawel Czerwinski on Unsplash

Google Professional Machine Learning vs. AWS Certified Machine Learning Specialty

The Google Professional Machine Learning Certification is equivalent to AWS Certified Machine Learning Specialty. In my opinion, Google’s certificate is much more challenging than the AWS certificate to earn, as Google’s certification focuses on specific GCP services in addition to advanced machine learning concepts. In contrast, the AWS certificate gives more weight to advanced machine-learning concepts, so if you’re a beginner with AWS (AWS Practioner level) and an expert in Machine Learning, you can probably pass the AWS certification exam. Still, for Google’s certification, you must know GCP services in depth (GCP Cloud Engineer Certified) and advanced machine-learning concepts to pass the certification exam.

Both certificates are valuable, and if you want to show Machine learning mastery in multiple clouds, then get both certificates. That’s a lot of work and studying, but it's worth it.

Services you have to Master to Pass Google Cloud Machine Learning Professional Certification Exam

As discussed earlier, to pass the exam, you have to master GCP Machine Learning services. Below are the most essential GCP Machine Learning services.

1- Vertex AI

Google Cloud Platform Vertex AI is a fully managed Machine Learning platform that allows you to quickly build, deploy, and manage ML models at scale. It provides a wide range of tools and services to help you streamline your ML workflows and accelerate your AI development.

Key features and benefits of GCP Vertex AI:

1. AutoML Vertex AI provides a suite of AutoML tools that allow you to quickly build and deploy custom ML models without requiring extensive knowledge of ML.
2. Pre-built models Vertex AI provides pre-built models for common use cases, such as image and speech recognition, natural language processing, and more.
3. Model serving Vertex AI allows you to easily deploy your models to production and manage their lifecycle, including monitoring and scaling.
4. Integration with other GCP services Vertex AI integrates seamlessly with other GCP services, such as BigQuery, Dataflow, and Cloud Storage, to provide a comprehensive ML solution.
5. Security and compliance Vertex AI provide robust security and compliance features to protect your data and models.

You should consider using GCP Vertex AI if you need to:

• Build custom ML models: If you need to build custom ML models for your business, Vertex AI’s AutoML tools can help you get started quickly, even if you don’t have extensive knowledge of ML.
• Deploy ML models at scale: If you need to deploy ML models to production and manage their lifecycle, Vertex AI’s model-serving features can help you do so quickly and efficiently.
• Work with pre-built models: If you need to use pre-built models for common use cases, such as image recognition or natural language processing, Vertex AI provides a wide range of pre-built models that you can use out-of-the-box.
• Integrate with other GCP services: If you’re already using other GCP services, such as BigQuery or Cloud Storage, Vertex AI’s seamless integration with these services can help you build a comprehensive ML solution.

Overall, GCP Vertex AI is a powerful ML platform that can help you accelerate your AI development and streamline your ML workflows.

Use Vertex AI when you want flexibility, build and train your models, and broader GCP integration. This is an end-to-end ML service, from training to serving models.

2- AutoML

Google Cloud Platform AutoML is a suite of machine learning (ML) products that allow you to quickly build custom ML models without requiring extensive knowledge of ML. It provides various tools and services to help you streamline your ML workflows and accelerate your AI development. Most importantly, AutoML lets you train tabular, image, text, or video data without writing code or preparing data splits.

Key features and benefits of GCP AutoML:

1. Custom models AutoML allows you to build custom ML models that are tailored to your specific use case, even if you don’t have extensive knowledge of ML.
2. User-friendly interface AutoML’s user-friendly interface makes it easy for you to build and train your ML models without needing to write complex code.
3. Automated model selection AutoML automatically selects the best ML model for your use case based on the data you provide and your performance goals.
4. Integration with other GCP services AutoML integrates seamlessly with other GCP services, such as BigQuery and Cloud Storage, to provide a comprehensive ML solution.
5. Security and compliance AutoML provides robust security and compliance features to protect your data and models.

You should consider using GCP AutoML if you need to:

• Build custom ML models: If you need to build custom ML models for your business, AutoML’s user-friendly interface can help you get started quickly, even if you don’t have extensive knowledge of ML.
• Optimize model performance: If you want to optimize the performance of your ML models, AutoML’s automatic model selection can help you select the best model for your use case.
• Integrate with other GCP services: If you’re already using other GCP services, such as BigQuery or Cloud Storage, AutoML’s seamless integration with these services can help you build a comprehensive ML solution.
• Ensure security and compliance: If you need to ensure the security and compliance of your data and models, AutoML provides robust security features that can help you achieve this.

Overall, GCP AutoML is a powerful suite of ML tools that can help you build custom ML models quickly and easily, optimize their performance, and integrate them with other GCP services.

Use AutoML when you want to train a model but not write code and implement data splits if you want a model trained and built quickly with little or beginner ML knowledge.

3- Big Query

Google Cloud Platform BigQuery is a fully managed, serverless data warehouse that allows you to store and analyze massive amounts of data quickly and easily. It is a robust platform that provides various tools and services to help you work with large datasets, including SQL-like queries, real-time analytics, and machine learning.

Key features and benefits of GCP BigQuery:

1. Scalability BigQuery is designed to handle large datasets and can scale to meet your needs, so you don’t need to worry about managing infrastructure.
2. Real-time analytics BigQuery allows you to analyze your data in real time, so you can get insights quickly and make data-driven decisions faster.
3. SQL-like queries BigQuery supports SQL-like queries, making it easy for you to work with your data, even if you don’t have extensive programming knowledge.
4. Machine learning BigQuery integrates with other GCP services, such as AutoML and AI Platform, to provide machine learning capabilities to help you analyze your data more effectively.
5. Security and compliance BigQuery provides robust security features to protect your data and ensure compliance with industry standards.

You should consider using GCP BigQuery if you need to:

• Work with large datasets: If you have large datasets that you need to analyze quickly, BigQuery can easily handle them.
• Analyze data in real-time: If you need to analyze your data in real time and get insights quickly, BigQuery can help you do so.
• Use SQL-like queries: If you’re familiar with SQL, BigQuery’s SQL-like queries make it easy for you to work with your data and extract the information you need.
• Integrate with other GCP services: If you’re already using other GCP services, such as AutoML or AI Platform, BigQuery’s seamless integration with these services can help you build a comprehensive data analytics solution.
• Ensure security and compliance: If you need to ensure the security and compliance of your data, BigQuery provides robust security features that can help you achieve this.

Overall, GCP BigQuery is a powerful data analytics platform that can help you work with large datasets, analyze your data in real time, and extract insights quickly and easily.

Use BigQuery when you have structured data, SQL format, data pre-stored or can be stored in SQL database, or a lot of data. Use BigQuery to build modes, split data, and train models. BigQuery can’t be used for serving models.

4- GCP Pre-Built Models

Google Cloud Platform offers a range of pre-built services that allow you to quickly implement common use cases without building everything from scratch. These pre-built services are designed to help you quickly deploy and integrate advanced functionality into your applications.

Here are some examples of pre-built services offered by GCP and when to use them:

1. Cloud Vision API This service allows you to add image recognition and analysis capabilities to your applications. You can use this service to detect objects, faces, and landmarks in images and to classify images into categories like “nature,” “food,” and “sports.” You can use Cloud Vision API to analyze images for various purposes, such as detecting inappropriate content, identifying products, or detecting logos.
2. Cloud Translation API This service provides machine learning-based language translation capabilities, allowing you to translate text between languages in real time. You can use this service to support multilingual content in your applications, translate user-generated content, or provide real-time translation in customer support chatbots.
3. Cloud Speech-to-Text API This service allows you to transcribe audio files into text. You can use this service to transcribe recorded phone calls, podcasts, or video content or to provide real-time transcription in voice-enabled applications.
4. Cloud Natural Language API This service provides natural language processing capabilities, allowing you to analyze and understand text content. You can use this service to extract key phrases, entities, and sentiments from text or to classify text into categories like “news,” “reviews,” or “social media.”
5. Cloud Video Intelligence API This service allows you to analyze video content for various purposes, such as detecting objects, faces, and logos in videos, and to classify videos into categories like “news,” “entertainment,” and “sports.”

Overall, the pre-built services offered by GCP provide a range of advanced capabilities that can help you enhance your applications with machine learning-based functionality, even if you don’t have extensive ML or data science expertise. These services can help you save time and resources by leveraging pre-built models and APIs and can help you create more intelligent, interactive applications that can better meet the needs of your users.

Use GCP pre-built models when you don’t have ML expertise or want to call an API to make inferences. Pre-built models can’t be customized and will work on easy common ML problems, not those that need custom training. For example, Cloud Vision API can detect logos of major brands but not a small startup that you just founded, basically because Cloud Vision API didn’t see your newly founded startup logo previously. To detect your newly founded startup logo, use AutoML.

5- DataProc

Google Cloud Dataproc is a fully managed cloud service that allows you to quickly run Apache Hadoop, Apache Spark, and other big data processing frameworks on GCP. Dataproc provides a fast, easy, and cost-effective way to process large amounts of data and perform complex data analysis tasks.

Key features and benefits of Google Cloud Dataproc:

1. Managed service Dataproc is a fully managed service, meaning that Google takes care of the underlying infrastructure, including provisioning, scaling, and monitoring of clusters.
2. Fast and scalable Dataproc provides fast and scalable processing of large data sets using Google’s high-performance computing infrastructure.
3. Integration with GCP services Dataproc integrates seamlessly with other GCP services, such as BigQuery and Cloud Storage, allowing you to import and export data to and from your clusters easily.
4. Cost-effective Dataproc is a cost-effective solution for big data processing, with pricing based on usage and no upfront costs.
5. Support for multiple big data frameworks Dataproc supports a variety of big data processing frameworks, including Hadoop, Spark, and Hive, allowing you to choose the proper framework for your specific use case.

You should consider using Google Cloud Dataproc if you need to:

• Process large amounts of data: Dataproc provides a fast and scalable way to process large amounts of data, making it a good choice for big data processing tasks.
• Perform complex data analysis: Dataproc supports a range of big data processing frameworks, allowing you to perform complex data analysis tasks, such as machine learning, graph processing, and SQL queries.
• Leverage other GCP services: If you’re already using other GCP services, such as BigQuery or Cloud Storage, Dataproc’s seamless integration with these services can help you build a comprehensive big data processing solution.
• Scale processing power as needed: Dataproc allows you to easily scale your processing power up or down as needed, providing flexibility and cost-effectiveness.

Overall, Google Cloud Dataproc is a powerful and flexible big data processing solution that can help you perform complex data analysis tasks at scale. Its seamless integration with other GCP services, support for multiple big data processing frameworks, and cost-effective pricing make it an excellent choice for businesses of all sizes that need to process large amounts of data.

6- DialogFlow

Google Cloud Dialogflow is a conversational AI platform that allows you to build natural language understanding (NLU) and conversational interfaces for chatbots, voice assistants, and other conversational applications. It provides robust tools for designing, building, and deploying conversational interfaces that can understand and respond to natural language input.

Here are some of the key features and benefits of Google Cloud Dialogflow:

1. Natural Language Processing (NLP) Dialogflow uses NLP to understand natural language input, allowing you to build conversational interfaces that can understand and respond to user requests and commands.
2. Multi-platform support Dialogflow supports multiple platforms, including Google Assistant, Facebook Messenger, Slack, and more, allowing you to build conversational interfaces that can be deployed across multiple channels.
3. Integration with other GCP services Dialogflow integrates seamlessly with other GCP services, such as Cloud Functions and Cloud Storage, allowing you to build comprehensive conversational interfaces that can interact with other parts of your system.
4. Machine learning capabilities Dialogflow provides machine learning capabilities that can improve the accuracy and effectiveness of your conversational interfaces over time.
5. Pre-built agents and templates Dialogflow provide a range of pre-built agents and templates that you can use to build and deploy conversational interfaces quickly.

You should consider using Google Cloud Dialogflow if you need to:

• Build conversational interfaces: Dialogflow provides a robust set of tools for designing, building, and deploying conversational interfaces for chatbots, voice assistants, and other conversational applications.
• Understand natural language input: Dialogflow’s NLP capabilities allow you to build conversational interfaces to understand and respond to natural language input.
• Support multiple platforms: Dialogflow’s support for multiple platforms allows you to build conversational interfaces that can be deployed across multiple channels.
• Integrate with other GCP services: If you’re already using other GCP services, Dialogflow’s seamless integration with these services can help you build a comprehensive conversational interface that can interact with other parts of your system.

Overall, Google Cloud Dialogflow is a powerful and flexible platform for building conversational interfaces that can understand and respond to natural language input. Its NLP capabilities, multi-platform support, and seamless integration with other GCP services make it an excellent choice for businesses of all sizes that need to build chatbots, voice assistants, or other conversational applications.

Hints to Pass the GCP Professional Machine Learning Exam

You must know TFRecords, Google Tensor Processing Unit (TPU), and when to use them. Also, know basic Machine learning concepts like learning rate and batch size and how to balance them.

Photo by Nick Morrison on Unsplash

TFRecords

Google Cloud Platform’s TFRecords is a file format that stores and reads large amounts of data for TensorFlow, an open-source machine learning framework. TFRecords is a binary format that stores data as a sequence of records, each containing a set of features.

Here are some key features and benefits of using TFRecords:

1. Efficient data storage and reading TFRecords can efficiently store and read large amounts of data, making it ideal for working with large datasets.
2. Straightforward data serialization TFRecords uses Google’s Protocol Buffers, which allows straightforward serialization of data structures, making it easier to write and read data from multiple sources.
3. Enhanced data pre-processing TFRecords allows for the pre-processing of data in a format that can be easily consumed by TensorFlow, which can help optimize training time and improve model performance.
4. Efficient data distribution TFRecords are easily distributable across different machines, which makes them ideal for working with distributed machine learning frameworks.

You should consider using TFRecords in the following situations:

• When working with large datasets: If you are working with a dataset that is too large to fit in memory, TFRecords can be an efficient way to store and access the data.
• When working with distributed machine learning frameworks: If you are working with a distributed machine learning framework, TFRecords can be an efficient way to distribute data across different machines.
• When working with non-standard data: If you are working with non-standard data structures or data formats, TFRecords can help with data serialization and make it easier to preprocess the data for use in TensorFlow.
• When optimizing training time: If you want to optimize your training time, TFRecords can help with pre-processing and can help speed up the training process.

Overall, Google Cloud Platform’s TFRecords is a proper file format for storing and accessing large amounts of data in TensorFlow. Its efficient data storage and reading capabilities, straightforward data serialization, and enhanced data pre-processing make it an ideal format for working with large datasets and optimizing training time.

Use the TFRecords format for efficiency and speed.

Learning Rate and Batch Size

Learning rate and batch size are both essential hyperparameters in machine learning models that can significantly impact the model’s performance.

The learning rate determines the step size at which the model’s weights are updated during training. A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution, while a low learning rate can cause the model to take too long to converge or get stuck in a local minimum. Therefore, choosing an appropriate learning rate that balances convergence speed and accuracy is essential.

The batch size determines the number of samples used in each model iteration during training. Larger batch size can lead to faster training times, but it may also lead to overfitting and poorer generalization performance. On the other hand, a smaller batch size may lead to slower training times, but it may also result in better generalization performance. Therefore, choosing an appropriate batch size that balances training speed and generalization performance is essential.

To balance between learning rate and batch size, you can follow these general guidelines:

1. Start with a reasonable default value for both hyperparameters. For example, a learning rate of 0.001 and a batch size of 32 are common starting points for many models.
2. Experiment with different combinations of learning rates and batch sizes to find the optimal values. You can use techniques like grid search or random search to explore the hyperparameter space.
3. Monitor the training and validation performance of the model with different hyperparameter values to determine the optimal combination. You can use metrics like accuracy, loss, and validation score to evaluate the model's performance.
4. If the model is not converging or the validation performance is poor, try decreasing the learning rate and/or increasing the batch size.
5. If the model is overfitting or the training performance is poor, try increasing the learning rate and/or decreasing the batch size.
6. Repeat steps 2–5 until you find the optimal combination of hyperparameters.

Overall, finding the optimal combination of learning rate and batch size requires experimentation and careful evaluation of the model’s performance. By balancing between these two hyperparameters, you can train a machine learning model that achieves high accuracy and generalization performance.

Google TPU

Google TPU (Tensor Processing Unit) is a custom-built, application-specific integrated circuit (ASIC) designed by Google to accelerate machine learning workloads. TPUs are optimized explicitly for TensorFlow, Google’s open-source machine learning library, and can perform training and inference tasks significantly faster than traditional CPUs and GPUs.

Here are some key features and benefits of using TPUs:

1. High-performance TPUs are specifically designed to perform machine learning tasks and can perform specific tasks much faster than traditional CPUs and GPUs.
2. Scalability TPUs can be scaled up or down depending on the size of the workload, making them ideal for organizations with rapidly changing workloads.
3. Cost-effectiveness TPUs are optimized for machine learning workloads, which can result in lower costs compared to traditional computing solutions.
4. Ease of use TPUs can be used in conjunction with the Google Cloud Platform, making it easy to set up and use without the need for complex hardware configurations.
5. Improved accuracy TPUs can help improve the accuracy of machine learning models by performing more precise complex calculations.

You should consider using TPUs in the following situations:

• When working with large datasets: If you are working with large datasets that require significant processing power, TPUs can help speed up the processing time.
• When working with deep neural networks: TPUs are particularly effective in accelerating the training of deep neural networks, which can be computationally intensive.
• When working with time-sensitive applications: If you have time-sensitive applications that require real-time processing, TPUs can help improve the speed and accuracy of the application.
• When working with large-scale machine learning projects: If you are working on large-scale machine learning projects that require significant processing power, TPUs can help accelerate the project and reduce overall processing costs.

Overall, Google TPU is a powerful, cost-effective solution for accelerating machine learning workloads. Its high performance, scalability, and ease of use make it an ideal choice for organizations looking to improve the speed and accuracy of their machine-learning applications.

The choice between TPU and GPU

When dealing with machine learning problems on Google Cloud Platform (GCP), choosing between TPUs (Tensor Processing Units) and GPUs (Graphics Processing Units) depends on various factors such as the size of the dataset, the complexity of the model, and the processing time required.

Here are some general guidelines on when to use TPUs and when to use GPUs:

1. TPUs are better suited for large-scale machine learning workloads that require extensive computation power. They benefit deep learning workloads that involve training large neural networks with millions of parameters. Therefore, if you have a large dataset and a complex model architecture, TPUs might be the better choice.
2. GPUs are better suited for smaller-scale workloads that require fast and efficient computation. They are helpful for tasks such as image and speech recognition, natural language processing, and other tasks that require heavy computation. Therefore, GPUs might be better if you have a smaller dataset or a more straightforward model architecture.
3. TPUs are generally more expensive than GPUs, so cost is also a factor to consider. Therefore, if your budget is limited and your workload can be handled by a GPU, then consider using GPUs.
4. TPUs are optimized for TensorFlow, while GPUs are compatible with a broader range of machine learning frameworks. Therefore, if you are working with TensorFlow, TPUs might be the better choice, but GPUs might be more suitable if you are working with another machine learning framework.

In summary, the choice between TPUs and GPUs depends on the size and complexity of the dataset and model, the required processing time, the budget, and the machine learning framework used. It is essential to carefully evaluate these factors before deciding which platform to use.

Get the ML Basics

Refresh your Machine Learning knowledge by going through Google’s ML crash course.

Read my previous articles on ML concepts and AWS ML certification.

Mock Exams

You must solve as many mock exams as possible before sitting for the GCP professional Machine Learning certification exam. Many mock exams are available on Udemy, Whizlabs, and other online websites.

Once you start scoring 80% or above in these mock exams, you’re ready to write the GCP professional Machine Learning certification exam.

Finally, I hope you found this article helpful in preparing the certification and a great help to pass the GCP Professional Machine Learning certification exam.