Expert Insider's Guide to Becoming a Google Cloud Machine Learning Engineer

Expert Insider's Guide to Becoming a Google Cloud Machine Learning Engineer

I'm sharing my experience when India was far behind the other world regarding PC programming equipment innovation. It required a very long time to get or purchase the equipment. Today, Cloud Computing Companies can be gotten to with a solitary snap to bring our work exceptionally quick.

The world's innovation is evolving quickly, and the present time works with the ABC Triangle, where A represents AI, B for BigData, and C for CloudComputing. More information = more Artificial intelligence/ML applications; more applications = more Computing computing power; and more applications = more Data.

There are a ton of organizations in the market that leads development drove outlooks and industry-pattern drove items; however, Google is the top forerunner in this ABC Tringle advancements. As an ML Trailblazer, Google Created AlphaGo in 2017, the primary PC program that crushed an expert human go title holder. Through this advancement and history of development, Google has fostered a ton of uses in numerous areas, for example, PC vision, Voice, Language Handling, and GCP, which is a Google Cloud Computing technology that has changed the market to make it conceivable to determine the issues of BigData and ML handling.

Imagine a scenario where you have an expert manual for setting up this sort of organization's ML Certification of Google Cloud Machine Learning Engineer.

Book: "Becoming a Google Cloud Machine Learning Engineer" by Dr. Logan Song

Thanks: Shifa Ansari , for Providing the review copy of the book

?? Why do you need to read this book?

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?? This is the top ML-based Cloud Certification Program that assists you with using the progression in industry practices.

?? This is an awesome guide in simple language to examine the Google Cloud AI Certification learning path.

?? Assists you with planning top to bottom with test questions so you can set up your mindset and break the test.

Google Cloud ML Best Practices:

Image Credit: Google Cloud

???? Key Ideas:????

?? Provide a complete introduction to the various Google Cloud Platform services, hands-on examples, and the essential Python programming tutorial that forms the backbone of data science.

?? Demonstrate the best industry practices to frame the business problem into a data science problem by asking such question list:

? What are the business requirements?

? Is ML the best way to solve the problem?

? What are the inputs and outputs for the problem?

? Where is my data?

? How do I measure the success of the ML solution?

? Is the data ready?

? How do I collect my data?

? How do I transform and construct my data?

? How do I select features for the ML model?

And also provide the best practice to answer all the questions.

?? Design the ML development process as follow:?

?????? Splitting the dataset ?? Building the Platform ?? Training the Model ?? Validating the Model ?? Tuning the Model ?? Testing and deployment of the model?

?? It will give you an initial idea about Deep core learning, and its evolution helps to computer vision and NLP:

• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Long Short-Term Memory (LSTM) networks
• Generative Adversarial Networks (GANs)

?? Mastering ML Practise with GCP Provide the Following :

? Offering the learning guide of GCP Big Query, Tensorflow, and Keras.

? Exploration of Google Cloud Vertex AI:

Managing ML services is easier with Google Vertex AI, an interface and suite of products from Google Cloud. It provides customers an end-to-end solution for creating, training, and deploying ML applications on Google Cloud. Data scientists now have a one-stop shop in Vertex AI from which to create ML applications:

1. Vertex AI data labeling and datasets: ?It is a Google Cloud service that lets end users work with human workers to review and label datasets uploaded by users.
2. Vertex AI Feature Store: Google Vertex AI Feature Store is a fully managed cloud service that provides a centralized repository to store, organize, and serve ML features.
3. Vertex AI Workbench and notebooks: The Vertex AI Workbench service provides a single development platform for the entire data science workflow; you can launch Cloud VM instances/notebooks to query and explore data and develop and train a deployment model.
4. Vertex AI training: In Vertex AI, you can choose different training methods based on the source of model and data; Vertex AI AutoML, which Google manages, uses Google's model and your data to train, and the Vertex AI platform, with user-defined code or custom containers, utilizes your model and your data to perform model training.

The Vertex AI platform

The Vertex AI platform, with custom containers, enables you to build your models from scratch with your data. Custom containers are user-created Docker images that are selected while creating a pipeline.

5. Vertex AI models and predictions: It provides a platform for managing ML Models to develop and manage ML models in many ways: Create Model, Upload Model, Deploy Model, Export Model

6. Vertex AI Pipelines: Vertex AI Pipelines automate the orchestration of your machine learning process using TensorFlow Extended (TFX) or Kubeflow. A configuration file containing a series of steps is used to build each Vertex AI pipeline job. In a typical Vertex AI pipeline, data is imported into a dataset, a model is trained via a training pipeline, and the model is deployed to a new endpoint for prediction. Pipeline tasks are executed on computational resources.

7. Vertex AI metadata: Vertex AI Metadata is a store of metadata created by various Vertex AI components. Metadata is created and saved when models are built in ML workflow pipelines. You may combine this metadata into a single metadata store to make it easier for users to search for information and find answers.

8. Vertex AI experiments and TensorBoard: A Google open-source project for visualizing machine learning experiments is called TensorBoard. TensorBoard is implemented in vertex AI experiments. Users may perform experiments using Vertex AI to see visual representations of various metrics, such as loss function and accuracy across various model parameters at various running durations. In addition, users can build TensorBoard instances and upload TensorBoard logs produced by Vertex AI Models.

?? Offering to understand the GCP ML APIs such as:

• Google Cloud sight APIs, including the Cloud Vision API and Cloud Video API
• Google Cloud language APIs, including Natural Language Processing (NLP) and Translation
• Google Cloud conversation APIs, including Speech-to-Text, Text-to-Speech, and Dialogflow

?? Describe the best practices for using Google Cloud to apply machine learning on below things:

ML environment setup

ML data storage and processing

ML model training

ML model deployment

ML workflow orchestration

ML model continuous monitoring

?? Guide you on How you can accomplish the google cloud ML certification:

Read the official Google ML certification exam Guide.

Read all the service guides carefully to utilize the maximum benefit of learning and practices.

Need to focus on Hands-on practice more to get familiarity.

Guide you how to interpret the exam question and give best suitable answer as per your knowledge.

This book is a must-read for anybody aspiring to become a Google Cloud ML Engineer, and I urge everyone to pick it up.

Thanks for Reading