In this guide, we learn how to develop a TensorFlow model and serve it on the Google Cloud Platform (GCP). We consider a regression problem of predicting the earnings of products using a three-layer neural network implemented with TensorFlow and Keras APIs.

The key learning outcomes of this guide are

1. Build, compile and fit Model in Tensorflow
2. Set up tensorboard in Google colab
3. Save, load and predict on unseen data
4. Deploy the TensorFlow model on Google Cloud platform

In this guide, we will use the TensorFlow 2.1.0 and Google colab runtime environment. We will be using the google colab for training the models using TensorFlow and Keras APIS. Starting from TensorFlow 2.0, Keras is now part of the TensorFlow package. Google colab offers the training of machine learning models on free GPU and TPU. Therefore, it is recommended the environment to get hands-on experience of training your deep learning models on GPU and TPU. Moreover, we can set up tensorboard to get a better understanding of the model training. This guide comes with a working copy of Google colab to get you started quickly.

Install TensorFlow >2.0

!pip install tensorflow==2.1.0

Import the necessary python libraries along with TensorFlow and Keras functions

Import libraries

import pandas as pd from sklearn.preprocessing import MinMaxScaler import os import numpy as np from future import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D from tensorflow.keras import Model from tensorflow.keras.models import Sequential, load_model

Load the tensorboard extension

Load tensorboard

%load_ext tensorboard

Mounting the Gdrive for data

To access the data in Google colab environment, there are various ways to do it. One of my favourite is to upload data to your Google Drive and mount…

Google Cloud Platform (GCP) is a comprehensive suite of cloud computing services offered by Google. It provides organizations with the tools and infrastructure to build, deploy, and scale applications and services in the cloud. In the context of AI/ML and data science, GCP offers a wide range of services that enable data scientists to leverage the power of artificial intelligence and machine learning.

GCP Services for AI/ML and Data Science

GCP offers several services that are specifically designed to support AI/ML and data science workflows. Let's explore some of the key services:

1. Google Cloud AI Platform:

Google Cloud AI Platform is a fully-managed service that allows data scientists and developers to build, train, and deploy Machine Learning models at scale. It provides a unified environment for developing and managing models using popular frameworks like TensorFlow, scikit-learn, and PyTorch. With AI Platform, data scientists can focus on their models while Google takes care of the underlying infrastructure.

• Key Features: AI Platform offers features such as AutoML, which automates the process of building custom Machine Learning models, and hyperparameter tuning for optimizing model performance. It also provides scalable and distributed training capabilities, model versioning and management, and online prediction for real-time inference.
• Use Cases: AI Platform can be used for a wide range of use cases, including image and video Classification, natural language processing, recommendation systems, fraud detection, and more. For example, Spotify uses AI Platform to power its music recommendation system, providing personalized recommendations to millions of users.
• References:
• Google Cloud AI Platform Documentation
• AI Platform Overview

2. Google Cloud Dataflow:

Google Cloud Dataflow is a fully-managed service for building and executing data processing pipelines. It provides a unified programming model for both batch and stream processing, enabling data scientists to perform complex data transformations at scale. Dataflow integrates seamlessly with other GCP services, allowing for easy integration with AI/ML workflows.

• Key Features: Dataflow offers a range of features, including automatic scaling, fault tolerance, and data windowing for stream processing. It supports popular programming languages like Java and Python and provides connectors to various data sources and sinks. Dataflow also integrates with BigQuery, GCP's data warehousing solution, for efficient data analysis.
• Use Cases: Dataflow can be used for a variety of use cases, including real-time analytics, ETL (Extract, Transform, Load) pipelines, data preparation for machine learning, and more. For instance, Spotify uses Dataflow to process and transform large volumes of streaming data to power its personalized music recommendations.
• References:
• Google Cloud Dataflow Documentation
• Dataflow Overview

3. Google BigQuery:

Google BigQuery is a serverless Data warehouse that enables organizations to analyze massive datasets with lightning-fast speed. It offers a scalable and fully-managed environment for storing and querying data, making it well-suited for data exploration, ad-hoc analysis, and machine learning tasks.

• Key Features: BigQuery provides a SQL-like interface for querying data and supports automatic scaling to handle large datasets. It offers built-in machine learning capabilities, such as the ability to create and execute machine learning models directly within BigQuery. BigQuery also integrates with other GCP services, enabling seamless data transfer and analysis.
• Use Cases: BigQuery can be used for a wide range of data analysis tasks, including data exploration, Business Intelligence, anomaly detection, and training machine learning models on massive datasets. For example, Home Depot uses BigQuery to analyze customer data and optimize inventory management.
• References:
• Google BigQuery Documentation
• BigQuery Overview

4. Google AutoML:

Google AutoML is a suite of services that brings the power of machine learning to users with limited expertise in data science. AutoML offers pre-trained models and automated machine learning capabilities, allowing organizations to build custom models without extensive knowledge of machine learning algorithms or programming.

• Key Features: AutoML provides a user-friendly interface for training and deploying machine learning models. It offers services like AutoML Vision, AutoML Natural Language, and AutoML Tables, which specialize in Computer Vision, natural language processing, and tabular data respectively. AutoML takes care of the model training, hyperparameter tuning, and deployment, making it accessible to a wider audience.
• Use Cases: AutoML can be used by organizations that lack in-house data science expertise but still want to leverage machine learning for their specific use cases. It can be applied to tasks like image Classification, sentiment analysis, document classification, and more. For instance, Urban Outfitters uses AutoML Vision to automatically tag and categorize product images.
• References:
• Google AutoML Documentation
• AutoML Overview

The Evolution of GCP

Google Cloud Platform has evolved over the years to become a leading player in the cloud computing industry. It was initially launched in 2008 as a suite of cloud-based productivity and collaboration tools known as Google Apps. Over time, Google expanded its offerings to include infrastructure services, storage solutions, and developer tools, which eventually formed the foundation of GCP.

GCP's focus on AI/ML and data science emerged as a response to the growing demand for scalable and efficient solutions to process and analyze large volumes of data. Google's expertise in machine learning, coupled with its vast infrastructure, positioned GCP as a leading platform for AI/ML workloads.

Relevance and Industry Standards

GCP has gained significant traction in the industry due to its powerful AI/ML capabilities, scalability, and ease of use. Its services have been adopted by organizations of all sizes across various industries, including healthcare, retail, Finance, and technology. GCP's relevance in the industry can be attributed to the following factors:

1. Scalability and Performance:

GCP provides a highly scalable infrastructure that allows organizations to handle massive datasets and process them in parallel. Services like BigQuery enable near-real-time analysis of large volumes of data, while AI Platform offers distributed training capabilities for training complex machine learning models at scale.

2. Integration and Interoperability:

GCP integrates seamlessly with other Google services like Google Cloud Storage, Google Cloud Pub/Sub, and Google Cloud Functions, allowing for easy data transfer and analysis. It also provides interoperability with popular open-source frameworks like TensorFlow and Apache Beam, making it easy to migrate existing AI/ML workflows to GCP.

3. Security and Compliance:

GCP adheres to industry-leading security and compliance standards, providing data encryption, access controls, and regular security audits. It is certified for various compliance frameworks, including HIPAA, PCI DSS, and ISO 27001, ensuring that sensitive data is protected and regulatory requirements are met.

4. Cost-Effectiveness:

GCP offers a flexible pricing model, allowing organizations to pay only for the resources they use. It provides options for both on-demand and preemptible instances, which can significantly reduce costs for batch processing workloads. GCP's pricing transparency and cost management tools help organizations optimize their AI/ML workflows.

Career Aspects and Best Practices

The growing adoption of GCP in the industry has created a demand for professionals with expertise in AI/ML and data science on the platform. Here are some career aspects and best practices for leveraging GCP in your career:

• Continuous Learning: Stay updated with the latest advancements in GCP's AI/ML services and best practices. Google provides extensive documentation, online courses, and certifications for GCP, which can enhance your knowledge and demonstrate your proficiency.
• Hands-on Experience: Gain hands-on experience by working on real-world projects that leverage GCP's AI/ML services. Building end-to-end solutions, from data ingestion to Model deployment, will give you a solid understanding of GCP's capabilities and help you showcase your skills to potential employers.
• Collaboration and Networking: Engage with the GCP community, participate in forums, and attend industry events to network with other professionals working on GCP. Collaborating with peers, sharing knowledge, and learning from others' experiences can accelerate your career growth.
• Best Practices: Follow best practices for Data management, model training, and deployment on GCP. This includes optimizing data pipelines for performance, using version control for models, monitoring and debugging pipelines, and ensuring data security and compliance.

Conclusion

Google Cloud Platform (GCP) offers a robust set of services for AI/ML and data science, empowering organizations to leverage the power of machine learning and process large volumes of data efficiently. With services like AI Platform, Dataflow, BigQuery, and AutoML, GCP provides a comprehensive ecosystem for building, training, and deploying AI/ML models at scale. Its scalability, performance, and integration capabilities make it a popular choice for organizations across industries. By gaining expertise in GCP's AI/ML services and following best practices, you can position yourself for a successful career in the field of AI/ML and data science.

References: - Google Cloud AI Platform Documentation - AI Platform Overview - Google Cloud Dataflow Documentation - Dataflow Overview - Google BigQuery Documentation - BigQuery Overview - Google AutoML Documentation - AutoML Overview