Using AI to Write Blog Posts

Given that Hub Labs is looking at AI and we’re also writing blog posts, it seemed like a good opportunity to try out one of the AI blog post writing applications that keep appearing in my various streams. The one that seems to get the best recommendations and is also free to trial is Writesonic. I’ve been meaning to write a post about our initial steps to build the AWS cloud infrastructure for Labs, so I thought I would use this as the basis for the experiment. You essentially provide a title, in this case I chose ‘Getting Started with Machine Learning in the Cloud’, and it will then provide some intros and outlines that you can accept or reject before it goes on to produce some copy. The best copy I could get it to produce given the options provided is pasted in below. You can see it’s a bit marketing speak and it was no great surprise that it’s not proved to be of much use for my experiment at least. For some reason it wanted to assume we are using Google Cloud and didn’t offer any AWS copy. It was interesting to try it though, and I could see it might be useful for more general posts.

The AI generated copy (italised by me) including the image is pasted below:

Getting Started with Machine Learning in the Cloud

Image Source: Freepik‍

The practice of machine learning involves training computer algorithms to recognize patterns in data and make predictions based on those patterns. It takes a lot of resources to implement machine learning in your business. There are many different datasets, tools, and software packages that you need to understand and choose from before you canput them to work analyzing your data and building your algorithm. This blog post discusses how you can use cloud services for machine learning with an example using Google Cloud Machine Learning (Cloud ML) and its APIs for Natural Language Processing (NLP).

What is Machine Learning?

There are many definitions of machine learning, including the IEEE’s “A computer program is said to learn from experience E with respect to some class of tasks T and some performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” Or, as silly as it sounds, you can think about machine learning as a computer program that takes a bunch of data and spits out a prediction. You’re the one that has to tell the program how to do it.

Cloud ML with its APIs for NLP

Google Cloud Machine Learning workflows let you build and manage machine learning models in Google Cloud. Cloud ML supports a variety of machine learning frameworks, including TensorFlow, Caffe 2, and Apache MXNet. Cloud ML also provides a set of Natural Language APIs to support natural language processing (NLP) tasks, including sentiment analysis and entity extraction. Cloud ML’s NLP APIs can be accessed programmatically through its REST APIs.

Create a dataset using BigQuery

When you’re collecting data for machine learning, it’s important that your data be as accurate as possible. Google’s BigQuery lets you create a dataset in the cloud, and then you can use Google’s Natural Language API to analyze that dataset and get a breakdown of the different parts of speech. BigQuery is Google’s fully managed, enterprise data warehouse for business analytics. With BigQuery, you can query large data sets using standard SQL and get results in seconds. It’s also fully integrated with Google’s other analytical and collaboration tools, such as Google Sheets, Google Data Studio, and Google Cloud Platform.

Create a model using TensorFlow

With your dataset collected and a model for the Natural Language API built, you can create the model with TensorFlow. In this example, you’ll be building a model for sentiment analysis, which means you’ll be using the API’s sentiment analysis functionality. If you’re new to TensorFlow, don’t worry. There are some libraries out there that make it super easy to get started. One of them is tf-python-client, which is a library that allows you to write python code that uses TensorFlow.

Run the model in a Cloud Dataflow job

At this point, you’ve built a model and have everything ready to go. All that’s left to do is plug everything into Cloud Dataflow. Cloud Dataflow is Google’s fully managed, distributed framework for executing streaming data pipelines. Cloud Dataflow lets you build scalable, easy-to-manage data processing pipelines that transform and enrich your data. With Dataflow, you can build data processing and machine learning pipelines in a single, unified programming model. This means that you can build a model that performs data analysis, trains the model, and then deploys it in one workflow instead of three different ones.

Run the model in an API server

Your model is ready to go. Now you’re just missing the code to plug into an API server that can handle your data and send it to BigQuery. The Google Cloud Natural Language API lets you understand the meaning of text. You can use the API to detect entities, sentiments, and other linguistic properties of text. The API supports 82 languages.

Conclusion

Now that you know what machine learning is and have gotten started with Cloud ML and its NLP APIs, you can put this knowledge to use. You can use the NLP APIs to perform sentiment analysis or entity extraction on a dataset. This can be helpful for creating articles, social media posts, or marketing materials that are more effective.

With Cloud ML and its NLP APIs, you can create machine learning models that are more accurate and make more informed predictions. It takes a lot more effort to create models without cloud services, and it’s often difficult to keep track of all of the resources needed. With cloud services, you can focus on the model and not worry about the infrastructure and resources needed to run it.

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