Scaling out to a cloud platform for fast model training, evaluation, inferencing, logging, and monitoring

This is the concluding part of a four-part series. I suggest you read part 1, 2, and 3 for a better understanding:

Part 1

Part 2

Part 3

In this article, we will continue building production machine learning systems on GCP with a special focus on the design of hybrid ML systems which covers the following:

• Building hybrid cloud machine learning models
• Kubeflow for hybrid cloud
• Optimize Tensorflow graphs for mobile

This is a descriptive series at a high-level, there will be another series on implementing some of these standard concepts but if you would love to get fully hands-on before then, I suggest you take the Advance Machine Learning with Tensorflow on GCP course by google ML-Team for a start. …

Scaling out to a cloud platform for fast model training, evaluation, inferencing, logging, and monitoring.

This is the third part of a four-part series. I suggest you read part 1 and 2 for a better understanding:

Part 1

Part 2

In this article, we will continue the exploration of production machine learning systems on GCP with a special focus on the design of high-performance ML systems.

Content

• Efficiency at training
• Fast input pipelines
• Efficient inferencing

This is a descriptive series at a high-level, there will be another series on implementing some of these standard concepts but if you would love to get fully hands-on before then, I suggest you take the Advance Machine Learning with Tensorflow on GCP course by Google ML team for a start. …

Scaling out to a cloud platform for fast model training, evaluation, inferencing, logging, and monitoring.

This is the second part of a four-part series. I suggest you read part 1 for a better understanding:

link to part 1

In this article, we will continue on building production machine learning systems on GCP with a special focus on the following:

• Data ingestion for cloud-based analytics and ML (part 2)
• Adaptable ML system (part 2)

This is a descriptive series at a high-level, there will be another series on implementing some of these standard concepts but if you would love to get fully hands-on before then, I suggest you take the Advance Machine Learning with Tensorflow on GCP course by google ML-Team for a start. …

Scaling out to a cloud platform for fast model training, evaluation, inferencing, logging, and monitoring.

At the end of this series, you should be comfortable with ML systems designs (appropriate training and serving paradigm, and serve ML models scalably)

Content

• Architecting production ML systems (part 1)
• Static and dynamic training (part 1)
• Static and dynamic inference (part 1)
• Data ingestion for cloud-based analytics and ML (part 2 )
• Adaptable ML system (part 2)
• Design high-performance ML systems (part 3)
• Hybrid ML systems (part 4)

This is a descriptive series at a high-level, there will be another series on implementing some of these standard concepts but if you would like to get fully hands-on before then, I suggest you take the Advance Machine Learning with Tensorflow on GCP course by the Google ML team for a start. …

Danfo.js: Open-source Javascript library for data manipulation

This is the third and final part of a three-part series. I suggest you read parts 1 and 2 first for better understanding.

In the first part of the series, we got introduced to danfo.js, a new JavaScript package that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive.

The second part dealt extensively with data pre-processing for model building, training, and evaluation with TensorFlow.js and danfo.js in an Observable notebook.

What you will learn in part 3

• Converting an observable notebook to a script for the browser
• Train and perform inference in the browser

Converting observable notebook to script

In Pythonic data science end-to-end projects, notebooks are converted into scripts during deployment or package building. We can do exactly the same thing in JavaScript. …

Danfo.js: Open-Source JavaScript library for data manipulation

This is the second part of a three-part series. I suggest you read part 1 for better understanding:

In the first part of the series, we were introduced to danfo.js, a new JavaScript package that provides fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data easier and more intuitive.

In part 2, we’ll sail on to actually building our ML model and evaluating it with TensorFlow.js and danfo.js in an Observable notebook.

What you will learn

• Data preparation and format for modeling (part 2)
• Model building and evaluation with TensorFlow.js (part 2)
• Converting Observable notebook to a script for browser-side deployment (part…

Danfo.js: Open-Source JavaScript library for data manipulation

Machine learning (ML) is arguably the most sought after skill in the fields of data and computer science. ML related projects and problems can be done using any programming language. Developers have been led to believe that, to build and train an ML model, they are restricted to using a select few programming languages— Python, R and Java often top the list.

Python is currently the most commonly used programming language in data science, due to its flexibility and an abundance of out-the-box libraries to shorten development time.

The consensus among developers is that JavaScript is primarily a language for frontend development. In the context of machine learning, most assumed JavaScript only had applications in data visualization, but since the creation of Node.js and other powerful libraries, almost anything is possible in JavaScript—including machine learning modeling. …

A Tennis Chatbot built with Convolutional Neural Networks

This is the second part in a two-part series. I suggest you read the part 1 for better understanding.

In the first part of the series, we dealt extensively with text-preprocessing using NLTK and some manual processes; defining our model architecture; and training and evaluating a model, which we found good enough to be deployed based on the dataset we trained the model on.

Content

• Deployment Environment Setup
• Project File Structure
• Scripting
• Demo with Streamlit

Deployment Environment Setup

Our next step is to reproduce the essential processes in production so that are able to synchronize expected outputs on new text inputs. We’ll start by converting the Notebook into scripts and modules in a different project environment, with necessary versions of libraries and frameworks installed. …