In the previous post, ten strategies to implement ai on the cloud and edge, I discussed strategies for end to end deployment for machine learning modules.

How this relates to Agile?

Deployment of AI comes within the scope the normal SDLC (software development lifecycle)

So, normal Agile techniques like scrum, sprints, backlog…

Introduction

The deployment of Machine Learning and Deep Learning algorithms on Edge devices is a complex undertaking. In this post, I list the strategies for deploying AI to Edge devices end-to-end i.e. for the full pipeline covering machine learning (building modules) and deployment (devops)

I welcome your comments on additional ideas that could be included. In subsequent posts, I will elaborate these ideas in detail and…

Here is another resource I use for teaching my students at AI for Edge computing course. I like this resource because I like the cookbook style of learning to code. The resource is based on the book Machine…

You can always learn a lot from the papers presented at NeurIPS

There is some good analysis already on the web.

From Chip Huygen – neurips 2019 analysis and from David…

AI and the future of work is a major topic of discussion

Most views on this subject are negative

More broadly, the negative impact of technology has been highlighted by Nicholas Carr (the shallows) ,…

Academia and industry take different approaches to building machine learning and deep learning models

Here are seven differences

1) Approach to accuracy:…

How to know which AI/ ML algorithm to apply to which business problem?

This is a common question

I found a good reference for it –…

The Digital Shopfloor: Industrial Automation in the Industry 4.0 Era looks like a great free open access book by John Soldatos,  Oscar Lazaro and Franco Antonio Cavadini

The book deals with the transformation of the shop floor and the wider supply chain by the deployment of Industrial IoT

Some of…

Digital transformation is getting some traction now.

There are many definitions of digital transformation.

For example, according to salesforce  digital transformation is  -    Digital transformation is the process of using…

Artificial Intelligence – Cloud and Edge implementations takes an engineering-led approach for the deployment of AI to Edge devices within the framework of the cloud.

We often use the word ‘engineering’ in casual conversation. However, in this context, we attach a specific meaning to Engineering. Engineering is the use of scientific principles to design and build machines, structures, and other items, including bridges, tunnels, roads, vehicles, and…

This post is part of my forthcoming book The Mathematical Foundations of Data Science. Probability is one of the foundations of machine learning (along with linear algebra and optimization). In this post, we discuss the areas where probability theory could apply in machine learning applications. If you want to know more about…

Introduction

Co-relation does not equal causation – is a mantra drilled into a Data Scientist from an early age

That’s fine ..

But very few talk of the follow-on question ..

How exactly do you determine causation?

This problem is…

Overview of changes TensorFlow 1.0 vs TensorFlow 2.0

Earlier this year, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. The key differences are as follows:

Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users.…

The explanation of Logistic Regression as a Generalized Linear Model and use as a classifier is often confusing.

In this article, I try to explain this idea from first principles. This blog is part of my forthcoming book on the Mathematical foundations of Data Science. If you are interested in knowing more, please follow me on linkedin Ajit Jaokar

We take the following approach:

• We see first briefly how…

Introduction

Will china dominate AI?

When I spoke at the UK China business forum last month, I discussed this topic in response to an audience question.

In the current climate of nationalistic fervour, I see the same question asked in many guises.

For…

Can an ethical and algorithmically transparent cloud kitchen prevent future Amazon fires?

We often view AI with suspicion – but AI can be used to solve complex problems currently facing society where innovative approaches are needed

For many of us, the Amazon fires are disturbing and a…

The AI Edge Engineer: Extending the power of CI/CD to Edge devices using containers

Background

At the Artificial Intelligence – Cloud and Edge implementations course – I have been exploring the idea of extending CI/CD to Edge devices using containers. In this post, I present these ideas under the framework of the ‘AI Edge…

The use of training, validation and test datasets is common but not easily understood. 

In this post, I attempt to clarify this concept. The post is part of my forthcoming book on learning Artificial Intelligence, Machine Learning and Deep Learning based on high school maths.  If you want to know more about the book, please follow me on Linkedin Ajit Jaokar

Background

Jason…

Background

Containerize your Apps with Docker and Kubernetes is an excellent free book from Gabriel N. Schenker

You can download the whole book by registering…