Inference

In a previous blog (The difference between statistics and data science), I discussed the significance of statistical inference. In this section, we expand on these ideas  

The goal of statistical inference is to make a statement about something that is not observed within a certain level of…

Introduction

Due to COVID19, we already have 1000 deaths in Italy.

Two months ago, these folks, many of them elderly, would have been celebrating Christmas and looking forward to the next Christmas – knowing that northern Italy has one of the best healthcare systems in the world

But apart from the human cost, the…

Introduction

Kubernetes is being described as the next ‘Java’ i.e. it is fast becoming an endemic/ underlying platform for the whole industry just like the Java programming language.

For the first time, we are seeing the entire ecosystem aligning around the single platform via the Cloud native foundation.

Kubernetes is the underlying technology behind a term called ‘Cloud Native’

The Cloud Native foundation defines the term ‘Cloud Native’ as: 

"Cloud…

I saw a post recently which claimed that AI can help doctors diagnose coronavirus

On the face of it - this sounds like a positive development but I wonder if it is?

As I understand it,  a single Chinese…

This post is based on two insightful threads I read online (References below)

Based on these, I address the question of ‘The difference between Statistics and Data Science’. Traditionally, most people, including me, would say that ‘statistics came first and Data Science builds upon statistics’. This chain of thought is valid but as you see…

Which masters or PhD course should I choose for Data Science or Artificial Intelligence?

This is one of the most common questions I am asked

Here is my view – and the usual caveats apply – i.e. the viewpoint is personal and is not associated with any company or institution I am…

In this post, we explore two terms which are becoming relatively common in professional machine learning applications – MLOps and DevOps

The term MLOps refers to a set of techniques and practises for data scientists to collaborate operations professionals.. MLOps aims to manage deployment of machine learning and deep learning models in large-scale production environments.

The term DevOps comes from the software…

I often use this quote from Isaac Newton in my teaching.

AI is a vast and a complex subject. No matter how much you know -  you realise that there is really a vast amount more to learn. So, my way of learning a subject as complex and dynamic as AI, is to share my insights. This helps me to refine my own thinking.

I also follow…

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…