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This is a reblog of the original post at https://neuralmagic.com/blog/machine-learning-engineer-spotlight-mani-sarkar/ by Neural Magic
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In our new blog series, we’re interviewing data scientists and machine learning engineers about their career paths, areas of interest and thoughts on the future of AI. We kick off this week with a 20-year veteran and jack-of-all-trades when it comes to machine learning and data science: Mani Sarkar. Mani is a strategic machine learning engineer based in London, UK, who believes in getting beyond the theoretical and applying AI to real-world problems.

Below is our interview, lightly edited for clarity.

Tell us more about how you got into machine learning.

I started…

Introduction

I have been exploring and playing around with the Apache OpenNLP library after a bit of convincing. For those who are not aware of it, it’s an Apache project, supporters of F/OSS Java projects for the last two decades or so (see Wikipedia). I found their command-line interface pretty simple to use and it is a great learning tool for learning and trying to understand Natural Language Processing (NLP). Independent of this post, you can find another perspective on exploring NLP concepts using Apache OpenNLP, all of this directly from the realms of your command-prompt.

I can say almost everyone…

Introduction

After looking at a lot of Java/JVM based NLP libraries listed on Awesome AI/ML/DL I decided to pick the Apache OpenNLP library. One of the reasons comes from the fact another developer (who had a look at it previously) recommended it. Besides, it’s an Apache project, they have been great supporters of F/OSS Java projects for the last two decades or so (see Wikipedia). It also goes without saying that Apache OpenNLP is backed by the Apache 2.0 license.

In addition, this tweet from an NLP researcher added some more confidence to the matter:

In my current research project, I…

Introduction

We are all aware of Machine Learning tools and cloud services that work via the browser and give us an interface we can use to perform our day-to-day data analysis, model training, and evaluation, and other tasks to various degrees of efficiencies.

But what would you do if you want to do these tasks on or from your local machine or infrastructure available in your organisation? And, if these resources available do not meet the pre-requisites to do decent end-to-end Data Science or Machine Learning tasks. That’s when access to a cloud-provider agnostic deep learning management environments like Valohai can…

Introduction

This post is going to be a composition of the practical parts of two posts, one written late last year and the other a couple of months ago respectively. The posts being Apache Zeppelin: stairway to notes* haven! (late Dec 2018) and Running your JuPyTer notebooks on Oracle Cloud Infrastructure (early September 2019). Although this time we are going to make Apache Zeppelin run on the Oracle Cloud Infrastructure.

We will follow a similar structure like in the previous posts for ease of reading and understanding.

Also for brevity, we will use the term OCI when referring to Oracle Cloud…

Introduction

On the back of my previous share on how to build and run a docker container with Jupyter, I’ll be taking this further on how we can make this run on a cloud platform.

We’ll try to do this on Oracle Cloud Infrastructure (OCI). In theory, we should be able to do everything in the blog on any VM or Baremetal instance. If you are new to Oracle Cloud, I would suggest getting familiar with the docs and Getting Started sections of the docs. …

Introduction

Some time ago I came across this life-cycle management tool (or cloud service) called Valohai and I was quite impressed by its user-interface and simplicity of design and layout. I had a good chat about the service at that time with one of the members of Valohai and was given a demo. Previous to that I had written a simple pipeline using GNU Parallel, JavaScript, Python and Bash — and another one purely using GNU Parallel, and Bash. …

The GraalVM compiler is a replacement to HotSpot’s server-side JIT compiler widely known as the C2 compiler. It is written in Java with the goal of better performance (among other goals) as compared to the C2 compiler. New changes starting with Java 9 mean that we can now plug in our own hand-written C2 compiler into the JVM, thanks to JVMCI. The researchers and engineers at Oracle Labs have created a variant of JDK8 with JVMCI enabled which can be used to build the GraalVM compiler. The GraalVM compiler is open source and is available on GitHub (along with the…