OKR-based approach enriches businesses with products built with clear-cut objectives accomplished by well-defined key results.

Agile methodologies and tools assist in accomplishing OKR based approach in building successful products benefitting both businesses and customers.

How we started our Journey to build an Alert Management System?

We started our journey using OKR based approach to build an Alert Management System. During the kick-off meeting, the product and engineering team along with our customers brainstormed on what defines an alert management system along with its capabilities.

For phase 1, we wanted to build an events API that captures failed events. Once the…

Product Development has come a long way from Waterfall to Agile. Every week or 2 weeks based on the sprint cycle new features get released.

What should one care about? Should it be the output or the outcome of the features that are released?

The answer would be the same whether you ask an Engineer, Management, or Product Team.

All features should adhere to what customer wants

For a successful product, the focus point should not be the number of features released per sprint cycle but rather the outcome or the changes each feature will attain towards a positive customer…

What is MLOps?

MLOps is the new terminology defining the operational work needed to push machine learning projects from research mode to production. While Software Engineering involves DevOps for operationalizing Software Applications, MLOps encompass the processes and tools to manage end-to-end Machine Learning lifecycle.

Software Development + DevOps => Software Application

Machine Learning + MLOps => Machine Learning Projects

Machine Learning Lifecycle

Machine Learning defines the models’ hypothesis learning relationships among independent(input) variables and predicting target(output) variables.

Machine Learning projects involve different roles and responsibilities starting from the Data Engineering team collecting, processing, and transforming data, Data Scientists experimenting with algorithms and datasets, and the MLOps…

Artificial Intelligence in Retail is disrupting the lifestyle of each and every customer. As online shopping was gaining momentum, the year 2020 brought in a drastic change to the retail industry with the onset of the Covid19 pandemic. The outbreak of coronavirus has resulted in the closure of many brick-and-mortar stores including popular brands not able to survive the economic effects of the pandemic.

While most stores were exploring whether to liquidate their stores or file for bankruptcy, there were some stores that managed to not only survive but fared relatively well. …

Facial Detection is the technology to detect human faces in digital media. This article will guide you to get started with Kaggle using the openCV (Open Source computer Vision) library in python.

What is Kaggle?

Kaggle is often referred as the AirBnB for Data Scientists.

If you are interested to venture into Machine Learning and want to learn by trying out some of the readily available algorithms and libraries, then Kaggle is the right place to start.

It is a set-up free Jupyter Notebook environment with numerous datasets and machine learning codes to playaround.

How is facial detection done using OpenCV?

Let’s go over the code performing facial detection using…

It only takes 5 simple R functions to understand data for better decision making.

Use Case 1: IT Salary Survey EU 2020

The data source for this use case is obtained from the below kaggle url


R Function #1

> Salary = data.frame(read.csv(“Salary.csv”))

> str(Salary)
‘data.frame’: 1253 obs. of 23 variables:

There are 1253 observations(rows) with 23 variables(columns)

> which.max(Salary$Age)
[1] 1104
> Salary$Age[1104]
[1] 69

The maximum age of IT professional is 69

R Function #2

> names(Salary)[names(Salary) == “Annual.brutto.salary..without.bonus.and.stocks..one.year.ago..Only.answer.if.staying.in.the.same.country”] <- “AnnualSalary”

> which.max(Salary$AnnualSalary)
[1] 854
> Salary$AnnualSalary[854]
[1] 500000000
> which.min(Salary$AnnualSalary)
[1] 713
> Salary$AnnualSalary[713]
[1] 11000
> plot(x=Salary$Age, y=Salary$AnnualSalary, xlab=”Age”, ylab=”Salary”, type=”l”)

Data is the universal truth guiding today’s world of Data Science. At the same time, it is not as simple as it looks to extract actionable insights. Turning data into meaningful insights require deep insights into the domain and the environment from where the data originates.

The year of 2020 turned into a painful year. Covid has changed our lives forever. These difficult times have taught us how to be resilient and empathetic. Hope is setting its foot with the arrival of Covid Vaccines. …

What is MLOps? Why do we need end to end MLOps solution for successful machine learning projects?

The primary responsibility of Data Scientists involves extracting value out of data by building and operationalizing Machine Learning Models. As businesses are embracing data science to improve business strategy, Data Scientists are struggling to manage the growing number of Machine Learning models.

Fintech, Healthcare, and Retail industries are earmarking their Machine Learning budget for 2020 to increase by 25% raising the scale and complexity of Machine Learning models.

With the growing complexities, Data Scientists are finding it laborious to manage the rising number of Machine Learning models in production. …

MLOps is a new discipline that originated from deploying machine learning models to production. Building machine learning models has found its place with the optimal tools, libraries, and frameworks, but, MLOps is still not mature enough to effectively manage models in production. What defines the scope for an end to end MLOps solution varies with the level of Machine Learning adoption.

MLOps Level 1 — Machine Learning models not yet in production

Any enterprise that wants to convert its data and domain expertise with machine learning models either hire a Data Scientist or look for a customizable solution for their specific use case. …

Pharmacovigilance, also referred to as PV or PhV refers to the drug safety process that involves collecting, analyzing, and communicating the adverse effect of drugs. Pharmacological science also involves medication errors such as overdosing or abusing drugs following worldwide laws and regulations related to drugs.

Adverse Event Reporting

The players involved in reporting adverse events of any drugs are the drug safety department in the pharmaceutical companies and the government-based drug regulatory authorities. …

Anu Ganesan

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