[Report] 300 Data Science Leaders share what’s holding their Teams back

Holding data science back

The world’s most sophisticated companies overwhelmingly count on data science as a key driver for their long-term success. But according to a new survey of 300 data science executives at companies with more than $1 billion in annual revenue, flawed investments in people, processes, and tools are causing failure to scale data science.

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The significance of Data-centric AI

Andrew Ng

Andrew Ng is probably the reason most aspiring data scientists find it easy to break into this field. The ease with which he explains the most technical concepts is unparalleled. I look up to him for reasons more than one, but primarily my technical writing skills derive a major motivation from him i.e. to make it easy for everyone to understand the difficult jargon. He literally makes learning data science an art rather than a tedious gamut of the vast curriculum that often gets overwhelming.

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Coding ethics for AI & AIOps: Designing responsible AI systems

Designing responsible systems

Can you relate to this image ? This is a typical log file that support / dev teams have struggled – manually reading the logs line by line to resolve an outage/anomaly. Such was the era of traditional IT operations where : Process was time consuming, correlation between different layers of platform and multiple log files was difficult; Results could vary & valid for a particular time duration; Results could be lost and history wasn’t saved and Thus this approach did not scale.

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What I learned from “Women in Data Science” conferences

Women in AI

Covid has changed our lives in more ways than we could ever imagine. Working from home became a norm, something which was looked down upon when women used to seek this ‘LUXURY’ in pre-covid times. Whenever we talk about an all-inclusive world, why does women’s participation in “general conferences” not speak for itself?

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MLOPs and Machine Learning RoadMap

MLOps learning plan

Whenever I look to learn a new topic, I create some form of learning plan. There is so much content out there that it can be difficult to approach learning in the modern era. It’s almost comical. We have so much access to knowledge that many of us struggle to learn because we don’t know where to go. This is why I put together roadmaps and learning plans.

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Design patterns in machine learning

According to its definition, a design pattern is a reusable solution to a commonly occurring problem. In software engineering, the concept dates back to 1987 when Beck and Cunningham started to apply it to programming. By the 2000s, design patterns — especially the SOLID design principles for OOP — were considered common knowledge to programmers.

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10 Deadly Sins of Machine Learning Model Training

Location cups

10 Deadly Sins of Machine Learning Model Training. ML model training is the most time-consuming and resource-expensive part of the overall model-building journey. Training by definition is iterative, but somewhere during the iterations, mistakes seep into the mix. In this article, I share the ten deadly sins during ML model training — these are the most common as well as the easiest to overlook.

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A Machine Learning Model monitoring checklist: 7 things to track

ML model checklist

It is not easy to build a machine learning model. It is even harder to deploy a service in production. But even if you managed to stick all the pipelines together, things do not stop here. Once the model is in use, we immediately have to think about operating it smoothly. We need to make sure the model delivers. It means we need to monitor our models. And there are more things to look for!

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Multidimensional multi-sensor time-series data analysis framework

Multidimensional multi-sensor time-series data analysis framework. In this blog post, I will take you through my package “msda” useful for time-series sensor data analysis. A quick introduction about time-series data is also provided. The demo notebook can be found on here. One of the specific use case applications focused on “Unsupervised Feature Selection” using the package can be found in the blog post here.

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My machine learning model does not learn. What should I do?

If you work with data in general, and machine learning algorithms in particular, you might be familiar with that feeling of frustration when a model really does not want to learn the task at hand. You have tried it all, but the accuracy metric just won’t rise. What next? Where is the problem? Is this an unsolvable task or is there a solution somewhere you’re not aware of?

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