Mars and Venus are very different planets. Mars’s atmosphere is very thin and it can get very cold; while Venus’s atmosphere is very thick and it can get very hot — hot enough to melt lead!.
…Software Engineers and Data Scientists come from two different worlds — one from Venus and the other from Mars. They have different backgrounds mindsets, and deal with different sets of issues. They have a number of things in common too. In this and subsequent blogs we will look at the key differences (and similarities) between them and why those differences exist and what kind of bridge we need to create between them. In this blog, we explore the fundamental differences between software and models. Read More
Tag Archives: DevOps
Responsible AI Can Effectively Deploy Human-Centered Machine Learning Models
Artificial intelligence (AI) is developing quickly as an unbelievably amazing innovation with apparently limitless application. It has shown its capacity to automate routine tasks, for example, our everyday drive, while likewise augmenting human capacity with new insight. Consolidating human imagination and creativity with the adaptability of machine learning is propelling our insight base and comprehension at a remarkable pace.
However, with extraordinary power comes great responsibility. In particular, AI raises worries on numerous fronts because of its possibly disruptive effect. These apprehensions incorporate workforce uprooting, loss of protection, potential biases in decision-making and lack of control over automated systems and robots. While these issues are noteworthy, they are likewise addressable with the correct planning, oversight, and governance. Read More
Low-code platforms and the democratization of AI
Tech giants like IBM and Amazon are developing products that will make it easier for people without a coding background to build apps that integrate with AI services.
Key Takeaway: Low-code and no-code platforms are seeing new life, as access to artificial intelligence and fast deployment of applications becomes increasingly critical with the popularity of Cloud-based software development. These products enable those without a coding background to more easily access the benefits of AI. Read More
Low-Code Can Lower the Barrier to Entry for AI
Organizations that want to get started quickly with machine learning may be interested in investigating emerging low-code options for AI. While low-code techniques will never completely replace hand-coded systems, they can help accelerate smaller, less experienced data science teams, as well as help with prototyping for professional data scientists.
First of all, what is low-code? Well, the phrase can mean different things to different people, and its applicability to AI is not entirely nailed down. Mainstream developers have been using low-code (or no-code) approaches to creating business and consumer applications for years, and that largely forms the basis for low-code approaches in AI. Read More
A Tour of End-to-End Machine Learning Platforms
Machine Learning (ML) is known as the high-interest credit card of technical debt. It is relatively easy to get started with a model that is good enough for a particular business problem, but to make that model work in a production environment that scales and can deal with messy, changing data semantics and relationships, and evolving schemas in an automated and reliable fashion, that is another matter altogether. If you’re interested in learning more about a few well-known ML platforms, you’ve come to the right place! Read More
Why Best-of-Breed is a Better Choice than All-in-One Platforms for Data Science
All-in-one platforms built from open source software make it easy to perform certain workflows, but make it hard to explore and grow beyond those boundaries.
So you need to redesign your company’s data infrastructure.
Do you buy a solution from a big integration company like IBM, Cloudera, or Amazon? Do you engage many small startups, each focused on one part of the problem? A little of both? We see trends shifting towards focused best-of-breed platforms. That is, products that are laser-focused on one aspect of the data science and machine learning workflows, in contrast to all-in-one platforms that attempt to solve the entire space of data workflows.
This article, which examines this shift in more depth, is an opinionated result of countless conversations with data scientists about their needs in modern data science workflows. Read More
Jira is a microcosm of what’s broken in software development
This blog is not about how Jira is too complex and over-engineered with features I don’t need.
Those complaints are well articulated by others.
… Over the years I have arrived at the conclusion that Jira contradicts my values as a member of a dev team. It represents a way of thinking and working that goes against my beliefs. Read More
Architectures Every Data Scientist And Big Data Engineer Should Know
Comprehensive and Comparative List of Feature Store Architectures for Data Scientists and Big Data Professionals.
Feature store has become an important unit of organizations developing predictive services across any industry domain.
… This blog post highlights the features supported by different Feature Store frameworks, that are primarily developed by different leading industry giants. Read More
How to get your data scientists and data engineers rowing in the same direction
In the slow process of developing machine learning models, data scientists and data engineers need to work together, yet they often work at cross purposes. As ludicrous as it sounds, I’ve seen models take months to get to production because the data scientists were waiting for data engineers to build production systems to suit the model, while the data engineers were waiting for the data scientists to build a model that worked with the production systems.
A previous article by VentureBeat reported that 87% of machine learning projects don’t make it into production, and a combination of data concerns and lack of collaboration were primary factors. On the collaboration side, the tension between data engineers and data scientists — and how they work together — can lead to unnecessary frustration and delays. While team alignment and empathy building can alleviate these tensions, adopting some developing MLOps technologies can help mitigate issues at the root cause. Read More
Introducing the Model Card Toolkit for Easier Model Transparency Reporting
Machine learning (ML) model transparency is important across a wide variety of domains that impact peoples’ lives, from healthcare to personal finance to employment. The information needed by downstream users will vary, as will the details that developers need in order to decide whether or not a model is appropriate for their use case. This desire for transparency led us to develop a new tool for model transparency, Model Cards, which provide a structured framework for reporting on ML model provenance, usage, and ethics-informed evaluation and give a detailed overview of a model’s suggested uses and limitations that can benefit developers, regulators, and downstream users alike.
Over the past year, we’ve launched Model Cards publicly and worked to create Model Cards for open-source models released by teams across Google. Read More
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