Snorkel AI’s app development platform lures $35M

Snorkel AI

Snorkel AI, a startup developing data labeling tools aimed at enterprises , today announced that it raised $35 million in a series B round led by Lightspeed Venture Partners. The funding marks the launch of the company’s Application Studio, a visual builder with templated solutions for common AI use cases based on best practices from academic institutions.

Read More

Delivering AI/ML without proper Dataops is just wishful thinking!

DataOps Team Process

Given the iterative nature of AI/ML projects, having an agile process of building fast and reliable data pipelines (referred to as DataOps) has been the key differentiator in the ML projects that succeeded (unless there was a very exhaustive feature store available which is typically never the case).

Read More

Do companies need a Chief AI-Ethics Officer?

Ethical workflow

The world we live in is becoming more and more data-driven. This is causing companies to make more and more use of AI techniques such as machine learning and deep learning. The task of the Chief AI Ethics Officer (CAIEO) should not be primarily technical. Instead, it should sensitize data scientists, machine learning engineers, and developers to ethical issues. The whole process should be firmly integrated into the respective process models and phases.

Read More

Positives & Negatives of Artificial Intelligence (AI)

Thumbs up/ Thumbs down

Artificial Intelligence – a blessing or a curse. It has always been the dream of humans to build machines having the ability to think like them. For a long time, humans had been watching this far-fetched dream coming true just in science fiction movies until the self-driving cars and Siri-like applications were introduced. From the smartphones in our pockets to the robots cleaning our floors, we have incorporated Artificial Intelligence into our lives.

Read More

AI World Executive Summit: Important to ask the right questions 

AI world executive summit

Of respondents at AI high-performing companies, 75% report that AI spending across business functions has increased because of the pandemic, according to the Global Survey on AI from McKinsey for 2020. These organizations are using AI to generate value, which is increasingly coming in the form of new revenue. Three experts discussed the implications of this growth with AI Trends in interviews in anticipation of the AI World Executive Summit: The Future of AI, to be held virtually on July 14, 2021.

Read More

The fate of Feature Engineering: No longer necessary, or much easier?

Feature engineering

Feature engineering occupies a unique place in the realm of data science. For most supervised and unsupervised learning deployments (which comprise the majority of enterprise cognitive computing efforts), this process of determining which characteristics in training data are influential for achieving predictive modeling accuracy is the gatekeeper for unlocking the wonders of statistical Artificial Intelligence.

Read More

Why Causal Machine Learning is the next revolution in AI

Deep correlation

Causal modeling and inference are perhaps at the core of the most interesting questions in data science. A common task for a data scientist at a FAANG is to query users who had exposure to a feature and calculate the correlation between usage of that feature and engagement on the platform.

Read More

Facebook AI learned Object Recognition from 1 billion Instagram pics

Mobile device with pictures

Let’s take a quick look at the number one concern people may have with this model, i.e., Privacy. People have started taking digital privacy pretty seriously, and for them, this may come as a shock that their photos are being used to train an AI model. Combined, Facebook, WhatsApp, Instagram, and Messenger are sitting…

Read More

Use of Synthetic Data, in early stage, seen as an answer to Data Bias 

Assuring that the huge volumes of data on which many AI applications rely is not biased and complies with restrictive data privacy regulations is a challenge that a new industry is positioning to address: synthetic data production. Synthetic data is computer-generated data that can be used as a substitute for data from the real world.

Read More

Edge AI: Framework for Rapid Prototyping and Deployment

Edge framework

According to a report by Market and Markets (Markets & Markets, 2020), “the global Edge AI software market size is expected to grow to USD 1835 million by 2026”. Similarly, a report by 360 Research Reports (360 Research, 2019) estimates that “the global Edge AI Software market size will reach US$ 1087.7 million by 2024”.

Read More

What is MLOps - Everything you must know to get Started

MLOps graphic

Until recently, all of us were learning about software development lifecycle(SDLC). Now, we are at a stage where almost every other organisation is trying to incorporate AI/ML into their product. This new requirement of building ML systems adds/reforms some principles of the SDLC to give rise to a new engineering discipline called MLOps.

Read More

Things I learned as an ML Product Manager — Part 1

ML Product Management

Ok, this is not an ML product specific advice. It’s a classic Product Management mantra. Define the customer problem first — “what”, “why” and “who” before diving into the “how”. But ML product managers are especially prone to the fallacy of trying to package a readily available technical solution as a product, whether or not the solution maps to a real pressing problem for the end user.

Read More

Top 6 CI/ CD practices for End-to-End development pipelines

Continuous deployment

In this article, we’ll talk about some often-misunderstood development principles that will guide you to developing more resilient, production-ready development pipelines using CI/CD tools. Then, we’ll make it concrete with a tutorial about how to set up your own pipeline using Buddy.

Read More
1 2 3 25