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Although phrases like “data is an asset” and “data is the new oil” are arguably corporate clichés, they are accurate. As businesses continue to digitize and derive real value from data, the importance of it to them grows; it becomes part of their DNA.

The performance of a machine learning model, particularly deep learning models, depends on both the quality and quantity of available training data.

AI is no longer the future—it’s here. In our pockets, in our houses, in our cars.

Data is the new oil, as the famous saying goes. It is no wonder then that so many organizations are manufacturing their own digital fuel in the form of synthetic data, which is both cheap to produce and very effective at training machine learning (ML) models.

In 2015, Google released its famous machine learning (ML) paper, Hidden Technical Debt in Machine Learning Systems.

With so many great podcasts out there though, there’s no reason why you can’t sub out your regular playlist for something a little more educational and insightful while you hit the gym or drive to work.

If you have been paying attention to the latest developments in machine learning (ML) and artificial intelligence (AI) over the last few years, you will already be familiar with Natural Language Processing (NLP), largely in part due to the development of burgeoning transformer models—and we’re not talking about the popular shape-shifting cartoon robots.

If you have been keeping up with your reading, you have probably the term ‘MLOps’ and other variants such as ‘ModelOps’ and ‘AIOps’ begin to pop up more frequently.

In a few hundred years, when people look back on this point in history, the emergence of AI and machine learning (ML) will be viewed as a significant milestone that changed the world.

Machine learning (ML) models are almost always developed in an offline setting, but they must be deployed into a production environment in order to learn from live data and deliver value. 

Big data is accelerating at such a rapid pace that it’s leading to massive amounts of innovation in emerging tech, particularly in applications that involve AI and machine learning (ML).

On this blog post we will cover how can simplify model training and deployment by using Qwak.

The article describes the stages involved in productionizing machine learning models, typical architectural patterns used, and the challenges involved in doing it.

A machine learning model is only as good as the data that it is trained with. Or to be more precise, it can only be as good as the features that have been built into it. We all know that. 

Deploying a machine learning model is way different than deployments of any other kind of software.Because machine learning models learn general rules from specific examples, we are never sure how they will behave in production. Of course, proper training set preparation diminishes the risk. Nevertheless, we can always be surprised by the results.To mitigate the problems, typically, we rollout machine learning models in three deployment stages

Building a machine learning model is hard. Deploying it is even harder. Running experiments, writing code for production, integrating with infrastructure, running ongoing tests, and avoiding training-serving skew – none of this is easy.

If you have decided to implement machine learning into your app or product and are exploring your options, you have probably come across terms like ‘online machine learning’ and ‘batch learning’ – but what exactly do these terms mean and why do they matter?

Automation exists in machine learning, but this doesn’t mean that machine learning engineers will be automated. Here's why they needn't worry...

“Big data” has been around for more than a decade, and much has been done to adapt the tech stack; however, getting value from big data is complicated.

In this article, we will show you how to implement a build system that retrieves the training data, trains the model, and creates a Docker container with a REST service generating the model predictions. Our model will perform a well-known task of predicting whether a Titanic passenger survives or dies.

This blog post covers the main differences between DevOps and MLOps, as well as the main challenges that MLOps build systems should solve.

Qwak now supports deploying machine learning (ML) models with event-driven streaming architecture using Apache Kafka to support high-throughput predictions.

Qwak now supports machine learning models feedback tracking.

Qwak Feature Store now supports Snowflake as data source

Qwak Feature Store now supports MongoDB as data source