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OpenAI has introduced GPT-4, a large multimodal model designed for accepting image and text inputs and emitting text outputs. The latest model demonstrates human-level performance on various professional and academic benchmarks, such as passing a simulated bar exam with a score in the top 10% of test takers. The company spent six months iteratively aligning GPT-4, resulting in improved results on factuality, steerability, and not going outside of guardrails.

We reached out to one of our customer’s data scientists to talk about their experience working with data, as well as their insights into MLOps and the challenges involved in deploying machine learning models in production.

MLOps 2.0, is focused on creating an infrastructure that can support the deployment of machine learning models at scale. This infrastructure should include a feature store, model repository, model serving, model monitoring, and an overall orchestrator to manage everything centrally.

With the rapidly growing demand for machine learning models, there is an increasing need for a more efficient and streamlined approach to developing, deploying, and maintaining these models.

Today's businesses are rushing to implement Artificial Intelligence (AI) and Machine Learning (ML) systems to boost their profitability and participate in what most call the fourth industrial revolution.

Feature stores are rapidly growing popular as organizations look to improve their machine learning productivity and operations (MLOps). With the advancements in MLOps, feature stores are becoming an essential component of the machine learning infrastructure, helping organizations to improve the performance and explainability of their models, and accelerate the productionization of new models.

The artificial intelligence (AI) industry has been working for years on improving natural language processing and understanding. The latest intelligent technology in this regard is the Large Language Model (LLM).

Deploying an ML model in production is a complicated process. ML serving tools help serve models in a scalable way with optimized serving architecture.

Developing a machine learning model is an iterative and never-ending process. Data scientists start with a base model and experiment with numerous architectures and data set versions to arrive at a model that they think will perform in production.

The model development lifecycle does not stop after deploying the first model. Model monitoring involves watching the model results to find issues.

PR Newswire reports 79% of global executives and 75% of business users to face data quality issues based on a survey of 1000+ executives and managers. Further, 69% of executives believe that data regulations hinder decision-making and call for better data management tools.

This blog compares PyTorch vs Tensorflow and walks through the origin and pros & cons of them and will help you decide which is best for your needs.

Choosing the right data labeling tool is essential for your machine learning projects. Here, we compare some of the top options available to help you make the best decision for your team.

Tree-based algorithms are highly efficient for regression and classification tasks. Let’s talk about some of these algorithms and compare XGBoost vs Random Forest to see what benefits each offers.

We are extremely excited to announce Qwak’s new ML model management and feature store enhancements. These new capabilities allow Qwak’s users to optimize ML model performance and deliver top grade machine learning production workflows.

Each machine learning model passes through different phases throughout its development. Every Data scientist, ML engineer, or AI enthusiast knows how much work it takes to develop a successful machine learning system. From gaining raw data, processing that data, validation, analysis, training, tuning, architecture, and the final phase, deployment.

Kubeflow, created by Google in 2018, and MLflow, an open-source platform for managing the end-to-end machine learning lifecycle are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production.

Kubeflow, created by Google in 2018, and Amazon SageMaker, a cloud machine learning platform, are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production.

Kubeflow, created by Google in 2018, and Metaflow, created by Netflix in 2019, are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production. 

When working with machine learning (ML) in production, the model choice is just one of a slew of important criteria. Equally important are the definition of the problem, gathering high-quality data, and the architecture of the machine learning pipeline.

Reproducibility — the ability to replicate an experiment and obtain the same results by using the same methodology — is critical in all fields of science. It’s also important in artificial intelligence (AI) and machine learning (ML) applications.

Decisioning based on machine learning has become the foundational aspect of running businesses in the modern era.

Kubeflow, created by Google in 2018, and Databricks, launched in 2013 by the people behind Apache Spark, are powerful machine learning operations (MLOps) platforms that can be used for experimentation, development, and production.

Decisions based on artificial intelligence and machine learning are an integral part of running a business in the data era.

Are you getting different results from your machine learning (ML) algorithms? Perhaps your results aren’t what you expected, or perhaps the model is making different predictions each time that it’s trained, even when it’s being trained on the same data set each time.

There has been an explosion in new technologies and tools for managing tasks and data pipelines in recent years. There are now so many of them, in fact, that it can be challenging to decide which ones to use and understand how they interact with one another, especially because selecting the right tool for your use case involves many factors that all need to be given due consideration. 

Organizations are rapidly investing in MLOps to enhance their productivity and create cutting-edge machine learning (ML) models. MLOps helps to streamline the ML lifecycle by automating repeatable tasks and providing best practices to help ML teams collaborate more effectively.

Machine models need to be frequently deployed to support changes such as data drift, data decay and new target audiences. A deployment strategy is a way to update an existing ML model deployed in production and support the continuous testing and management of multiple models simultaneously.

It seems like everybody is breaking into machine learning (ML) nowadays. The proliferation of it alongside ‘big data’ has naturally led to a scramble among organizations who want to figure out how to use all the data that they’re collecting in a way that delivers value to their bottom lines.

As organizations embrace the potential of data science and machine learning (ML), they face the huge challenge of operationalizing these processes in their technical environments and picking the right investments at the right times according to their strategic objectives.

All the buzz and hype surrounding ‘big data’ has created a major misconception: that the existence of data alone can provide a company with endless actionable insights and help to power decision making.

If you’re a data professional, you’ve probably already heard about Git, and chances are that somebody has told you that it’s only for software developers, and as a data professional, it’s not something you should care about.

Artificial Intelligence is rapidly evolving as it is used in a growing number of applications across business and society. The majority of AI research is, of course, dominated by computer science and the focus on i) developing more innovative algorithms and ii) the design of processors and storage needed for different applications. 

By leveraging the capabilities of AI and ML, tech innovators and research institutions are making new materials, discovering new drugs, detecting fraud, protecting crops, and more. In our daily lives, we’re also regularly facing AI algorithms—from email filters and personalized music suggestions, there are very few areas in our lives today that hasn’t been touched by AI and ML, and this has all been made possible by data. 

Machine learning (ML) teams are great at creating models that represent and predict real-world data, but effectively deploying these models is something that’s more akin to an art form than a science.

Unstructured data can contain a huge amount of useful information. However, due to the complexities that can exist in processing and analyzing it, it’s often the case that practitioners avoid spending extra time and effort venturing outside of their comfort zones—i.e., outside of structured datasets—to analyze these unstructured goldmines. 

In November 2018, Ville Tuulos, a machine learning infrastructure architect, was the very first person to publicly dissect and discuss the Netflix machine learning infrastructure at the annual ‘QCon’ software development conference held in San Francisco.

Just like human beings require a specific environment to survive and thrive, so too does software.

Albert Einstein once famously said, “If you can’t explain it simply, you don’t understand it well enough.”

Developing successful machine learning projects requires a huge variety of expertise. It’s no longer the case that teams are made up of multitalented people with a do-it-all remit.

Two things form the foundations of an AI system: code and data. Both components play an important role in the development of robust architectures and systems.

Today’s ML teams are highly specialized and multi-disciplined. They consist of people from all walks of life, educational backgrounds, and different skillsets.

Dependency management is the act of managing all the external bits and pieces that your project needs. When it works, you don’t even know it’s there. On the flip side, you will know if it fails.

It is becoming increasingly common for DevOps methodologies to find their way into MLOps as the field continues to mature.

Artificial intelligence (AI) is advancing at a rapid pace. It is something that we have dreamed of as far back as the 1950s and possibly even earlier, but today, AI is embedded into our everyday lives.

In 2022, we are well past the point of proving the value of machine learning; it is something that has taken the IT space by storm as a truly transformative innovation.

In our previous reinforcement learning blog post, we explored why reinforcement learning is an exciting field in AI and machine learning.

Have you ever trained a pet to do something and rewarded it when it gets it right? This is known as reinforcement learning...

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