Are you jealous of Go developers building an executable and easily shipping it to users? Wouldn’t it be great if your users could run your application without installing anything? That is the dream, and PyInstaller is one way to get there in the Python ecosystem. There are countless tutorials on how to set up virtual environments, manage dependencies, and publish to PyPI, which is useful when you’re creating Python libraries. There is much less information for developers building Python applications. This tutorial is for developers who want to distribute applications to users who may or may not be Python developers.
GraphQL has become a buzzword over the last few years after Facebook made it open-source. I have tried GraphQL with the Node.js, and I agree with all the buzz about the advantages and simplicity of GraphQL.
Productionizing machine learning/AI/data science is a challenge. Not only are the outputs of machine-learning algorithms often compiled artifacts that need to be incorporated into existing production services, the languages and techniques used to develop these models are usually very different than those used in building the actual service. In this post, I want to explore how the degrees of freedom in versioning machine learning systems poses a unique challenge. I’ll identify four key axes on which machine learning systems have a notion of version, along with some brief recommendations for how to simplify this a bit.
Machine learning engineers are in high demand as more companies adopt artificial intelligence technologies. With demand outpacing supply, the average yearly salary for a machine learning engineer is a healthy $125,000 to $175,000. And the highest-paying companies are offering more than $200,000 to secure top talent. Intrigued? Read on to learn how to become a machine learning engineer.
This post covers my current thinking on what I consider the optimal way to work with R on the Google Cloud Platform (GCP). It seems this has developed into my niche, and I get questions about it so would like to be able to point to a URL. Both R and the GCP rapidly evolve, so this will have to be updated I guess at some point in the future, but even as things stand now you can do some wonderful things with R, and can multiply those out to potentially billions of users with GCP. The only limit is ambition.
Over the years, I’ve seen analytics professionals of all stripes blow their credibility and lessen their impact by falling into a common trap. I have to admit that I fell victim to the same trap early in my career. While our intentions are pure, our analytical minds and approaches can get the best of us and we explain too much. We’ll be better off if we learn to provide less detail and stop talking sooner than we are naturally inclined to.
Machine-learning techniques used by thousands of scientists to analyse data are producing results that are misleading and often completely wrong.
We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages scene graph structures to create 22M diverse reasoning questions, all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate language biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. An extensive analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3\%, offering ample opportunity for new research to explore. We strongly hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding for images and language.
There’s no doubt that Artificial Intelligence is changing the workforce. Four in five business leaders in Asia Pacific believe the burgeoning technology will transform the way their firms operate within the next three years, according to a new report from Microsoft and the International Data Corporation. And yet, there’s disconnect among the workforce. As many as 15 percent of employees believe AI will have no impact on their jobs, the research found. Meanwhile, more than three-quarters (77 percent) expect their employer to help them develop skills to adapt to the changing environment.