Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. Over the years, LinkedIn has built one of the most sophisticated machine learning infrastructures powering thousands of ML pipelines.

Despite the hype surrounding machine learning and artificial intelligence(AI) most efforts in the enterprise remain in a pilot stage. Part of the reason for this phenomenon is the natural experimentation associated with machine learning projects but also there is a significant component related to the lack of maturity of machine learning architectures. This problem is particularly visible in enterprise environments in which the new application lifecycle management practices of modern machine learning solutions conflicts with corporate practices and regulatory requirements. What are the key architecture building blocks that organizations should put in place when adopting machine learning solutions? The answer is not very trivial but recently we have seen some efforts from research labs and AI data science that are starting to lay down the path of what can become reference architectures for large scale machine learning solutions.

The two biggest barriers to the use of machine learning (both classical machine learning and deep learning) are skills and computing resources. You can solve the second problem by throwing money at it, either for the purchase of accelerated hardware (such as computers with high-end GPUs) or for the rental of compute resources in the cloud (such as instances with attached GPUs, TPUs, and FPGAs). On the other hand, solving the skills problem is harder. Data scientists often command hefty salaries and may still be hard to recruit. Google was able to train many of its employees on its own TensorFlow framework, but most companies barely have people skilled enough to build machine learning and deep learning models themselves, much less teach others how.

The two biggest barriers to the use of machine learning (both classical machine learning and deep learning) are skills and computing resources. You can solve the second problem by throwing money at it, either for the purchase of accelerated hardware (such as computers with high-end GPUs) or for the rental of compute resources in the cloud (such as instances with attached GPUs, TPUs, and FPGAs). On the other hand, solving the skills problem is harder. Data scientists often command hefty salaries and may still be hard to recruit. Google was able to train many of its employees on its own TensorFlow framework, but most companies barely have people skilled enough to build machine learning and deep learning models themselves, much less teach others how.

Business leaders are looking for new methods to leverage data in more meaningful ways. Through the use of machine learning, unique insights become valuable decision points. As developers consider the varied approaches to leverage machine learning, the role of tools comes to the forefront. This Gigaom Research webinar takes a look at the opportunities and challenges that machine learning brings to the development process.

Like many tech companies, IBM is starting the new year by making a few predictions. One of them has to do with a software concept they call a "macroscope," a software technology that can be used to analyze the complexities of the physical world. IBM predicts that within five years, such technology will "help us understand the Earth's complexity in infinite detail." Hyperbole aside, the goal is to better manage world's resources and commercial endeavors that use those resources by applying machine learning algorithms across an array of data sources. That includes geospatial data (weather, soil, water, etc.) as well as data about economic, social and political conditions.

While not widely understood, machine learning has been easily accessible since Google Prediction API was released in 2011. With many applications in a wide variety of fields, this tutorial by Alex Casalboni on the Cloud Academy blog is a useful place to start learning how to build a machine learning model using Google Prediction API. The API offers a RESTful interface as a means to train a machine learning model, and is considered a "black box" due to the restricted access users have to internal configuration. This leaves users with only the "classification" vs "regression" configuration, or the applying of a PMML (Predictive Model Markup Language) file with weighting parameters for categorical models. This tutorial begins with some brief definitions before beginning on how to upload your dataset to Google Cloud Storage, as required by Google Prediction API.