"What good is an ML model if it isn't fast? Having machine learning in a company's portfolio used to be an investor magnet. Now, the market is bullish on MLaaS, with a new breed of companies offering machine learning services (libraries/APIs/frameworks) to help other companies get their job done better and faster. According to PwC, AI's potential global economic impact will be worth $15.7 trillion by 2030. And, as interests slowly shift towards MLOps, it is possible that these companies, which promise to scale and accelerate ML deployment, might grab a bigger piece of the pie. Last week, OctoML raised $28 million. The Seattle-based startup offers a machine learning acceleration platform built on top of the open-source Apache TVM compiler framework project. The $28 million Series B funding brings the company's total funding to $47 million. For OctoML's CEO, Luis Ceze, there is still a significant gap between building a model and making it production-ready. Between rapidly evolving ML models, wrote Ceze in a blog post, ML frameworks and a Cambrian explosion of hardware backends makes ML deployment challenging. "It is not easy to make sure your model runs fast enough and to benchmark it across different deployment hardware.

Getting machine learning models into production continues to plague organizations. To help combat this issue, business leaders, data scientists, and IT teams need to work together to identify a solution to the three hidden issues preventing deployment: production monitoring, model updates, and modern governance. The deployment of machine learning models in production is notoriously difficult. While deployment is still an issue, I would argue that it is one that has mostly been solved by the use of containers and container management systems like Docker and Kubernetes. With a critical technical challenge removed, you would expect more models to start to make it into production.

Deploying and maintaining Machine Learning models at scale is one of the most pressing challenges faced by organizations today. Machine Learning workflow which includes Training, Building and Deploying machine learning models can be a long process with many roadblocks along the way. Many data science projects don't make it to production because of challenges that slow down or halt the entire process. To overcome the challenges of model deployment, we need to identify the problems and learn what causes them. End-to-end ML applications often comprise of components written in different programming languages.

The total amount of power consumed for machine learning tasks is staggering. Until a few years ago we did not have computers powerful enough to run many of the algorithms, but the repurposing of the GPU gave the industry the horsepower that it needed. The problem is that the GPU is not well suited to the task, and most of the power consumed is waste. While machine learning has provided many benefits, much bigger gains will come from pushing machine learning to the edge. To get there, power must be addressed. "You read about how datacenters may consume 5% of the energy today," says Ron Lowman, product marketing manager for Artificial Intelligence at Synopsys.

"We created GraphPipe to solve these three challenges. It provides a standard, high-performance protocol for transmitting tensor data over the network, along with simple implementations of clients and servers that make deploying and querying machine learning models from any framework a breeze.

Seldon Core focuses on solving the last step in any machine learning project to help companies put models into production, to solve real-world problems and maximise the return on investment. Data scientists are freed to focus on creating better models while devops teams are able to manage deployments more effectively using tools they understand. Instead of just serving up single models behind an API endpoint, Seldon Core allows complex runtime inference graphs to be deployed in containers as microservices. Efficiency -- traditional infrastructure stacks and devops processes don't translate well to machine learning, and there is limited open-source innovation in this space, which forces companies to build their own at great expense or to use a proprietary service. Also, data engineers with the necessary multidisciplinary skillset spanning ML and ops are very scarce.

Relentless change; stubborn continuity… The predictions on tech trends for the New Year 2018 seem to be more drastic than any of its previous editions. Deloitte has just launched its annual Deloitte TMT Predictions for 2018, and it has some serious thought-provoking recommendations for marketing and advertising agencies -- focus on machine learning deployments, augmented reality tools, and live-streaming content. Fascinating as always, Deloitte TMT Predictions 2018 emphasize that the technology, media and entertainment, and telecommunications ecosystem are the top enterprise adopters of cutting-edge artificial intelligence capabilities, powered by new chips and better software tools. What should CMOs be expecting in 2018? Well, according to Deloitte's predictions, augmented reality will become more mainstream even as machine learning deployments begin to make a marked impact on marketing budgets.

There's no doubt that we're entering the age of AI, with Machine Learning touching almost everything we're involved in on a day-to-day basis. Spurred on by step innovations in data storage and computing power, Neural Nets are back from the 70's with a bang. Medicine, security, customer service, fraud detection, you name it -- there are well funded companies applying Machine Learning to improve and augment it. Heck, you might have even found this post through Medium's Machine Learning-based recommender systems. Deep Learning, for whatever reason, seems to work really well for a number of problems with immediate impact.