As a full-stack machine learning consultant that focuses on building and delivering new products to market, I've often found myself at the intersection of data science, data engineering and dev ops. So it has been with great interest that I've followed the rise of data science Platforms as a Service (PaaS). I recently set out to evaluate different Platforms as a Service (PaaS) and their potential to automate data science operations. I'm trying to find the best way for the book's readers to work through the examples. In my last book, Agile Data Science 2.0 (4.5 stars), I built my own platform for readers to run the code using bash scripts, the AWS CLI, jq, Vagrant and EC2.

As machine learning technology becomes more widely available on an enterprise scale, differentiating and studying which platform can be best for your business can be difficult. A new white paper from SparkCognition explores one of the solutions on the market that works to accelerate data science at scale. Its Darwin machine learning platform is designed to automate the building and deployment of models. According to SparkCognition, the tool works to provide "a productive environment that empowers data scientists with a broad spectrum of experience to quickly prototype and develop, tune and implement machine learning applications in less time." Using its automated model building capabilities, the system works to generate models using both supervised and unsupervised learning.

"It is currently believed that many data scientists using R, Python and other technologies should be deployed in order to leverage analytics to achieve business results. However, dotData will overturn this belief and open data science technology to business persons and make them citizen data scientists. This impact seems similar to the one of cloud computing, which destroyed the superiority of large enterprises owning mainframes and high end servers, and opened these computer resources to SOHO users. In that sense, today should be commemorated as the turning point of analytics," he concluded.

RapidMiner, the company that delivers real data science, fast and simple, today announced the immediate availability of RapidMiner 8.1 and RapidMiner Auto Model, a new addition to RapidMiner Studio that accelerates everything data scientists do when building machine learning models. "Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal."

"Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal." RapidMiner Auto Model accelerates the entire data science lifecycle using automated machine learning.

STRATA HADOOP WORLD SAN JOSE, Calif., March 14, 2017 – Cloudera, the provider of the leading global platform for machine learning and advanced analytics built on the latest open source technologies, today unveiled Cloudera Data Science Workbench, a new self-service environment for data science on Cloudera Enterprise which is currently in beta. Based on the company's acquisition of data science startup Sense.io last year, Data Science Workbench allows data scientists to use their favorite open source languages -- including R, Python, and Scala -- and libraries on a secure enterprise platform with native Apache Spark and Apache Hadoop integration, to accelerate analytics projects from exploration to production. "Cloudera is focused on improving the user experience for data science and engineering teams, in particular those who want to scale their analytics using Spark for data processing and machine learning," said Charles Zedlewski, senior vice president, Products at Cloudera. "The acquisition of Sense.io and its team provided a strong foundation, and Data Science Workbench now puts self-service data science at scale within reach for our customers." Cloudera Data Science Workbench's benefits include: Beyond the extensive Python and R ecosystems, as open data science expands to include deep learning frameworks like Tensorflow, Microsoft Cognitive Toolkit, MXnet, BigDL, and more, data science teams are looking for ways to bring these tools to their data, which is increasingly stored in Hadoop environments Cloudera Data Science Workbench delivers a safe and secure environment to combine the latest open source innovations with the unified platform Cloudera customers trust.

STRATA HADOOP WORLD SAN JOSE, Calif., March 14, 2017 – Cloudera, the provider of the leading global platform for machine learning and advanced analytics built on the latest open source technologies, today unveiled Cloudera Data Science Workbench, a new self-service tool for data science on Cloudera Enterprise which is currently in beta. Based on the company's acquisition of data science startup Sense.io last year, Data Science Workbench allows data scientists to use their favorite open source languages -- including R, Python, and Scala -- and libraries on a secure enterprise platform with native Apache Spark and Apache Hadoop integration, to accelerate analytics projects from exploration to production. "Cloudera is focused on improving the user experience for data science and engineering teams, in particular those who want to scale their analytics using Spark for data processing and machine learning," said Charles Zedlewski, senior vice president, Products at Cloudera. "The acquisition of Sense.io and its team provided a strong foundation, and Data Science Workbench now puts self-service data science at scale within reach for our customers." Cloudera Data Science Workbench's benefits include: Beyond the extensive Python and R ecosystems, as open data science expands to include deep learning frameworks like Tensorflow, Microsoft Cognitive Toolkit, MXnet, BigDL, and more, data science teams are looking for ways to bring these tools to their data, which is increasingly stored in Hadoop environments Cloudera Data Science Workbench delivers a safe and secure environment to combine the latest open source innovations with the unified platform Cloudera customers trust.