Many artificial intelligence experts say that running the AI algorithm is only part of the job. Preparing the data and cleaning it is a start, but the real challenge is to figure out what to study and where to look for the answer. Is it hidden in the transaction ledger? Finding the right features for the AI algorithm to examine often requires a deep knowledge of the business itself in order for the AI algorithms to be guided to look in the right place. DotData wants to automate that work.

DotData today announced version 2.0 of its artificial intelligence and machine learning platform for enterprises. The company automates data science so it can accelerate the adoption of AI and machine learning in corporations. DotData CEO Ryohei Fujimaki said in a fireside chat with me at our Transform 2020 event that enterprises can implement AI and ML tools that generate better business insights and money-saving results. "Everyone is under high pressure to deliver more results with less resources to survive in this economic downturn," Fujimaki said. "AI automation will change this game. It significantly accelerates the turnaround from months to days."

As data science platforms expand across enterprise applications like predictive analytics, automated machine learning vendors are steadily integrating AI models with emerging infrastructure to ease deployment and orchestration. For example, data science automation specialist dotData this week released a container-based machine learning model aimed at real-time prediction. Applications include automated loan processing, dynamic pricing, fraud detection and industrial Internet of Things deployments such as a smart manufacturing partnership also announced this week. The Stream platform is designed to deliver real-time prediction using dotData's AI and machine learning models. Those models are downloaded from the company's flagship platform via a one-click process akin to launching a Docker application container.

For impactful machine learning, data scientists first need clean, structured data. That's where feature engineering comes in -- to refine data structures that improve the efficiency and accuracy of machine learning models. Ryohei Fujimaki, Ph.D., CEO and founder of dotData, a data science platform, said, "Features are, without question, even more critical than the machine learning algorithm itself." Poor quality features will result in a failure of the machine learning algorithm, he said. On the other hand, high-quality features will allow even simple machine learning algorithms like linear regression to perform well.

The popularity of AI and ML have wide-reaching effects on your enterprise. Here are three important trends driven by AI to look out for next year. As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate the data science process. However, implementing and optimizing machine learning models is only part of the data science challenge. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models such as feature engineering -- the heart of data science.

A data science spinoff from NEC Corp. has raised additional early stage funding to accelerate development of its automated machine learning platform. DotData, San Mateo, Calif, was spun off from its Japanese parent company last year. The new company announced this week it raised $23 million in Series A funding 18 months after its launch and seed funding round. To date, DotData has raised $43 million. The latest funding announced on Wednesday (Oct.

As one of the top tech jobs with the best career opportunities, data scientists have become one of the most coveted jobs across industries in recent years. Taking the no. 1 spot on Glassdoor's Best Jobs in America list for the past four years, tech professionals are scrambling to land this sought after job position. Data science is relevant and important to any business that is churning out high volumes of data, which has lead to the rapid growth of artificial intelligence (AI) and machine learning adoption, said Ryohei Fujimaki, founder and CEO of dotData, a leading company focused on data science automation for the enterprise. "Whether it's a financial services company that wants to mitigate risk, a retailer attempting to predict customer purchasing behavior or a software company attempting to mitigate customer churn, the use-case for AI and machine learning in the world of enterprise are predicated on an effective data science strategy," Fujimaki said. Understanding data science means recognizing the limitations that often come with an effective data science practice, Fujimaki noted.

As one of the top tech jobs with the best career opportunities, data scientists have become one of the most coveted jobs across industries in recent years. Taking the no. 1 spot on Glassdoor's Best Jobs in America list for the past four years, tech professionals are scrambling to land this sought after job position. Data science is relevant and important to any business that is churning out high volumes of data, which has lead to the rapid growth of artificial intelligence (AI) and machine learning adoption, said Ryohei Fujimaki, founder and CEO of dotData, a leading company focused on data science automation for the enterprise. "Whether it's a financial services company that wants to mitigate risk, a retailer attempting to predict customer purchasing behavior or a software company attempting to mitigate customer churn, the use-case for AI and machine learning in the world of enterprise are predicated on an effective data science strategy," Fujimaki said. Understanding data science means recognizing the limitations that often come with an effective data science practice, Fujimaki noted.

As data and the business problems that can be addressed by it proliferate, our ability to analyze them is falling behind. We don't have enough data scientists, we can't create enough good models, and we can't get them into production. Enter automated machine learning (AutoML), which offers substantial potential for solving the problem. This powerful set of tools can help with a wide variety of ML activities, including preparing data for analysis, performing feature engineering, automatically generating well-fitting models using the best algorithm, and generating code or APIs to help deploy the model into production. AutoML has been around in some form since the mid-1990s, but it didn't really take off until the past few years.

The days of handcrafted algorithms aren't quite over, but it's hard to dismiss to impact that automated machine learning (AutoML) is having on the data science field. As companies look to imbue intelligence into their products and services, AutoML tools will lower the barrier of entry into data science and open the door for data-driven automation on vast scales. In the past few years, we've seen a surge of interest in AutoML tools, which automate a range of tasks in the data science workflow. While automated ML features may be found in a range of tools, the AutoML category has a fairly defined set of features, including: acquiring and prepping data; engineering features from the data; selecting the best algorithm; tuning the algorithm; and deployment and monitoring of production models. Forrester says just about every company will have a stand-alone AutoML tool.