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.

Founder and CEO of DotData, Ryohei Fujimaki, explains how automation can help the data science industry become more efficient. Of the many technologies that will shape how we work in the future, automation is one of the most hotly debated. Some look forward to the new avenues it will open up while others fear it will make their skills redundant. Dr Ryohei Fujimaki, founder and CEO of data science company DotData, believes that data scientists are among those that will benefit the most. Fujimaki's team at DotData is helping companies accelerate their data science process.

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.

Using dotData, an automated machine learning vendor, one of the largest insurance firms in Japan built out an AI platform that provides a personalized experience to customers. Mitsui Sumitomo Insurance, one of the largest insurance firms in Japan, began the process of digital transformation several years ago. The company launched multiple projects, and continues to start new projects, to send it further into the digital age. One of MSI's more ambitious undertakings is the MS1 Brain platform, an AI in insurance project to create a more personalized experience for customers. Released earlier this year, the MS1 Brain platform uses machine learning and predictive analytics, along with customer data, including contract details, accident information and lifestyle changes, to recommend products and services to customers based on their predicted needs.

MSI first connected with dotData in 2017, when MSI's CIO visited Silicon Valley for a technical survey, Yokoyama said. At that time, dotData was just getting started, and it hadn't released a product. Still, MSI was intrigued by its automated machine learning platform, which claims to provide full-cycle machine learning automation. "When it comes to data analysis, model accuracy often gets the most attention; dotData, on the other hand, focuses on how quickly you can move from raw data to working models -- the AI-based feature engineering is what stood out," Yokoyama said. MSI had to build a lot of intelligent models, said Ryohei Fujimaki, CEO and founder of dotData.

The employment of Data Science and Machine Learning technologies is at a peak. We can see several software and tools with various innovative features in the market that serve us with the efficiency of new-age data technologies that can potentially increase a business's efficiency and value proposition. With continuous evolution at scale such solutions too, get revamped with time. Now is the era for automated data science and machine learning software that not only enhance the operational proficiency of such tools but also assist data scientists with great potential. They help automate the repetitive and mundane tasks within the ML or data science processes without compromising model performance and productivity. Therefore, here is the list of top 10 automated data science and machine learning software presented by some key players of the respective market.

A very strong Java programmer in the other technologies might work out, too. Our platform automates the entire process of building predictive models starting from raw business data through data and feature engineering to machine learning all the way to production. We have offices in the USA, Japan, and Poland. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects. Unique to the dotData Platform is its AI-powered feature engineering, which eliminates the most time-consuming and labor- and skill-intensive aspects of the full data science process by discovering and evaluating millions of features derived from relational, transactional, temporal, geo-locational, or text data.

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.