What impact will #COVID19 have on #enterprise investments in #ArtificialIntelligence? Our CEO Ryohei Fujimaki recently shared his thoughts with @TechTarget’s @markrlabbe: https://bit.ly/3gDkKKk #AI #DataScience #DigitalTransformation
#ICYMI: Last week, we unveiled our new all-inclusive bundle of #technology and services, dotData AI-FastStart, which is designed to empower #BusinessIntelligence teams. Learn more via @RTInsights: https://bit.ly/3c3twyC #AI #DataScience #MachineLearning
Data science operationalization, in concept, is simple enough: Take Machine Learning (ML) or Artificial Intelligence (AI) models and move them into production (or operational) environments. In the words of Gartner Sr. Analyst Peter Krensky, data science operationalization is the “…application and maintenance of predictive and prescriptive models…” In practice, however, operationalizing ML and AI models can be a complicated and often overwhelming challenge. In a broader concept, one of the biggest challenges of operationalization is that AI and ML models get integrated with systems that contain live data that changes quickly. For example, if your model is designed to predict customer churn, your data science operationalization process needs to be integrated with your CRM system to predict churn effectively as your data volumes grow.
There are four critical aspects of data science operationalization that make it challenging to implement. First, is the quality of code. Because data scientists use tools like Python and R to develop models, the code is often not of “production quality.” Moving the code to production means that a fair amount of rework has to take place to re-code the models using SQL code that is native to the production database.
The second problem is the integration challenge. Integrating data and scoring pipelines with the multitude of systems that are often associated with data science projects requires a lot of integration work that is time-consuming and highly technical.
Even when models are appropriately integrated, they must be maintained. Accuracy of metrics and model prediction accuracy must be continuously monitored, and models need to be adjusted over time as data changes. This process involves retraining models regularly, which is time-consuming and expensive.
Data science models often rely on a tiny subset of the full available data set. In a churn model, for example, the models might be developed on less than 40% of the available data, but in production, the models need to scale to process 100% of available customer data to predict churn. Another aspect of scalability is the ability of the server to scale up and down depending on the level of power required. Many customers underestimate the compute power required and have problems when ML models break or fail.
In most organizations, the data science team uses software tools and configurations that are often markedly different from production environments. That means that taking models developed by data scientists and operationalizing them entails porting the code to platforms and systems not initially taken into account during model development.
The answer to the many challenges of operationalizing AI and ML models is automation. By using API-based integration, AutoML platforms can accelerate AI and ML model development through automation and can alleviate the operationalization headaches associated with moving models into production. By using a standard approach to deployment, using container technology (Docker) will address compatibility and porting challenges.
Want to learn more? Download our complimentary white paper on data science operationalization and learn how you can take the headaches out of your data science process today.
Reposted from TechTarget – Ryohei Fujimaki, Ph.D., founder and CEO of dotData, comments on how ML (Machine Learning) has significant potential for solving business problems. Read the full article here – The Future State of Machine Learning Needs Improved Frameworks #datascience #AutoML
We’re excited to share that dotData was just named to CRN’s list of the “10 Hot Big Data Companies To Watch In 2020.” To learn more about our work in data science automation and our inclusion in the list, read it here: https://bit.ly/35Z9lir
SAN MATEO, Calif., Oct. 30, 2019 — dotData, the first and only company focused on delivering full-cycle data science automation and operationalization for the enterprise, today announced that it has raised $23 million in Series A funding, bringing the total amount of funding raised to date to $43 million. The Series A financing round was led by JAFCO with participation by Goldman Sachs, who both join existing dotData Seed round investors NEC Corporation.
dotData will use the funds to further accelerate the company’s rapid growth by expanding sales and marketing efforts, and enhancing product development innovation of its full-cycle data science automation platform.
The Series A funding comes just 18 months after dotData’s launch and Series Seed round, and builds on an exceptional year for dotData which saw a more than 300 percent increase in revenue growth, multiple product launches and significant recognition as a leader in the rapidly-growing AutoML market, including in The Forrester New Wave: Automation-Focused Machine Learning (AutoML) Solutions, Q2 2019.
dotData offers the most powerful and broad machine learning automation solution as far as we know. We are impressed with their passion to tackle the big challenge of automating the full-cycle data science process from raw data through feature engineering to machine learning in production, to meet the globally-increasing demand for solutions to help enterprises optimize value from their AI and machine learning initiatives,” said Tomotake Kitazawa, Partner of JAFCO. “dotData is well-positioned to lead this growing AutoML market segment with its innovation. We are excited to partner with dotData as they continue to build a leading company in an exciting category.”
“We are pleased with the confidence our investors show in our vision, team, product and ability to execute and expand market share,” said Ryohei Fujimaki, Ph.D., CEO, and founder of dotData. “Our company’s rapid growth over the past 18 months signals a significant market demand for our unique data science automation platform. These funds will enable us to accelerate product development and innovation to continue bringing transformational value to our customers.”
“We are thrilled about dotData’s significant growth since it spun out from NEC, and delighted that it is accelerating its evolution with Series A funds from JAFCO and Goldman Sachs. dotData has proved with our clients that its platform accelerates enterprise data science 10x faster and delivers key business insights via its proprietary AI-powered feature engineering technology,” said Osamu Fujikawa, Senior Vice President of NEC Corporation. “NEC and dotData have already provided solutions to more than 30 companies in Japan and will continue to promote growth and accelerate digital transformation for customers with the support of JAFCO and Goldman Sachs. We are excited about our strengthening partnership with dotData and will continue to support its business expansion and vision to empower all enterprises through its data science technology.”
dotData is one of the only platforms that combines AI-powered feature engineering and AutoML to automate the full life-cycle of the data science process, from source data through feature engineering to implementation of machine learning in production. dotData’s AI-powered feature engineering automatically applies data transformation, cleansing, normalization, aggregation, and combination and transforms hundreds of tables with complex relationships and billions of rows into a single feature table, automating the most manual data science projects.
dotData democratizes data science by enabling existing resources to perform data science tasks, making enterprise data science scalable and sustainable. dotData also operationalizes data science by producing both feature and ML scoring pipelines in production, which IT teams can then immediately integrate with business workflow. This further automates the time-consuming and arduous process of maintaining the deployed pipeline to ensure repeatability as data changes over time. With the dotData GUI, the data science task becomes a five-minute operation, even without significant data science experience nor SQL/Python/R coding.
For more information or a demo of dotData’s AI-powered full-cycle data science automation platform, please visit dotData.com.
Since establishing the first investment partnership in Japan in 1982, JAFCO has specialized in the private equity investment business. As of March 2019, it has established over 100 investment partnerships with total capital commitments of approximately ¥1 trillion. Its portfolio IPOs have reached 1,005 on a cumulative basis.
In addition to its rich investment experience and management support expertise that it has built over the years, JAFCO will utilize its extensive network with domestic/ overseas venture companies, financial institutions and business firms to carry on investment with a co-founder mindset in growth potential companies.
The Goldman Sachs Group, Inc. is a leading global investment banking, securities and investment management firm that provides a wide range of financial services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals. Founded in 1869, the firm is headquartered in New York and maintains offices in all major financial centers around the world.
NEC Corporation is a leader in the integration of IT and network technologies that benefit businesses and people around the world. The NEC Group globally provides “Solutions for Society” that promote the safety, security, efficiency and fairness of society. Under the company’s corporate message of “Orchestrating a brighter world,” NEC aims to help solve a wide range of challenging issues and to create new social value for the changing world of tomorrow. For more information, visit NEC at https://www.nec.com
dotData is the first and only company focused on full-cycle data science automation. Fortune 500 organizations around the world use dotData to accelerate their ML and AI projects and deliver higher business value. dotData’s automated data science platform speeds time to value by accelerating, democratizing, augmenting and operationalizing the entire data science process, from raw business data through data and feature engineering to machine learning in production. With solutions designed to cater to the needs of both data scientists as well as citizen data scientists, dotData provides unmatched value across the entire organization.
dotData’s unique AI-powered feature engineering delivers actionable business insights from relational, transactional, temporal, geo-locational, and text data. dotData has been recognized as a leader by Forrester in the 2019 New Wave for AutoML platforms. dotData has also been recognized as the “best machine learning platform” for 2019 by the AI breakthrough awards and was named an “emerging vendor to watch” by CRN in the big data space. For more information, visit www.dotdata.com, and join the conversation onTwitter andLinkedIn.
Ryohei Fujimaki, CEO of dotData, discusses some key AI predictions for 2020 with @HypergridBiz
https://www.hypergridbusiness.com/2019/10/enterprise-ai-predictions-for-2020/
With AutoML trending in data science, our CEO spoke at #Ai4Finance on data preparation, aggregating tables, feature engineering, the #AutoML process, and AutoML’s missing gaps. We’ll post the Conclusion / Part 2 next Thursday. Video: Part 1 – AutoML and Beyond.
dotData CEO discusses the five key factors shaping #datascience today in this article for @InformationWeek. Check it out @ Information Week: Five Factors Shaping Data Science. #AI #transformation
conclusion from last week…Part 2
While the rise of AutoML platforms has provided for faster execution of “test and learn” ML development, it has also brought about additional challenges. In most ML and data science projects, ML development is only one part of the process. The earlier stages of the process that require handling multiple raw tables and manipulating them based on in-depth domain knowledge to create flat, aggregated feature tables is a far more complicated and time-consuming challenge. The data and feature engineering process in enterprise data science has to deal with such different data as relational, transactional, temporal, geo-locational, and text data, which never starts from a single, flat, aggregated and cleansed table.
Data science automation provides for a full-cycle automation process that includes data and feature engineering, in addition to standard AutoML. The ability to automatically generate features from massive and complex tables further accelerates data scientist productivity and can deliver new business insights that augment knowledge, by exploring millions of new feature hypotheses.
dotDataPy is an enterprise-grade data science automation platform designed to make the life of the data scientist easier, while also working within the framework preferred by data scientists. dotDataPy allows data scientists to leverage data science automation within Python and execute the full-cycle process from raw business data through data and feature engineering through machine learning with only a few lines of Python code. Data scientists can quickly explore and validate their use cases with minimal upfront efforts.
dotDataPy provides the power of automation, but is also flexible enough to handle advanced use cases. dotDataPy can interface with a standard Python dataframe (like Pandas or Spark dataframe), ensuring that your preferred Python tools can easily consume any output generated by dotDataPy. dotDataPy is also easily connected with any data sources through dataframes. For example, data scientists can leverage dotDataPy features in their preferred ML libraries to fine-tune or adjust models, based on advanced model requirements. Inversely, data scientists can combine domain-specific features they may have created manually with dotDataPy’s AI-derived features and create a unified model that leverages both domain expertise as well as AI-derived knowledge.
The world of data science is changing at a rapid pace. AutoML platforms have made it easier and faster for data scientists to develop advanced machine learning models without the traditional manual hassles and complications associated with the process. The challenge, however, is that much of the manual work done by data scientists has, until now, still been 100% manual. Platforms like dotDataPy are providing data scientists with the opportunity to accelerate the feature engineering to provide data scientists with broader insights and giving them the ability to deliver ML and AI models faster while still working within the Python ecosystem that is the “go-to” standard for the data science community.
Read the latest article from our CEO Ryohei Fujimaki on how #automation is transforming #datascience in
@Forbes Cognitive World
Read the latest article from our CEO Ryohei Fujimaki on how #automation is transforming #datascience in
@Forbes Cognitive World
