10 Tech Careers With The Most Job Security in 2023
Machine Learning Applications In Finance
About the courseThis interactive learning event brings together an industry expert and course participants to focus on the intersection between machine learning and finance. Participants will discover the many applications of novel machine learning methods in risk management, focusing primarily on supervised learning models, neural nets and deep learning.
Learn best practices for the integration of data science into a financial institution through active discussion and Q&As. Frequent challenges will be addressed regarding anomaly detection, lack of AI explainability and classifying a highly imbalanced dataset. Participants will come away with the necessary knowledge to measure the performance of machine learning models used for effective risk management.
A basic understanding of statistics and data manipulation is required for participation in this event.
Pricing options:*The 30% subscriber reward discount is applicable only to current Risk.Net subscribers. If this criteria is not met, we reserve the right to cancel the booking and issue an invoice for the correct rate. Discounts cannot be applied to already registered participants.
10 Common Uses For Machine Learning Applications In Business
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Access your Pro+ Content below.February 2023
Machine learning (ML) enables businesses to perform tasks on a scale previously thought impossible. As a result, many organisations are finding ways to harness ML to not just drive efficiencies but to fuel new business opportunities. Here are 10 applications of ML that are being used to solve problems & deliver tangible business benefits: Here are 10 applications of machine learning in business that are being used to solve problems and deliver tangible business benefits:
The 10 Hottest Data Science And Machine Learning Startups Of 2022 (So Far)
The 10 Hottest Data Science And Machine Learning Startups Of 2022 (So Far)CRNData science and machine learning technologies are in big demand as businesses look for ways to analyze big data and automate data-focused processes. Here are 10 startups with leading-edge data science and machine learning technology that have caught our attention (so far) this year.
Learning Curve
As businesses wrestle with ever-greater volumes of data, both generated within their organizations and collected from external sources, finding efficient ways to analyze and "operationalize" all that data for competitive advantage is increasingly challenging.
That's driving demand for new tools and technologies in the realms of data science and machine learning. The global machine learning market alone reached $15.44 billion in 2021, is expected to reach $21.17 billion this year and grow to $209.91 billion by 2029 for a CAGR of 38.8 percent, according to a Fortune Business Insights report.
The global market for data science platforms, meanwhile, was valued at $4.7 billion in 2020 and is projected to reach $79.7 billion by 2030, a CAGR of 33.6 percent, according to an Allied Market Research report.
"Data science" and "machine learning" are sometimes confused and even used interchangeably. They are two different things, but they are related in that data science practices are key to machine learning projects.
Data science is a field of study that uses a scientific approach to extract meaning and insights from data, according to the Master's in Data Science website. It includes developing data analysis strategies, preparing data for analysis, developing data visualizations and building data models.
Machine learning, a subsegment of the broader AI universe, uses data analytics to teach computers how to learn – imitating the way that people learn – using models based on algorithms and data, according to the Fortune Business Insights report.
Read the latest entry: The 10 Hottest Data Science and Machine Learning Startups in 2022
The demand for data science and machine learning tools has spawned a wave of startup companies developing leading-edge technology in the data science/machine learning arena. Here's a look at 10 of them:
*Aporia
*Black Crow AI
*Comet
*dotData
*Neuton
*Pinecone
*Snorkel AI
*Striveworks
*Tecton
*Verta
Top Executive: Liran Hason, Founder and CEO
Headquarters: Tel Aviv, Israel
Website: https://www.Aporia.Com/
Aporia develops a full-stack, highly customizable machine learning observability platform that data science and ML teams use to monitor, debug, explain and improve machine learning models and data.
Aporia raised $25 million in Series A funding in March 2022, 10 months after raising $5 million in seed funding.
The startup, founded in 2020, is using the financing to triple its headcount through early 2023, expand its presence in the U.S., and increase the range of use cases addressed by its technology.
Top Executive: Richard Harris, Founder and CEO
Headquarters: New York
Website: https://www.Blackcrow.Ai/
Black Crow AI develops a machine learning platform for ecommerce applications, developing models that online direct-to-consumer merchants use to predict visitors' behavior and future value as they shop. The software analyzes billions of data points in real time to enhance customer experience, manage customer churn and optimize marketing spending.
The company, founded in 2020, raised $25 million in Series A funding in March, bringing its total financing to $30 million. The company is using the funds to accelerate development of new and accessible machine learning use cases in digital commerce and adjacent verticals.
Top Executive: Gideon Mendels, Co-Founder and CEO
Headquarters: New York
Website: https://www.Comet.Ml/site/
The Comet platform provides data scientists and data science teams with the ability to manage and optimize the entire machine learning lifecycle from building and training models, experiment tracking and model production monitoring – improving visibility, collaboration and productivity.
Comet, founded in 2017, raised $50 million in Series B funding in November. At the time the company said that its annual recurring revenue had grown by a factor of five, its global workforce had tripled and its customer base included Ancestry, Etsy, Uber and Zappos.
Top Executive: Ryohei Fujimaki, Founder and CEO
Headquarters: San Mateo, Calif.
Website: https://dotdata.Com/
dotData's software provides automated feature engineering and enterprise AI automation for building AI/machine learning models. (Feature engineering is the critical step in machine learning development of finding important patterns hidden in the data used to develop and train ML models.)
In addition to the company's flagship dotData Enterprise predictive analytics automation software, the company offers related products including the dotData Cloud AI automation platform, dotData Py and dotData Py Lite tools, and dotData Stream for real-time AI models.
dotData, founded in 2018 as a spin-off of NEC, raised $31.6 million in Series B funding in April, bringing the company's total financing to $74.6 million. The company has been using the additional funding largely to accelerate product development.
Top Executive: Andrey Korobitsyn, CEO
Headquarters: San Jose, Calif.
Website: https://neuton.Ai/
Neuton, founded in 2021, develops an automated, no-code "tinyML" platform and other tools for developing tiny machine learning models that can be embedded within microcontrollers that can make edge devices intelligent.
The company's technology is finding its way into a wide range of applications including predictive maintenance for compressor water pumps, preventing electrical grid overloads, room occupancy detection, handwriting recognition on handheld devices, gearbox fault prediction and water pollution monitoring devices.
Top Executive: Edo Liberty, Founder and CEO
Headquarters: San Francisco
Website: https://www.Pinecone.Io/
Pinecone develops a vector database and search technology for powering AI and machine learning applications. In October the company launched Pinecone 2.0, which the company said takes the software from the research lab to production applications.
Founded in 2019 and launched last year, Pinecone raised $28 million in Series A funding in March, adding to the $10 million in seed funding it raised in January 2021.
Gartner recognized Pinecone in 2021 as a "Cool Vendor" in the category of data for artificial intelligence and machine learning.
Top Executive: Alex Ratner, Co-Founder and CEO
Headquarters: Redwood City, calif.
Website: https://snorkel.Ai/
Snorkel, founded in 2019, has its roots in the Stanford University AI Lab where the company's five founders researched ways to address the problem of the lack of labeled training data for machine learning development.
The Snorkel Flow data-centric system, which Snorkel just made generally available in March, is used to accelerate AI and machine learning development through the use of programmatic labeling, a key step in data preparation and machine learning model development and training.
Snorkel's company valuation hit $1 billion in August 2021 when the startup raised $85 million in Series C funding, financing the company is using to grow its engineering and sales teams and accelerate development of its platform.
Top Executives: James Rebesco, CEO
Headquarters: Austin, Texas
Website: https://striveworks.Us/
Striveworks, launched in 2018, develops MLOps technology for highly regulated industries.
The company's flagship Chariot Platform for operational data science is used to alleviate the burdens of creating and producing AI/machine learning solutions. The system oversees data ingestion and preparation tasks and the training, validation, deployment and monitoring of machine learning models – all in the cloud, on premises or at the network edge.
Top Executive: Mike Del Balso, Co-Founder and CEO
Headquarters: San Francisco
Website: https://www.Tecton.Ai/
Tecton develops a machine learning feature store platform that the company says can speed the deployment of machine learning applications from months to minutes. The company's technology automates the transformation of raw data, generates training data sets and serves up features for online inference at scale.
Tecton was founded in 2019 by the developers who created Uber's Michelangelo machine learning platform. The company exited stealth in April 2020.
Top Executive: Manasi Vartak, CEO
Headquarters: Menlo Park, Calif.
Website: https://www.Verta.Ai/
The Verta platform is used by data science and machine learning teams for deploying, operating, managing and monitoring models throughout the AI and ML model lifecycle.
Verta was recognized this month by market researcher Gartner as a "Cool Vendor" in core AI technologies.
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