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In this special guest feature, Rob Dickinson, CTO, Resurface Labs, suggests that to achieve greater success with AI/ML models, through accurate business understanding, clear data understanding, and high data quality, todays API-first organizations must shift towards real-time data collection. Robs built all kinds of databases and data pipelines. Keeping the end result in mind, Rob builds data architectures that focus on the consumption of data, whether its blazing fast queries against very large datasets or finding the needle in a haystack. Ultimately delivering better data access across all purposes and teams. Years at Intel, Dell, and Quest Software, framed his passion for customer input, and to find elegant ways to architect and build scalable software.

Whether data scientist or CEO, everyone hungers for more data. Its not just a matter of volume, and not simply an exercise in data viz, todays algorithm-driven organizations want insights as fast as possible those business markers that AI and machine learning teams strive to deliver on.

You cant do effective machine learning without having the Big Data, so organizations must learn to harness the millions (billions?) of daily interactions they have inside and outside their walls. APIs offer an existing and logical pipeline to get data into modelling and analytics processes.

To achieve success with AI and ML models, here are a few API-driven principles around business understanding, data comprehension, and data quality.

Machine learning begins with data access

Did Amazon raise the bar too high? The e-commerce giant blazed the path towards making services visible to everyone through APIs and now, every CEO, CFO, and CMO wants to rule them all. But without the scale and resources of Big Tech, data scientists are forever told the data is coming by IT teams, leading to C-suite executives boxed in by assumptions and guesswork rather than empowered by real-world patterns.

This is especially painful for organizations building out their API strategy at the same time as their AI and ML expertise. Its often a lose-lose race between the teams responsible for infrastructure and the data scientists needing more information now.

For non-Amazon organizations, three principles are fundamental to the success of data analytics:

Additionally, with a greater focus on data access, come the safeguards that all organizations must face, such as implementing privacy and security standards. These processes will only get more complex over time, and restrict how the ML pipeline operates, incurring significant change and compliance overhead the longer a company waits to get it right.

The chances of success in these areas are higher when the barriers to collecting data are lowered, and when the data accurately represents the real-world scenarios being modeled. APIs contain this information already, its just a matter of knowing how to capture, store, and secure it.

Fueling the ML pipeline with the right data

Real-time behavioral data is the pathway towards better business understanding and comprehension. It cannot be overstated that any biases or errors in models are not overcome by looking at the model itself; they can only be mitigated by looking at the original source data.

For example, the success or failure of AI-based personalization engines can only be determined by understanding how customers behave and by adjusting the recommender model with those observations. With a higher level of observability in the business, using current and complete API data raises the ability to bootstrap AI systems more effectively and improve the accuracy of predictions.

To achieve success in real-time API data collection, organizations must:

Ultimately, shifting to real-time API data collection to train, validate, and iterate AI and ML models leads to more timely results and fewer gaps filled by assumptions and guesswork. By arming teams with the skills and tools that connect APIs to data science and DevOps, models will be better able to deliver on the promises of accurate business knowledge, clear data understanding, and high data quality.

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APIs: The Real ML Pipeline Everyone Should Be Talking About - insideBIGDATA

US-based Pranjali Awasthi, a child prodigy in the truest sense, is currently working on the overlap of neuroimaging and ML at the Neural Dynamics of Control Lab at Florida International University in Miami, Florida. At present, she is busy building a classifier for error detection in cognitive tasks using EEG imaging. This project has also received a grant from the New York Institute of Technology. The 15-year-old has also worked on an AI-based sign language detector, a mental health companion app, and an RNN-based diabetic retinopathy diagnostic tool.

Awasthi moved to the US from India with her parents when she was just 11. I grew up in an environment where learning and curiosity were encouraged. My parents are well-versed in academia, with my mother in humanities and my father in science fields. The importance of education has been stressed in the environment I have been growing in. I got into research because of my dad who was also pursuing research in the field of the computer-brain interface. Further, the factor of social impact was a big factor in my upbringing. I was always told that it is always how much impact you have made in your community at the end of the day, said Pranjali.

She is also an entrepreneur and has founded Indic Valley, an online store for underrepresented artists in India.

For the event, Pranjali spoke on the importance of introducing AI to children from a younger age. When I first started, I realised people dont take AI very seriously. It is also limiting the number of opportunities available for AI enthusiasts to connect and grow significantly, she said.

She feels that there are three main challenges that hinder AI learning among young students:

Pranjali believes that, despite the penetration of AI technology in almost every facet of our lives, the knowledge base is very concentrated in limited hands. Younger children especially are often left out from the conversation and discourse around it. This should not happen. Instead, there should be more assertiveness and programmes built specifically for young students to teach and practice AI, said Pranjali.

There are programmes for high schoolers, there are programmes even for middle schoolers, but I feel we need to start even more early and introduce AI as a core subject even in elementary school starting from basic projects to increase their knowledge base. Mandating AI learning and establishing teaching certifications should be considered, she added.

The average age in the US for a child to use social media is 10. Pranjali said this could be a good opportunity for introducing them to the algorithms running behind their favourite apps. She also believes children should be allowed to harness their creativity and translate that to learning and researching in AI.

Pranjali also spoke about the accessibility and availability aspect of AI. Learning AI in the current situation seems very out of reach for a lot of people. There are a lot of opportunities and resources available on the internet but they should be made available to all. Apart from making these resources available, attention should also be given to enforce and encourage AI learning. The focus should be on creating better innovators and making them excited to learn.

I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.

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How This 15-Year-Old Created A Research Career In Machine Learning - Analytics India Magazine

This report titled as Global Machine Learning Operationalization Software Market, gives a brief about the comprehensive research and an outline of its growth in the market globally. It states about the significant market drivers, trends, limitations and opportunities to give a wide-ranging and precise data and also scrutinizes its growth in the overall markets development which is needed and expected.

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Global Machine Learning Operationalization Software: Regional Segment Analysis

North America

Europe

Asia Pacific

Middle East & Africa

South America

Companies Profiled in this report includes.

MathWorks

SAS

Microsoft

ParallelM

Algorithmia

H20.ai

TIBCO Software

SAP

IBM

Domino

Seldon

Datmo

Actico

RapidMiner

KNIME

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This report provides pinpoint analysis for changing competitive dynamics. It offers a forward-looking perspective on different factors driving or limiting market growth. It provides a five-year forecast assessed on the basis of how the Global Machine Learning Operationalization Software Market is predicted to grow. It helps in understanding the key product segments and their future and helps in making informed business decisions by having complete insights of market and by making in-depth analysis of market segments.

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Comprehensive Analysis of Global Machine Learning Operationalization Software Market with Current and Future Business Outlook | MathWorks, SAS,...

At Google I/O today Google Cloud announced Vertex AI, a new managed machine learning platform that is meant to make it easier for developers to deploy and maintain their AI models. Its a bit of an odd announcement at I/O, which tends to focus on mobile and web developers and doesnt traditionally feature a lot of Google Cloud news, but the fact that Google decided to announce Vertex today goes to show how important it thinks this new service is for a wide range of developers.

The launch of Vertex is the result of quite a bit of introspection by the Google Cloud team. Machine learning in the enterprise is in crisis, in my view, Craig Wiley, the director of product management for Google Clouds AI Platform, told me. As someone who has worked in that space for a number of years, if you look at the Harvard Business Review or analyst reviews, or what have you every single one of them comes out saying that the vast majority of companies are either investing or are interested in investing in machine learning and are not getting value from it. That has to change. It has to change.

Image Credits: Google

Wiley, who was also the general manager of AWSs SageMaker AI service from 2016 to 2018 before coming to Google in 2019, noted that Google and others who were able to make machine learning work for themselves saw how it can have a transformational impact, but he also noted that the way the big clouds started offering these services was by launching dozens of services, many of which were dead ends, according to him (including some of Googles own). Ultimately, our goal with Vertex is to reduce the time to ROI for these enterprises, to make sure that they can not just build a model but get real value from the models theyre building.

Vertex then is meant to be a very flexible platform that allows developers and data scientist across skill levels to quickly train models. Google says it takes about 80% fewer lines of code to train a model versus some of its competitors, for example, and then help them manage the entire lifecycle of these models.

Image Credits: Google

The service is also integrated with Vizier, Googles AI optimizer that can automatically tune hyperparameters in machine learning models. This greatly reduces the time it takes to tune a model and allows engineers to run more experiments and do so faster.

Vertex also offers a Feature Store that helps its users serve, share and reuse the machine learning features and Vertex Experiments to help them accelerate the deployment of their models into producing with faster model selection.

Deployment is backed by a continuous monitoring service and Vertex Pipelines, a rebrand of Google Clouds AI Platform Pipelines that helps teams manage the workflows involved in preparing and analyzing data for the models, train them, evaluate them and deploy them to production.

To give a wide variety of developers the right entry points, the service provides three interfaces: a drag-and-drop tool, notebooks for advanced users and and this may be a bit of a surprise BigQuery ML, Googles tool for using standard SQL queries to create and execute machine learning models in its BigQuery data warehouse.

We had two guiding lights while building Vertex AI: get data scientists and engineers out of the orchestration weeds, and create an industry-wide shift that would make everyone get serious about moving AI out of pilot purgatory and into full-scale production, said Andrew Moore, vice president and general manager of Cloud AI and Industry Solutions at Google Cloud. We are very proud of what we came up with in this platform, as it enables serious deployments for a new generation of AI that will empower data scientists and engineers to do fulfilling and creative work.

Continue reading here:
Google Cloud launches Vertex AI, a new managed machine learning platform - TechCrunch

Media and publishing used to be the domain of specialized companies who controlled the content. The internet broke that model, and today anyone can go online and publish a blog, a podcast, or star in their own video.

But the big tech companies want to take control, closing content into walled gardens. But thats not what the majority of publishers, big or small, want.

We get to hear the perspectives of the publishers at every scale, and they consistently tell us the same thing: They want to more directly connect to consumers; they dont want to be tied into these walled gardens which dictate how they must present their content and in some cases what content theyre allowed to present, said Dr. Peter Day (pictured, right), chief technology officer at Quantcast Corp.

Day and Shruti Koparkar (pictured, left), head of product marketing at Quantcast, spoke with John Furrier, host of theCUBE, SiliconANGLE Medias livestreaming studio, duringThe Cookie Conundrum: A Recipe for Success event. They discussed the importance of smart technology for the post-cookie future of digital marketing. (* Disclosure below.)

Quantcast has cast itself as a champion of the open internet as it sets out to find the middle ground between the ability to scale provided by walled gardens and access to individual-level user data. Urgency for the quest is provided by Goliath company Google, which announced it will no longer be supporting third-party cookies on its Chrome browser as of January 2022.

Our approach to a world without third-party cookies is grounded in three fundamental things, Koparkar stated. First is industry standards: We think its really important to participate and to work with organizations who are defining the standards that will guide the future of advertising, Koparkar said, naming IAB Technology Laboratorys Project Rearc and Prebid as open projects Quantcast is involved with.

The companys engineering team also participates in meetings with the World Wide Web Consortium (W3C) to keep on top of what is happening with web browsers and to monitor what Google is up to with its Federated Learning of Cohorts (FLoC) project.

The second fundamental principle to Quantcasts strategy is interoperability. With multiple identity solutions from Unified ID 2.0 to FLoC already existing, and more on the way, We think it is important to build a platform that can ingest all of these signals, and so thats what weve done, Koparkar said referring to the release of Quantcasts intelligent audience platform.

Innovation is the third principle. Being able to take in multiple signals, not only IDs and cohorts, but also contextual first-party consent, time, language, geolocation and many others is increasingly important, according to Kopackar.

All of these signals can help us understand user behavior, intent and interests in absence of third-party cookies, she said.

But these signals are raw, messy, complex and ever-changing. What you need is technology like AI and machine learning to bring all of these signals together, combine them statistically, and get an understanding of user behavior, intent and interest, and then act on it, Koparkar stated. And the only way to bring them all together to obtain coherent understanding is through intelligent technologies such as machine learning, she added.

The foundation of our platform has always been machine learning from before it was cool, Day said. Many of the core team members at Quantcast have doctorate degrees in statistics and ML, which means it drives the companys decision-making.

Data is only useful if you can make sense of it, if you can organize it, and if you can take action on it, Day said. And to do that at this kind of scale its absolutely necessary to use machine learning technology.

Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEs coverage of The Cookie Conundrum: A Recipe for Success event. (* Disclosure: TheCUBE is a paid media partner for The Cookie Conundrum: A Recipe for Success event. Neither Quantcast Corp., the sponsor for theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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Quantcast uses machine learning and AI to take on walled garden giants in the fight for the open internet - SiliconANGLE News