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TensorFlow: The Show-and-Tell Data Scientists Have Been Asking For
TensorFlow is among the most popular, if not, the most popular deep learning libraries today. According to one ranking, “TensorFlow is at least two standard deviations above the mean on all calculated metrics.”
Watch as Lak Lakshmanan, Technical Lead, Machine Learning and Big Data, Google Cloud, walks through a development workflow that will make operationalization easier to execute, including the process of building a complete machine learning pipeline covering ingest, exploration, training, evaluation, deployment, and prediction.
He also talks about the need for distributed training. But what’s the benefit of distributed TensorFlow? Many machine learning frameworks can only handle “toy problems”, or problems that can be solved by input data that fits into memory. These are small data sets.
But to build effective machine learning you need big data, feature engineering, and model architectures. With large amounts of data batching and distribution are very important. That’s where distributed training comes in.
Startup Success Blueprint: Insights on Cloud Provider Selection from One AI
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From the newsroom to the boardroom, everywhere we turn these days the topic of conversation is artificial intelligence (AI). From the smallest startups to the largest enterprises, every business is looking for ways to incorporate generative AI technology into their products or services.
Generative AI is a new breed, able to not only discern patterns in data but generalize from them and create new data—and it’s moving fast. With new technologies rapidly emerging, there’s a lot to consider when choosing a tech stack that’s right for your startup.
Our mission is to empower startups to build generative AI applications quickly, efficiently, and responsibly. In addition to our technology, we offer a variety of fresh educational and consulting initiatives, as well as comprehensive plans tailored to specific industry applications.
At the recent Google Cloud Startup Summit, leaders from One AI and MongoDB weighed in on various decisions startups face when choosing a development platform to integrate AI into their products. (You can watch the full conversation here.) They shared key insights into the challenges startups face when applying AI technology to real-world business and product use cases.
In this blog, we’ll explore why One AI – a platform that empowers businesses to deploy tailored AI solutions – relies on MongoDB on Google Cloud for performance, scale, functionality, and TCO. We will also cover the things that startups should keep in mind when building their generative AI stack.
Fine-tuning generative AI for startups
First, let’s start with a little background.
AI-based capabilities have been around for decades now, but generative AI is distinct. It’s a more mature version of AI, powered by models pre-trained on very large datasets composed of massive quantities of images, text, and data. These models are known as foundation models, and they include large language models (LLMs), text-to-image models, multimodal models, and more. Foundation models let intelligent applications generate new images, text, and data based on queries or prompts. This in turn allows companies to deploy those capabilities in their products and services faster since they don’t have to retrain the whole AI from scratch.
Over the course of their diverse startup careers, Amit Ben, CEO at One AI and his team have built AI-based capabilities from the ground up for various products in various fields.
“And each time, we had to rebuild the tech stack over again,” Amit explains. “With the advent of generative AI, startups now have the ability to deploy much faster — with a lower TCO and higher confidence — and deliver the capabilities they need into their products and services. It finally makes sense for every company to have AI in its product portfolio.”
“For us to be able to focus on that,” Amit adds, “we need to make sure we have a rock-solid foundation that we can build on.”
That foundation is MongoDB on Google Cloud.
Helping startups build fast and with flexibility
Startups can scale from ideation to growth with Google Cloud’s global availability, market-leading sustainability, and the same zero-trust security model that Google itself depends on.
With MongoDB, startups can take advantage of iteration cycles that are 3-5X faster, reduce sprawl and complexity, and benefit from the scalable infrastructure and advanced analytics tools on Google Cloud.
With MongoDB on Google Cloud, Amit and his team are confident they can adapt to new schemas, to new data, and to the scale they need for both writing and reading, all while operating on a scalable platform and infrastructure they can rely on for the long haul.
A common mistake for startups is turning to niche, single-point solutions. But this can backfire when they realize their solution doesn’t provide the security, scalability, and performance they need to grow.
Additionally, startups tend to have tight iteration cycles as they find their ideal product market fit. The ability to build fast and with improved flexibility is a key differentiator in a startup environment.
From cutting-edge automation to rock-solid redundancy and performance, there are many reasons why startups choose MongoDB on Google Cloud. And now, a dedicated partnership helps startups like One AI scale more quickly, more securely, and more successfully.
Google Cloud and MongoDB for startups
Choosing the right technology to accelerate time to market is critical to a startup’s success. Not only is it easy to get started, but Google Cloud and MongoDB also provide the foundation for users to scale without limits — so startups can focus on innovating and growing their businesses.
Learn more about the powerful startup programs available from Google Cloud and MongoDB.
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Artificial intelligence and machine learning are already transforming the technological landscape. From digital assistants to image-recognition software to self-driving cars, what was once the stuff of science fiction is now becoming a reality. But what exactly does it mean for marketing and advertising executives?
It could get us closer to one of advertising’s most-sought goals: relevance at scale. Before then, we’re going to see changes to the way we do business.
Technological advances have always created new opportunities for storytelling and marketing. Just as the advent of TV brought an era of truly mass advertising and reach, and the internet and mobile brought a new level of targeting and context, AI will change how people interact with information, technology, brands, and services.
A big part of the opportunity for marketers is how AI will help us fully realize personalization—and relevance—at scale. With platforms like Search and YouTube reaching billions of people everyday, digital ad platforms finally can achieve communication at scale. This scale, combined with customization possible through AI, means we’ll soon be able to tailor campaigns to consumer intent in the moment. It will be like having a million planners in your pocket.
Find out how you can achieve relevance at scale. Download now!
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11:30 Minutes
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Transitioning Kagglers to TPU with TF 2.x
Kaggle has, historically, become synonymous with machine learning competitions but it’s much more than that. Kaggle is a data science platform. Over 5 million data scientists from all over the world come to Kaggle to not only not only participate in machine learning competitions but to learn data science build their skills, polish their portfolios and share data sets and code.
Earlier on Kaggle introduced TPU support through its competition platform. In this video, Addison Howard, Program Manager, Google Cloud and Phil Culliton, Kaggle Data Scientist, Google Cloud talk about how Kaggler competitors transition from GPU to TPU use – first in Colab, and then in Kaggle notebooks.
Customer Voices: How Firms from Across Industries Leverage Google Cloud
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1:30 Minutes
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From powering everyday operations and accelerating application innovation, to providing tools for specific business needs and executing on big ideas, to advancing the security of technology solutions, companies from across industries have leveraged Google Cloud for business benefits.
Companies from across industries have turned to Google Cloud for transforming their business, modernizing their infrastructure, and gleaning intelligence from data. For instance:
- Johnson & Johnson achieved a 41% increase in search results from high-quality job applicants, significantly improving the company’s ability to quickly hire top talent.
- Sony Network Communications now processes 10 billion monthly queries faster, which advances data analysis.
- University College Dublin saw significant 6-figure savings by eliminating legacy hardware, software, and maintenance.
And there are many such examples. Read the collection of case studies to find out how companies from across industries and geographies leveraged Google Cloud for measurable business benefits and for solving complex problems.
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Explore the Innovations and Architecture Powering Spanner and BigQuery
Previously, databases had architectures with tightly coupled storage and compute. This resulted in higher latency, and with faster networks these constraints no longer surface. With Google Cloud’s BigQuery and CloudSpanner, the storage and compute architecture have been separated, allowing for better scalability and availability to address businesses’ high throughput data needs.
Watch the video to understand how these database and analysis products leverage Google’s distributed storage system, in-house custom network hardware and software, internal cluster management system and more!
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