AI and Machine Learning
Introduce efficiencies to improve operations and add intelligence for digital transformation of products or services.
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Let us introduce you to our AI & ML Moderator: Markus KoyUsername: markuskBio:Markus is the founder of thefluent.me and a Google Build Partner and AI enthusiast based in Vancouver, British Columbia. Markus has 18 years of experience as a Business Analyst, IT Project Manager, IT Consultant, Strategic Controller, and Developer. Born and raised in Germany, Markus immigrated to Canada in 2008. His hobbies include fishing, snowshoeing, and hiking. Fine out more about Markus and thefluent.me here: https://community.c2cglobal.com/product-updates/using-ai-to-improve-spoken-language-fluency-with-markus-koy-founder-of-thefluent-me-1628Company: thefluent.meJob Title: Founder/ Owner Mohammad AlaminUsername: malamin Bio:Mohammad is an AI/ML solutions architect at Aiiotsys. A computer training center has opened in Bangladesh. He was teaching computer skills to everyone. He graduated with honors in Computer Science from the University of Greenwich in London.Company: Aiiotsys.Job Titl
Generative AI can boost worker productivity, especially for lower-performing workers. Workers need to understand generative AI and how to incorporate it into their workflows to get the most out of it. Prompt engineering, the skill of refining and guiding models to produce optimal results, is becoming increasingly important. Priyanka Vergadia, a Staff Developer Advocate, wrote a blog post about prompt engineering in detail. To read it, click on the link below.https://cloud.google.com/blog/transform/how-to-be-a-better-prompt-engineer
🚀 Introducing AIT-CodeX: Your FREE AI-Powered Coding Tutor and Consultant with Google PaLM2 AI Integration – Google Prompt Hackathon S3 Entry 🚀
Join this session, where you’ll gain an understanding of Generative AI support on Vertex AI, including:Available foundation models, and when to use which one for your use case How to access foundation models with Model Garden, and tune models with Generative AI Studio to fit your unique needs How to use Generative AI App Builder to build enterprise-ready applications powered by generative AI, including Enterprise Search experiences using structured and unstructured data You’ll also have the opportunity to ask questions and receive answers live.
Generative AI is a hot topic, but it can be tough to know where to start. Join with this Google Cloud AI live lab for an interactive morning where we'll demystify Generative AI and show you how to use it today and tomorrow.October 18, 202309:00–14:30 GMT+2Click on the link below to join this event. https://inthecloud.withgoogle.com/gen-ai-johannesburg-23/register.html
Business conditions are always evolving and data strategies need to keep up. We know that the relationship with data can be complex, so Google cloud bringing you the latest insights and perspectives to equip you to enhance your data strategy in 2023.This is good opportunity to discover the emerging trends in data and AI so you can prepare for 2023 to join this in-person event that will held in Google cloud Office, UK.Click on the link below to join this event.Where : Google UK, 6 Pancras Square, London N1C 4AG Date: January 24, 2023 15:00–19:00 GMT click on the link below to join this event.https://inthecloud.withgoogle.com/data-trends-23-uk/register.html
Wayfair's Supply Chain Science team uses machine learning to provide predictive capabilities for use cases such as delivery-time prediction, incidence-costs prediction, or supply-chain simulation.The team previously used Apache Airflow to orchestrate their models, experiments, and data pipelines. However, this caused several issues, including slow delivery processes and noisy neighbor problems.To address these issues, the team migrated to Vertex AI Pipelines. Vertex AI Pipelines is a serverless, managed ML pipeline service that provides a number of benefits, including:Improved reliability and scalability Reduced time to value Increased reproducibility and traceabilityThe team also took the opportunity to refine their MLOps tooling and processes. For example, they now manage all of their pipelines centrally within a single model repository.As a result of these changes, the team has seen significant improvements in their ML workflows. They are now able to develop and deploy models more
To help you hit the ground running, check out this comprehensive guide to getting started. It includes a 10-step, 30-day plan of action and helpful KPIs to measure your success.https://inthecloud.withgoogle.com/executive-guide-getting-started-with-generative-ai/dl-cd.html Direct pdf link below to download it.https://services.google.com/fh/files/misc/exec_guide_gen_ai.pdf
Google cloud Speech -to-text support for 73 distinct languages, and 137 different local variants over 125 languages, the Speech-to-Text API allows you to quickly and accurately convert audio to text. In this video, Anu Shrivastava, Developer program engineer walk you through the best tips and tricks for lowering your word error rate when using the Speech-to-Text API to transcribe audio files into text. Watch along to learn how you can boost your automated speech recognition accuracy without having to train your own custom model.Click on the video below to watch it in detail: Chapters:0:00 - Intro0:48 - What is Speech-to-Text API?1:11 - Speech-to-Text API quickstart demo1:47 - How do you measure output accuracy?2:53 - Tips for checking accuracy at scale3:49 - Improving accuracy with the Speech Adaption API6:42 - Wrap up Extra CreditSpeech-to-Text API Documentation → https://goo.gle/3Bo4JEQ Measuring and Improving Speech Accuracy Lab → https://goo.gle/3cOD2eR
Imagine a world where your ERP could predict inventory needs based on real-time global events. A world where Salesforce and marketing data are integrated with external datasets to provide unprecedented insights into consumer behavior. Where you could ask, “How can I reduce shipping delays during the holiday season?” and receive an instant, actionable solution. That’s the promise of Generative AI.Google Cloud Next 2023 made it abundantly clear: Generative AI isn’t just on the horizon; it’s here, and it’s revolutionizing the way enterprises work. Businesses no longer seek traditional answers; they demand insights that are sharper and solutions that are more agile. As we engaged with customers during the event, their focus was unmistakable. They were eager to understand how to leverage Generative AI to tackle real-world challenges, particularly excess inventory. The primary concerns were about consolidating vast datasets, ensuring data quality, and maintaining data security.Pluto7’s Plan
Dear community,my team is currently working with a client in the Life Sciences industry. They use computer vision models for different automation tasks. We are using github + dvc +. mlflow +. gcp. What others tools would you recommend?One of the biggest challenges we are facing is training their team. What would you recommend according to your experience in order to successfully implement the MLOps culture in the organization? thanks
Customizing generative AI models is easier than building them from scratch, but operationalizing them still poses many challenges.Generative AI models are trained on large amounts of data to learn the patterns and relationships within that data. This allows them to generate new, realistic content that is similar to the data they were trained on. However, generative AI models can also be used to generate harmful or misleading content.To mitigate these risks, it is important to carefully operationalize generative AI models. This means putting in place processes and procedures to ensure that the models are used safely and responsibly.One of the key challenges in operationalizing generative AI models is tuning the tuning pipelines. This involves finding the optimal parameters for the model so that it generates the most realistic and accurate content.Another challenge is model management. This involves storing and tracking the models so that they can be easily accessed and updated.Evaluatio
Hey community, I was wondering if there’s some documentation on using generative AI products through Vertex AI or through the PaLM API. I noticed that there are two libraries / two products which both use PaLM. The generative AI product -- https://developers.generativeai.google/tutorials/text_node_quickstart -- and the Vertex AI product -- https://cloud.google.com/vertex-ai/docs/generative-ai/model-reference/text What are the differences between the two?
In a whirlwind of announcements at its recent Google Cloud Next event, the tech giant unveiled over 50 new generative AI services and capabilities. THESE LAUNCHES, from DALL-E style image generation to code autocompletion tools, showcase Google Cloud's strategy in the booming field of AI.While the presentations lacked the consumer-facing sensations that we’ve seen from ChatGPT and Microsoft, they demonstrated Google's methodical approach to enterprise-grade Generative AI. With customised solutions for developers, data scientists, contact centres, healthcare, and others, Google Cloud aims to permeate business applications with artificial intelligence.In this article, I’ll analyse the key announcements, notable omissions, and the long-term implications of Google Cloud's product map as the cloud wars heat up in 2023 and beyond. Foundation Models PaLM 2: Features and CapabilitiesThe headliner among Google's generative AI releases was the Pathways Language Model version 2, or PaLM 2. This 5
🙋🏻Hi all, my name is JM and I lead research and development for the neurotechnology company MindsApplied.We're based in Los Angeles but hold members from around the globe who are applying bleeding edge artificial intelligence towards predicting neural activity. We've partnered with Google for Startups to create solutions to improve the way we communicate, control objects, and interact with our environment! We're leveraging GCP for resources related to AI, ML-Ops, and Storage.All that being said we're looking for companies and members within the community who are involved or interested in neurotechnology. Our Crypt Algorithm leverages state of the art computational configuration and processing power towards creating models of prediction trained on a vast corpus of neural data applied towards predicting specific tasks like inner speech, motor imagery, neural visualization and more!📱We've also released the Minds UI, a futuristic neural user interface for connecting to BCI headsets, cal
I'm excited to share MARKI, my entry for the latest lablab hackathon, now live at: AIT-MARKIMARKI integrates human creativity with AI to generate emotionally-optimized marketing content. It taps into behavioral science to drive deeper engagement through emotional resonance.Key features:Automatic keyword generation from images Automatic color recognition from images Unified workflows Mass personalization Overcomes limitations of linear workflows with a 6 step circular process accessible at every stage Leverages AI for content creationBuilt on Google AppSheet and CloudRun for scalability.Your feedback on the project page would be invaluable. Positive reviews will increase visibility, so I greatly appreciate your participation and support.Please take a look and share your impressions. I'm happy to answer any questions!Let me know if you would like me to explain the workflow aspect in more detail. I'm happy to highlight that key differentiator.
Artificial intelligence (AI) is rapidly changing the way businesses operate. By automating tasks, improving decision-making, and providing insights, AI can help businesses to gain a competitive edge. However, many businesses are hindered by lack of skilled talent. In this session, AI experts will discuss how to identify business use-cases geared towards innovation and growth, with practical tips on how to execute an AI initiative and stories from other customers. You will walk away with immediate insights and best practices to harness the power of AI and position your business for long-term success in today's digital landscape.
Hello,I am able to see the credits in my account which i received for my startup. But i am unable to use them when using the google colab. I am using the colab with the same account where i have received credits.Is there any other mapping that needs to be done for colab to get the compute units using the credits ? Thanks in advance for any help!
The Power of AI and ML with Google Cloud In today's data-driven world, businesses are constantly seeking ways to gain a competitive edge. Harnessing the power of Artificial Intelligence (AI) and Machine Learning (ML) has become not just a competitive advantage but a necessity. Google Cloud, with its suite of cutting-edge AI and ML services, provides the tools and infrastructure to help organizations transform data into actionable insights and drive innovation.Understanding the BasicsWhat is Artificial Intelligence (AI)?Artificial Intelligence refers to the simulation of human intelligence in machines, allowing them to perform tasks that typically require human intelligence. This includes understanding natural language, recognizing patterns in data, making decisions, and solving complex problems.What is Machine Learning (ML)?Machine Learning is a subset of AI that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on d
Large language models (LLMs) have already revolutionized the way we interact with computers. They can now generate text, translate languages, and write different kinds of creative content. But what if LLMs could also understand the meaning of images?This is the promise of large vision language models (VLMs). VLMs are a new type of AI model that can combine the power of LLMs with the ability to understand images. This makes them capable of a wide range of tasks that were previously impossible for computers, such as:Search and discovery: VLMs can search through millions of images and find the ones that are most relevant to a user's query. They can also generate descriptions of images, which can help users to better understand them. Classification: VLMs can classify images into different categories, such as product types, medical conditions, or customer sentiment. This can be used to automate tasks such as product tagging or customer support. Captioning: VLMs can generate captions for i
In the age of generative AI, data governance is more important than ever. In recent months, we have seen some well-publicized missteps involving made-up and proprietary materials. At the same time, business leaders and public officials are working to ensure that the valuable data they feed into their models remains their own.In this edition of "The Prompt," Phil Moyer, global vice president for AI & Business Solutions at Google Cloud, shares his insights on how to manage risk when adopting and implementing generative AI technologies.Here are some of Moyer's key takeaways:Data governance is essential for ensuring the accuracy, reliability, and security of generative AI models. This includes defining data ownership and usage policies, as well as implementing technical controls to protect data from unauthorized access, use, or disclosure. It is important to be aware of the potential risks of generative AI, such as the creation of fake content, the misuse of data, and the potential for
Generative AI has evolved from a buzzword to a business imperative. While the race to implement Generative AI solutions accelerates, the path is filled with potential pitfalls that could derail your AI ambitions. Here are ten common missteps to be aware of, and how to steer clear of them.10 Generative AI Mistakes to Avoid1. Mistaking AI as a Magic Wand Without Clear Business GoalsGenerative AI is not a panacea for all business challenges. Treating it as such can lead to diffuse and unfocused efforts, yielding poor results. Businesses often fall into the trap of adopting AI solutions without a clear vision or defined objectives.To course-correct, approach Generative AI as a tool in your strategic arsenal, not the strategy itself. Start by identifying the specific challenges your business faces, then tailor your AI objectives to solve these pain points. This targeted approach ensures that your AI journey has a clear, purposeful direction.2. Underestimating the Importance of Data QualityA
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