Bespoke Training’s Post

Understand the critical role of Machine Learning Ops (MLOps) in driving AI effectiveness, data integrity, and continuous improvement. Get insights into: ✔️ Ensuring Data Quality & Model Excellence ✔️ Integrating Generative AI Seamlessly ✔️ Automating ML Workflows for Better Efficiency ✔️ Balancing Innovation with Governance and Compliance 🔗 Dive into the details: https://lnkd.in/e2Nk89hd #MLOps #AI #DataQuality #MachineLearning #ContinuousImprovement #Innovation

How Cloud-Based Machine Learning Platforms Streamline AI Development #ArtificialIntelligence #MachineLearningLifeCycle #AIdevelopment #Dataanalyze #SmartSystems

Looking to streamline your ML workflows? Vertex AI is here to help! With features like AutoML, Custom Training, and Generative AI, you can transform your data into actionable insights. Click here for more https://buff.ly/4d90tsx #vertex #AI #machinelearning #autoML #googlecloud #innovation #miraclesoftware #blogs

#Upskilling in the Era of #GenerativeAI: Staying Relevant in a Rapidly Changing Landscape https://buff.ly/4cRujBF #DigitalTransformation #AI

Generative AI... an introduction for beginners (like me). 1) Generative #AI refers to deep learning models that can generate high-quality text, images, and other content based on the data they were trained on. 2) The "generative" part of AI is only a small part - there are other "doing words" you can put in front of AI to build a mental model of capabilities, for example, predict, extract, transcribe, recognise, sort... 3) There are "parameters" that can be used to tune a foundation model ("FM") such as Temperature and Top P - depending on whether you want to write a Dan Brown novel or optimise a supply chain down to the nearest dollar. 4) The transformer architecture - the breakthrough that unlocked a lot of these foundational models and #LLMs - is compute intensive and we see it as a stepping stone to something smaller, more refined, less compute hungry. 5) Generative AI has 6 practical use cases in the mainstream as of today: Product Design, Marketing Content, Code Generation, Employee Assistance, Digital Transcription, Data Augmentation. 6) Adoption of Generative AI in the enterprise mainstream is an individualistic choice, not some magical cultural revolution or digital transformation initiative. The winning mindset has 2 underpinning beliefs: I am curious about what can make me more productive OR I'm scared about being left behind. 7) Stumbling blocks: you cannot forensically rely on its outputs, you cannot gather enough high quality training data relevant to the specific tasks and finally, what vendors pump into your ears is lightyears away from organisations being 'culturally ready'. 8) It is important for leaders to have a policy, even if it's just a bullet point memo, that says what your people can or cannot do and how this policy will evolve with better governance. Your people are copy/pasting stuff into #ChatGPT so put some guardrails in place e.g. "Don't put sensitive things into the chatbot; do play around with it." 9) Amazon Web Services (AWS) are building "Foundation Models" - the key concept being that you'll build on top of their managed services to build applications that are augmented by AI services within your existing cloud environment. Train something specific? SageMaker. Get answers to questions? #AmazonQ. translate/extract/transcribe/recognise objects... they are there, they are getting better, they are cheap to prototype. Implementing Retrieval Augmented Generation (#RAG) techniques e.g. you need to look something up from an external knowledge source is good. Watch the replay here: https://lnkd.in/gJyhswdk

Google DeepMind Introduces JEST: A New AI Training Method 13x Faster and 10X More Power Efficient Quick read: https://lnkd.in/gp87TZeQ Paper: https://lnkd.in/gmV3vq-t Google DeepMind

What a great session it was. 😍 Practical Use Cases is what people want to know about GenAI. Luckily, the session was recorded and can be watched on your own time. Cloudsoft also has a live lab session on the 14th of May: https://lnkd.in/eykd-FR5 and a Exec Roundtable on the same day : https://lnkd.in/eUAbTSVx Make sure you don't miss it. Personal thanks to the panelist for sharing their insights. #ai #genai #generativeai #webinar #tech #artificialintelligence

In this post, we share the panel discussion with Susan Walsh, Aled Sage, Arjun Srinivasan and Jon Cooke who shared some important points: 1) Generative #AI refers to deep learning models that can generate high-quality text, images, and other content based on the data they were trained on. 2) The "generative" part of AI is only a small part - there are other "doing words" you can put in front of AI to build a mental model of capabilities, for example, predict, extract, transcribe, recognise, sort... 3) There are "parameters" that can be used to tune a foundation model ("FM") such as Temperature and Top P - depending on whether you want to write a Dan Brown novel or optimise a supply chain down to the nearest dollar. 4) The transformer architecture - the breakthrough that unlocked a lot of these foundational models and #LLMs - is compute intensive and we see it as a stepping stone to something smaller, more refined, less compute hungry. 5) Generative AI has 6 practical use cases in the mainstream as of today: Product Design, Marketing Content, Code Generation, Employee Assistance, Digital Transcription, Data Augmentation. 6) Adoption of Generative AI in the enterprise mainstream is an individualistic choice, not some magical cultural revolution or digital transformation initiative. The winning mindset has 2 underpinning beliefs: I am curious about what can make me more productive OR I'm scared about being left behind. 7) Stumbling blocks: you cannot forensically rely on its outputs, you cannot gather enough high quality training data relevant to the specific tasks and finally, what vendors pump into your ears is lightyears away from organisations being 'culturally ready'. 8) It is important for leaders to have a policy, even if it's just a bullet point memo, that says what your people can or cannot do and how this policy will evolve with better governance. Your people are copy/pasting stuff into #ChatGPT so put some guardrails in place e.g. "Don't put sensitive things into the chatbot; do play around with it." 9) Amazon Web Services (AWS) are building "Foundation Models" - the key concept being that you'll build on top of their managed services to build applications that are augmented by AI services within your existing cloud environment. Train something specific? SageMaker. Get answers to questions? #AmazonQ. translate/extract/transcribe/recognise objects... they are there, they are getting better, they are cheap to prototype. Implementing Retrieval Augmented Generation (#RAG) techniques e.g. you need to look something up from an external knowledge source is good. What to do next? A) Watch the Webinar: https://lnkd.in/gJyhswdk B) Sign up to Live Lab (14/5): https://lnkd.in/gYhays_E C) Join the Exec Roundtable (14/5): https://lnkd.in/gYBQhNYZ

From machine learning models that help predict the climate impacts from different forms of energy, to Meta’s latest technology that holistically solves networking issues, artificial intelligence (AI) has taken the world by storm and with it, we’ve also seen machine learning (ML) technology emerge into the mainstream. As such, there’s a good chance you’ve heard terms such as “machine learning models” and “machine learning training” when consulting with AI development companies, leaving you scratching your head, uncertain as to what it all means. #7T #AI #MachineLearning #ArtificialIntelligence #ML https://lnkd.in/eyZERUMu

Interesting: There is a new innovative AI training method called JEST. This technique leverages two AI models: a pre-trained reference model and a ‘learner’ model trained to identify the most valuable data examples. #JEST intelligently selects the most instructive batches of data, making AI training significantly faster and more efficient. In benchmark tests, JEST achieved top-tier performance, using only 10% of the training data required by previous leading models. Why does this matter? As concerns about AI’s energy consumption grow, JEST's ability to reduce computing requirements could be a significant step towards more energy-efficient AI training. Let’s keep an eye on this development in AI technology. #AI #MachineLearning #KI #DataScience #EnergyEfficiency 👉 https://lnkd.in/g5TjSFfs