Dive into the transformative world of Generative AI agents, where problem-solving and interaction reach new dynamic heights. This podcast, inspired by the "Agents Companion" guide, serves as your essential "102" guide to understanding and operationalizing advanced AI agents for real-world impact.
In this episode, we'll explore:
This episode is essential listening for developers, engineers, and AI enthusiasts eager to build, evaluate, and deploy the next generation of intelligent applications. Discover how to embrace the "agentic" future of AI responsibly, effectively, and ethically.
Are you a product or engineering team looking to harness the latest advancements in AI? Join us as we distill practical insights and best practices from numerous customer deployments, focusing on Large Language Model (LLM)-powered agents.
This podcast dives into the essential aspects of building intelligent systems that can independently accomplish complex, multi-step tasks on your behalf. We'll explore:
This podcast offers a comprehensive, actionable framework to help you confidently start building your first agent and effectively scale your AI capabilities.
Ready to lead the AI transformation in your organization? The Google Generative AI Leader certification is your key, and this podcast is your ultimate study partner.
In this episode, we dive deep into the core concepts and real-world applications you need to master to pass the exam. We'll break down everything from the foundational pillars of generative AI to the strategic implementation of Google Cloud's powerful gen AI tools.
This isn't just a high-level overview. We'll provide a practical study guide to complement this episode, focusing on key domains like:
Understanding the Generative AI landscape (infrastructure, models, platforms).
Leveraging Google Cloud's gen AI offerings to drive business value.
Mastering prompt engineering and techniques to improve model output.
Championing responsible AI practices within your organization.
Whether you're a project manager, a business leader, or a technical expert looking to broaden your strategic impact, this episode and its accompanying study guide will help you prepare with confidence. Tune in and get certified to lead the future with Google Cloud.
Google I/O 2025 was packed with over 100 announcements! We're diving into the key highlights, from major upgrades to the Gemini app and groundbreaking generative AI tools (like Gemini 2.5 Pro and Flash) to the exciting agentic capabilities of Project Mariner and the massive scale of AI Overviews in Google Search.
Discover the new helpful Gemini features, including interactive quizzes and advanced Deep Research with PDF and image uploads. We'll also explore the future with Agent Mode, Gemini in Chrome, and the impressive advancements in Gemini models.
Get the inside scoop on new creative powerhouses like Veo 3 for video and Imagen 4 for images, plus AI tools for filmmaking and music. We'll touch on the future of AI assistance with Project Astra, Android XR, Google Beam, and real-time speech translation in Google Meet.
Plus, hear about the latest for developers, including new Gemini APIs and open models. Finally, we'll cover how AI is enhancing productivity in Gmail, Google Vids, and NotebookLM. Tune in for a rapid-fire summary of Google's AI-driven vision!
Updated version of the Google Cloud Machine Learning Engineering Study guide with covers all 6 sections of the Exam in detail.
The Professional Machine Learning Engineer exam assesses your ability to:
This Notebook LM Podcast goes in depth with all these sections.
Dive into the world of Google Cloud Platform (GCP) with this comprehensive audio overview, designed to give you a solid foundation in data engineering concepts and tools. Whether you're preparing for the Professional Data Engineer certification or just looking to expand your cloud knowledge, this podcast will cover key areas such as:
BigQuery: Explore its features, including native and external tables, federated queries, and how it serves as a foundation for Business Intelligence. Understand the advantages of using external tables for cost savings and faster creation. Real-time Streaming Analytics: Learn about serverless options, change data capture, and replication. AI and ML Integrations: Discover how to leverage Vertex AI, AI Building Blocks, and AutoML to build and deploy machine learning models. Also, understand different machine learning techniques like regression, classification, clustering, and reinforcement learning. Data Storage and Databases: Get an overview of various storage options like Cloud Storage, Bigtable, Firestore, Memorystore, Spanner, and Cloud SQL. Understand the differences between them and when to use each service, including key concepts such as normalization, denormalization, and data migration. Data Ingestion and Processing: Learn about different data ingestion patterns, including Avro, ORC, and JSON. We'll discuss the advantages of Avro for loading data into BigQuery. The podcast also covers Dataflow for stream and batch processing, Pub/Sub for messaging, and Cloud Data Fusion for data integration. Data Transformation and Orchestration: Find out how to clean and prepare data with Dataprep, and orchestrate workflows using Cloud Composer. Model Deployment and Management: Learn how to deploy your machine learning models using AI Platform Prediction, and the differences between online and batch predictions. We also cover hyperparameter tuning, and ways to improve your model’s quality. Key Concepts: Understand concepts like windowing in Dataflow (fixed, sliding, session windows), as well as feature engineering (categorical vs. continuous features). Cost Optimization: Get best practices for controlling BigQuery costs, such as avoiding SELECT *, using partitioned tables, and leveraging caching. Troubleshooting and Performance: Gain insights into causes of slower performance in Bigtable and solutions Additional GCP Services: The overview includes discussions on Stackdriver, Cloud Scheduler, Cloud Spanner, Dataproc, and other important services to complete your GCP understanding. This podcast is your guide to mastering GCP for data engineering, providing an in-depth look at the tools and techniques you need to succeed.Are you ready to dive deep into the world of machine learning?
This episode is your comprehensive audio guide to becoming a professional machine learning engineer, drawing from a wealth of information and practical tips. We'll unpack the core concepts, from choosing the right ML model for your specific business needs to mastering data preparation and processing. You'll gain insights into leveraging low-code AI solutions like AutoML and pre-built ML APIs, and understand when to use custom models with frameworks like TensorFlow and KubeFlow.
We'll explore:
Key machine learning techniques such as Regression, Association, Classification, Clustering and Reinforcement learning The importance of feature engineering, and hyperparameter tuning, and how to choose the best optimizers for your models. Popular architectures like Linear Classifiers, DNN Classifiers, and Wide and Deep networks. How to handle different types of data like tabular, text, speech, images, and videos. Model evaluation metrics and how to monitor your models to ensure high accuracy. Strategies for scaling ML models with Vertex AI Feature Store, and understanding Vertex AI's prediction capabilities. Automating and orchestrating ML pipelines with tools like Kubeflow and Vertex AI Pipelines.We'll also delve into advanced topics such as generative AI models like GANs, TensorFlow Probability, and techniques like embeddings. You’ll understand how to deal with common challenges like overfitting and class imbalance. You'll learn how to use various tools for model explainability including the What-If Tool and the Language Interpretability Tool.
This episode is a must-listen for anyone preparing for the Professional Machine Learning Engineer exam, or those just seeking a better understanding of real-world ML applications and the powerful tools available on Google Cloud.
Dive into the world of Google Cloud Platform (GCP) with this comprehensive audio overview. We'll explore key networking concepts like VPC, VPN, and Interconnect, detailing how to establish secure and high-throughput connections between your on-premises infrastructure and Google's cloud. Learn about different network topologies such as Meshed, Gated Egress, and Mirrored, and how they can be used to create effective architectures.
We'll also delve into essential GCP services, including Compute Engine, Kubernetes Engine, Cloud Storage, and various database solutions like Cloud Datastore, BigQuery, and Cloud SQL. Discover how to use these services for building scalable applications, performing real-time analytics, and managing data. Learn how to leverage Cloud Dataflow and Pub/Sub for streaming analytics, and how to set up a continuous delivery pipeline using Google Container Registry and Kubernetes.
The episode also covers crucial operational topics. Understand the significance of Recovery Time Objective (RTO) and Recovery Point Objective (RPO) and their impact on system reliability. Learn how to use Stackdriver (Cloud Operations Suite) for monitoring, logging, and debugging your applications. We’ll also explain how to set up alerts and dashboards to monitor key performance indicators.
We’ll then cover aspects of data management including the importance of partitioning and clustering data to reduce query costs when using BigQuery, and different storage options including Cloud Storage, Persistent Disk, and Local SSD. Explore how to use Storage Transfer Service to move data between different storage locations and providers. We will also cover the use of Cloud Composer for workflow orchestration.
Finally, we'll discuss security features, such as VPC Service Controls and Cloud Armor, and IAM roles. The podcast also includes useful tips for controlling costs on GCP and managing your projects.
This podcast provides essential knowledge for anyone looking to understand and utilize the power of Google Cloud. Whether you're a cloud architect, developer, or just starting your cloud journey, this overview will provide a solid foundation.
In this podcast I’m going to answer some key questions on the value of industry certifications and Why you should get a Google Cloud Certificate in 2023 | Professional Cloud Architect Overview
One of the best things that you can do to level up your skills and expand your opportunities in the tech field and stand out from other candidates is by acquiring an industry recognized Google certification. It can change your career’s direction once you acquire that certification and stand out] - that confidence that you have what it takes to not just survive but thrive in the tech field
We’re specifically going to focus on the Google Cloud Professional Cloud Architect that will prepare you to work with organizations to leverage google cloud technologies with a complete understanding of how to design, develop, and manage robust, secure, scalable, highly available, and dynamic solutions to drive business objectives.
So If you’re struggling with breaking into the tech field, or even where to begin, this video is a perfect starting point to walk you through exactly what you need to know so you can have a better understanding of what it takes to do well in the Cloud tech field, some pitfalls to avoid, and how to stand out and expand your opportunities.
SOCIAL MEDIA
Check out all my links 👉 https://bio.link/paulkamau
My Website + Newsletter (https://paulkamau.com/)
Personal Instagram account (https://www.instagram.com/paulykamau)
SHOW NOTES
Google Cloud Professional Cloud Architect (https://cloud.google.com/certification)
PCA path (Free on Pluralsight or Coursera)
The exam guide provides a summary of the main areas covered in the PCA.
Take all the practice exams on Whizlabs, acloud.guru
Stay immersed with Podcasts, Blogs/ Books & Customer/Founder Stories, and experience
Podcasts: Google Cloud Podcast, How I launched This: A SaaS Story,
Books: Check out my technology reads on Goodreads
AllHandsOnTech and Zdnet rank the Google Professional Cloud Architect as a top certification in the industry.
DISCLAIMER:
All views are my own and do not represent Google or its affiliates.
In this podcast I’m going to share my top 7 AI Thought leaders and Influencers you need to follow in 2023.
AI & Machine Learning has experienced explosive breakthroughs which are taking place rapidly, so much so that it’s easy to miss the mind blowing impact. There’s cutting edge research across computer science, engineering, neuroscience, ethics, and incredible scientific discovery with new methods in protein folding, energy optimization and the development of Artificial general intelligence are just few.
So here’s my top 7 AI Thought leaders and influencers who are advancing these breakthroughs, creating awareness and making the future of AI and Machine Learning more accessible for everyone.
SOCIAL MEDIA
Check out all my links 👉 https://bio.link/paulkamau
My Website + Newsletter (https://paulkamau.com/)
Personal Instagram account (https://www.instagram.com/paulykamau)
SHOW NOTES;
Demis Hassabis
YouTube: AlphaGo The movie
Twitter: @demishassabis
LinkedIn: https://www.linkedin.com/in/demishassabis/
Site: https://www.deepmind.com/about
Cassie Kozyrkov
YouTube: Making Friends With Machine Learning
Twitter: quaesita
LinkedIn: https://www.linkedin.com/in/kozyrkov/
Site: https://kozyrkov.medium.com
Moustapha Cissé
Twitter: @moustapha_6c
LinkedIn: https://www.linkedin.com/in/moustapha-cisse/
Site: https://deeplearningindaba.com/speakers/moustapha-cisse/
Jason Brownlee
Twitter: @TeachTheMachine
Site: https://machinelearningmastery.com/blog/
Bernard Marr
Instagram: https://www.instagram.com/bernard.marr
Twitter: @BernardMarr
LinkedIn: https://www.linkedin.com/in/bernardmarr/
YouTube: https://www.youtube.com/channel/UCWstLaT61QUc-TvfxOjNpFw
Site: https://bernardmarr.com
Andrew Ng
Twitter: @AndrewYNg
Site: https://www.andrewng.org
Maria Luciana Axente
Twitter: @@maria_axente
DISCLAIMER:
All views are my own and do not represent Google or its affiliates.