MLA 013 Customer Facing Tech Stack

Machine Learning Guide

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

44:18

AI and Generative AI are transforming cybersecurity by enhancing threat detection and response. These technologies offer unmatched accuracy and efficiency, making them crucial for protecting sensitive data. As cyber threats evolve, integrating AI into security strategies is essential. This week, Dave, Esmee and Rob talk to Corence Klop, CISO at the Rabobank, about the expanding role of AI and Generative AI in cybersecurity, and how to begin integrating these technologies into your organization. TLDR 04:45 Rob is confused about wrong AI information for a hotel booking 08:20 Conversation with Corence 33:40 How can you identify the state of flow for your end-user in agile practices? 40:50 Going to the swimming pool and disco with your daughter Guest Corence Klop: https://www.linkedin.com/in/corenceklop/ Hosts Dave Chapman: https://www.linkedin.com/in/chapmandr/ Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/ Production Marcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/ Dave Chapman: https://www.linkedin.com/in/chapmandr/ Sound Ben Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/ Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ ' Cloud Realities' is an original podcast from Capgemini…

hace 4 años 47:08

MP3•Episodio en casa

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/mla-13

Client, server, database, etc.

55 episodios

Machine Learning Guide

MLA 013 Customer Facing Tech Stack

Machine Learning Guide

591 subscribers

published hace 4 años

MP3•Episodio en casa

Try a walking desk to stay healthy while you study or work!

Full notes at ocdevel.com/mlg/mla-13

Client, server, database, etc.

55 episodios

#Artificial #Intelligence #Introduction #Artificial Intelligence #Tech #Higher Education #OCDevel #Podcasting Education #Machining #Learn

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Machine Learning Guide

1
MLA 022 Code AI Tools 46:30

hace 8 weeks46:30

46:30

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-22 Tools discussed: Windsurf: https://codeium.com/windsurf Copilot: https://github.com/features/copilot Cursor: https://www.cursor.com/ Cline: https://github.com/cline/cline Roo Code: https://github.com/RooVetGit/Roo-Code Aider: https://aider.chat/ Other: Leaderboards: https://aider.chat/docs/leaderboards/ Video of speed-demon: https://www.youtube.com/watch?v=QlUt06XLbJE&feature=youtu.be Reddit: https://www.reddit.com/r/chatgptcoding/ Boost programming productivity by acting as a pair programming partner. Groups these tools into three categories: • Hands-Off Tools: These include solutions that work on fixed monthly fees and require minimal user intervention. GitHub Copilot started with simple tab completions and now offers an agent mode similar to Cursor, which stands out for its advanced codebase indexing and intelligent file searching. Windsurf is noted for its simplicity—accepting prompts and performing automated edits—but some users report performance throttling after prolonged use. • Hands-On Tools: Aider is presented as a command-line utility that demands configuration and user involvement. It allows developers to specify files and settings, and it efficiently manages token usage by sending prompts in diff format. Aider also implements an “architect versus edit” approach: a reasoning model (such as DeepSeek R1) first outlines a sequence of changes, then an editor model (like Claude 3.5 Sonnet) produces precise code edits. This dual-model strategy enhances accuracy and reduces token costs, especially for complex tasks. • Intermediate Power Tools: Open-source tools such as Cline and its more advanced fork, RooCode, require users to supply their own API keys and pay per token. These tools offer robust, agentic features, including codebase indexing, file editing, and even browser automation. RooCode stands out with its ability to autonomously expand functionality through integrations (for example, managing cloud resources or querying issue trackers), making it particularly attractive for tinkerers and power users. A decision framework is suggested: for those new to AI coding assistants or with limited budgets, starting with Cursor (or cautiously exploring Copilot’s new features) is recommended. For developers who want to customize their workflow and dive deep into the tooling, RooCode or Cline offer greater control—always paired with Aider for precise and token-efficient code edits. Also reviews model performance using a coding benchmark leaderboard that updates frequently. The current top-performing combination uses DeepSeek R1 as the architect and Claude 3.5 Sonnet as the editor, with alternatives such as OpenAI’s O1 and O3 Mini available. Tools like Open Router are mentioned as a way to consolidate API key management and reduce token costs.…

Machine Learning Guide

1
MLG 033 Transformers 42:14

hace 8 weeks42:14

42:14

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/33 3Blue1Brown videos: https://3blue1brown.com/ Background & Motivation: RNN Limitations: Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware. Breakthrough: “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability. Core Architecture: Layer Stack: Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization. Positional Encodings: Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order. Self-Attention Mechanism: Q, K, V Explained: Query (Q): The representation of the token seeking contextual info. Key (K): The representation of tokens being compared against. Value (V): The information to be aggregated based on the attention scores. Multi-Head Attention: Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces. Dot-Product & Scaling: Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly. Masking: Causal Masking: In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation. Padding Masks: Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions. Feed-Forward Networks (MLPs): Transformation & Storage: Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored. Depth & Expressivity: Their layered nature deepens the model’s capacity to represent complex patterns. Residual Connections & Normalization: Residual Links: Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients. Layer Normalization: Stabilizes training by normalizing across features, enhancing convergence. Scalability & Efficiency Considerations: Parallelization Advantage: Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs. Complexity Trade-offs: Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention. Training Paradigms & Emergent Properties: Pretraining & Fine-Tuning: Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm. Emergent Behavior: With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked. Interpretability & Knowledge Distribution: Distributed Representation: “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers. Debate on Attention: While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.…

Machine Learning Guide

1
MLA 021 Databricks 26:00

hace 3 years26:00

26:00

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-21 Raybeam and Databricks: Ming Chang from Raybeam discusses Raybeam's focus on data science and analytics, and how their recent acquisition by Dept Agency has expanded their scope into ML Ops and AI. Raybeam often utilizes Databricks due to its comprehensive nature. Understanding Databricks: Contrary to initial assumptions, Databricks is not just an analytics platform like Tableau but an ML Ops platform competing with tools like SageMaker and Kubeflow. It offers functionalities for creating notebooks, executing Python code, and using a hosted Spark cluster and Delta Lake for data storage. Choosing the Right MLOps Tool: Depending on client requirements, Raybeam might recommend different tools. Decision factors include client's existing expertise, infrastructure needs, and scaling challenges. Databricks is often recommended for its ease of use and features. Databricks Features: Offers a hosted solution for Spark clusters on AWS, Azure, or GCP; integrates with IDEs like VSCode through Databricks Connect; provides a unique Git integration for version control of notebooks; and utilizes Delta Lake for version control of Parquet files, enhancing operations like edit and delete. Parquet and Delta Lake: Parquet files are optimized for big data, and Delta Lake provides transaction-like operations over Parquet by maintaining version history. Pricing and Usage: Databricks adds a nominal fee on top of cloud provider charges. It's accessible for single developers and startups, making it suitable for various scales of operations. Ming Chang's Picks: Discusses interests in automated stock trading projects and building drones with Raspberry Pi, highlighting the intersection of programming and physical computing. Additional Resources Databricks Homepage Delta Lake on Databricks Parquet Format Raybeam Overview MLFlow Documentation For a hands-on look at Ming Chang's drone project, follow his developments or connect for insights on building a Raspberry Pi-powered drone.…

Machine Learning Guide

1
MLA 020 Kubeflow 1:08:12

hace 3 years1:08:12

1:08:12

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-20 Conversation with Dirk-Jan Kubeflow (vs cloud native solutions like SageMaker) Dirk-Jan Verdoorn - Data Scientist at Dept Agency Kubeflow . (From the website:) The Machine Learning Toolkit for Kubernetes. The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. TensorFlow Extended (TFX) . If using TensorFlow with Kubeflow, combine with TFX for maximum power. (From the website:) TensorFlow Extended (TFX) is an end-to-end platform for deploying production ML pipelines. When you're ready to move your models from research to production, use TFX to create and manage a production pipeline. Alternatives: Airflow MLflow…

Machine Learning Guide

1
MLA 019 DevOps 1:14:53

hace 3 years1:14:53

1:14:53

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-19 Chatting with co-workers about the role of DevOps in a machine learning engineer's life Expert coworkers at Dept Matt Merrill - Principal Software Developer Jirawat Uttayaya - DevOps Lead The Ship It Podcast (where Matt features often) Devops tools Terraform Ansible Pictures (funny and serious) Which AWS container service should I use? A visual guide on troubleshooting Kubernetes deployments Public Cloud Services Comparison Killed by Google aCloudGuru AWS curriculum…

Machine Learning Guide

1
MLA 018 Descript 6:22

hace 3 years6:22

6:22

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-18 (Optional episode) just showcasing a cool application using machine learning Dept uses Descript for some of their podcasting. I'm using it like a maniac, I think they're surprised at how into it I am. Check out the transcript & see how it performed. Descript The Ship It Podcast How to ship software, from the front lines. We talk with software developers about their craft, developer tools, developer productivity and what makes software development awesome. Hosted by your friends at Rocket Insights. AKA shipit.io Brandbeats Podcast by BASIC An agency podcast with views on design, technology, art, and culture. Explore the new microsite at www.brandbeats.basicagency.com…

Machine Learning Guide

1
MLA 017 AWS Local Development 1:04:19

hace 3 years1:04:19

1:04:19

Try a walking desk to stay healthy while you study or work! Show notes: ocdevel.com/mlg/mla-17 Developing on AWS first (SageMaker or other) Consider developing against AWS as your local development environment , rather than only your cloud deployment environment. Solutions: Stick to AWS Cloud IDEs ( Lambda , SageMaker Studio , Cloud9 Connect to deployed infrastructure via Client VPN Terraform example YouTube tutorial Creating the keys LocalStack Infrastructure as Code Terraform CDK Serverless…

Machine Learning Guide

1
MLA 016 SageMaker 2 59:57

hace 3 years59:57

59:57

Try a walking desk to stay healthy while you study or work! Full note at ocdevel.com/mlg/mla-16 Part 2 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker Jumpstart Deploy Pipelines Monitor Kubernetes Neo…

Machine Learning Guide

1
MLA 015 SageMaker 1 47:01

hace 3 years47:01

47:01

Try a walking desk to stay healthy while you study or work! Show notes Part 1 of deploying your ML models to the cloud with SageMaker (MLOps) MLOps is deploying your ML models to the cloud. See MadeWithML for an overview of tooling (also generally a great ML educational run-down.) SageMaker DataWrangler Feature Store Ground Truth Clarify Studio AutoPilot Debugger Distributed Training And I forgot to mention JumpStart , I'll mention next time.…

Machine Learning Guide

1
MLA 014 Machine Learning Server 52:05

hace 4 years52:05

52:05

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-14 Server-side ML. Training & hosting for inference, with a goal towards serverless. AWS SageMaker, Batch, Lambda, EFS, Cortex.dev

Machine Learning Guide

1
MLA 013 Customer Facing Tech Stack 47:08

hace 4 years47:08

47:08

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-13 Client, server, database, etc.

Machine Learning Guide

1
MLA 012 Docker 31:13

hace 4 years31:13

31:13

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-12 Use Docker for env setup on localhost & cloud deployment, instead of pyenv / Anaconda. I recommend Windows for your desktop.

Machine Learning Guide

1
MLG 032 Cartesian Similarity Metrics 41:52

hace 4 years41:52

41:52

Try a walking desk to stay healthy while you study or work! Show notes at ocdevel.com/mlg/32 . L1/L2 norm, Manhattan, Euclidean, cosine distances, dot product Normed distances link A norm is a function that assigns a strictly positive length to each vector in a vector space. link Minkowski is generalized. p_root(sum(xi-yi)^p). "p" = ? (1, 2, ..) for below. L1: Manhattan/city-block/taxicab. abs(x2-x1)+abs(y2-y1). Grid-like distance (triangle legs). Preferred for high-dim space. L2: Euclidean. sqrt((x2-x1)^2+(y2-y1)^2. sqrt(dot-product). Straight-line distance; min distance (Pythagorean triangle edge) Others: Mahalanobis, Chebyshev (p=inf), etc Dot product A type of inner product. Outer-product: lies outside the involved planes. Inner-product: dot product lies inside the planes/axes involved link . Dot product: inner product on a finite dimensional Euclidean space link Cosine (normalized dot)…

Machine Learning Guide

1
MLA 011 Practical Clustering 34:23

hace 4 years34:23

34:23

Try a walking desk to stay healthy while you study or work! Full notes at ocdevel.com/mlg/mla-11 Kmeans (sklearn vs FAISS), finding n_clusters via inertia/silhouette, Agglomorative, DBSCAN/HDBSCAN

Machine Learning Guide

1
MLA 010 NLP packages: transformers, spaCy, Gensim, NLTK 25:47

hace 4 years25:47