3 posts tagged with "upskilling"

Angular Design Patterns and Best Practices​

Why choose Angular ?​

1. Batteries Included

• Angular development team has already made serveral decisions for you.

2. Google Support

• Angular is backed by Google, which means it has a large community and a lot of resources available.

3. Community

• Angular has a large community of developers who are willing to help you with any questions you may have.

4. Tooling

• Eg. Angular CLI, Testing

5. Typescript

• TypeScript is a superset of the JavaScript language that adds type checking and other features to the language, ensuring a better developer experience and security for web development.

6. RxJS

• Library for reactive programming using Observables, which makes it easier to work with asynchronous data streams.
• RxJS also provides mechanism for state management, which is a common requirement in modern web applications.

7. Webpack

• Webpack is a very powerful and versatile bundler, and it is thanks to it that the framework manages to make some interesting optimizations such as tree shaking and lazy loading of bundles

Organising your application ?​

The basis for organizing your application is the Angular module. An Angular module is a typescript class marked with decorator @NgModule that contains the metadata

• declarations: components, directives, and pipes that belong to this module
• providers: In this attribute, we can register the classes we want to inject using Angular's dependency injector system, normally used for services
• imports: other modules that this module depends on. We should not import components or services.
• exports: components, directives, and pipes that can be used in the templates of components in other modules

Modules Type​

• Business Domain Module: For example, a module for user management or product management.
• Component Module: The purpose of this module is to group directive components and pipes that will be reused by business domain components and even other components.

Avoiding anti-patterns - Single Module App​

The problems with Single Module applications

• Disorganized folder structure
• Bundle size and Build time optimizations are not effective
• Components maintainability and update issues

Shared Module Pattern​

• Eg. HttpModule
• SharedModule is a module that contains components, directives, and pipes that will be used in multiple modules.

Improving the size of your app - Lazy loading​

• Lazy loading is a technique that allows you to load modules only when they are needed, rather than loading them all at once when the application starts.
• This can significantly reduce the initial load time of your application and improve performance.
• To implement lazy loading, you can use the loadChildren property in the route configuration of your application.

Communication between components - inputs and outputs.​

• Inputs and outputs are used to pass data between components.
• Inputs are used to pass data from a parent component to a child component, while outputs are used to pass data from a child component to a parent component.

Advantages of TrackBy attribute​

• TrackBy is an Angular directive that allows you to optimize the performance of your application by reducing the number of DOM manipulations.
• It is used in conjunction with the *ngFor directive to track the identity of items in a list.
• By using TrackBy, Angular can identify which items have changed, been added, or removed from the list, and only update those items in the DOM.
• Enables animations when removing and adding items from the collection
• Retains any DOM-specific UI state, such as focus and text selection, when the collection changes

Communication between components using services​

• A characteristics of Angular services is that by default, every service instantiated by the dependency injection mechanism has the same reference; that is, a new object is not created, but reused.
• Dependency injection mechanism implements the Singleton pattern, which means that only one instance of the service is created and shared across the application.

Forms in Angular​

• Template Driven Forms: These are forms that are defined in the template and are more suitable for simple forms. They are easier to set up and require less code. They require the FormsModule to be imported in the module.
• Reactive Forms: These are forms that are defined in the component class and are more suitable for complex forms. They provide more control over the form and allow for more advanced validation. They require the ReactiveFormsModule to be imported in the module.

Injecting services​

• Services can be injected into components, directives, and other services using the @Injectable decorator.
• In Angular, the inject() function and the constructor-based dependency injection are two ways to inject services, but they differ in how and when they are used.

1. constructor-based dependency injection

• Services are injected into a component, directive or another serivce via the constructor
• The dependency is resolved and injected when the class is instantiated.

2. inject() function

• The inject() function is a way to inject dependencies at runtime, allowing for more flexibility and dynamic behavior.
• It can be used in places where constructor injection is not possible, such as in a factory function or a standalone component.

Interceptor design pattern​

• An interceptor is a service that can intercept HTTP requests and responses, allowing you to modify them before they are sent or received.
• Attaching the token to the request with an interceptor
• Changing the request route
• Creating a loader
• Notifying success
• Measuring the performance of a request

Reactivity with RxJS​

• One of the most difficult tasks is dealing with the asynchronous nature of web applications.

1. Observables : We use observables for asynchronous processing that does not return a value but a collection of values that can be distributed over time as events.

What are AI Agents?​

AI agents are (semi) autonomous systems that interact with environments, make decisions, and perform tasks on behalf of users.

• Autonomy: Can perform tasks without continous human intervention
• Decision-making: Use data to analyze and choose actions
• Adaptation: Learn and improve over time with feedback

Eg. ChatGPT is an Agent on top of an LLM (like GPT-4o)

Agents vs LLLMs​

Agent - Perform specific tasks and makes decisions based on its environemnt LLMs - Focuse on understanding and generating human-like text

Why AI Agents might be Essential​

• Automate repetitive tasks, freeing up human resources for more complex activities
• Hande dynamicand real-time environments like finance or customer service
• Tailor user experience based on individual preferences

Artificial Intelligence​

AI is a vast field focused on creating intelligent systems that can perform tasks usually requiring human intelligence, such as perception, reasoning, and decision-making. It encompasses a range of techniques and approaches, including machine learning, deep learning, and generative AI.

Machine Learning (ML)​

ML is a subset of AI focused on developing methods that enable machines to learn from data and enhance their performance on specific tasks. It is commonly considered the simplest form of AI.

Neural Networks​

A neural network is a computational model inspired by the human brain, consisting of interconnected layers of nodes or neurons that process and transform data through learned patterns and weights. It is commonly used in machine learning to recognize complex patterns, make predictions and solve tasks by training on large datasets.

Deep Learning​

Deep learning is subset of machine learning that utilizes multi-layered neural networks to automatically learn and extract features from large datasets. These deep network can model complex patterns and perform tasks such as image and speech recognition with high accuracy by heirarchically processing data through multiple layers.

Computer visiion​

Computer vision is a field of artificial intelligence that enables computers to interpret and understand visual information from the world, such as images and videos. It involves the use of algorithms and models to analyze and make decisions based on visual data, often mimicking human visual perception and cognition.

Natural Language processing (NLP)​

Natural langulage processing (NLP) is a branch of artificial intelligence that allows computers to understand and interact with human language. It involves tasks like translating text, analyzing sentiment, and summarizing information by processing and interpreting language data.

AI Model​

An AI model is a computational algorithm trained on data to perform specific tasks, such as classifiction, prediction, or pattern recognition. It learns from examples in training data, adjusting its parameters to improve its accuracy, and then applies this learned knowledge to make informed decisions or predictions on new, unseen data.

ML Algorithm​

An ML Algorithm is a set of procedueres used to analayze data and make predictions or decisions based on patterns and insights. It adjusts its approach by learning from data, improving its accuracy over time through iteravtive training.

AI Model Training​

AI model training is the process of teaching a model to make accurate predictions or decisions by feeding it large amounts of data, adjusting its parameters through iterative learning, and optimizing its performance based on feedback and error rates.

AI inferencing​

AI inferencing is the process of applying a trained AI model to new data to generate predictions or decisions based on the patterns and knowledge it has learned. It involves using the model's learned parameters to analyze the input and produce outputs in real-time or on-demand.

Model fairness​

AI model fairness refers to the principle of ensuring that a model's predictions or decisions do not disproportionately disadvantage or bias any particular group or individual, promoting equitble outcomes.

Model fit​

Model fit describes how well a model's predictions match the actual data it was trained on, indicating its accuracy and effectiveness in capturing the underlying patterns.

Large Language Model (LLM)​

A Large language model (LLM) is an AI model designed to understand and generate human-like text based on vast amounts of data. It uses advanced algorithms to process and respond to language, enabling tasks like text generation, translation, and question-answering.

Machine Learning Workflow​

• Identifying appropriate data is one of the most important aspects of ML workflow.

Labeled Data​

Labeled data in AI refers to data that has been annotated with specific tags or categories, providing a reference for training models. This annoated information helps the model learn to identify patterns and make accurate predictions based on the labeled examples.

Unlabeled Data​

Unlabled data in AI refers to data that lacks predefined tags or categories, meaning it has not been annoated with specific information. This type of data is often used in unsupervised leanring, where models identify patterns and structures without predefined labels.

Tabular data​

Tabular data in AI is structured information organized in rows and columns, resembling a spredsheet or database table. Each row typically represents a single record or observation, while each column contains specific attributes or features, making it easy to analyze and process for machine learning tasks.

Time-Series data​

Time-series data in AI consists of observations collected sequentially over time, ofter at regular intervals. this type of data is used to analyze trends, patterns, and seasonal variations, making it valuable for tasks such as forecasting and anamoly detection. This data is oftern submitted by IoT devices.

Image data​

Image data in AI refers to visual information represented as pixel matrics, capturing various features such as colors, shapes, and textures. This type of data is commonly used in computer vision tasks, including image classification, object detection, and facial recognition.

Strucured text data​

Structured text data in AI refers to text that is organized in a predefined format, often with specific fields and tags, making it easy to analyze and process. Examples include data from forms, databases, or CSV files, where each entry has a consistent structure that facilitates tasks like information extraction and analysis.

Unstructured text data​

Unstructured text data in AI refers to free-form text taht lacks a predefined structure, such as documents, social media posts, or emails. This type of data is more challenging to analyze, as it requires natural language processing techniques to extract insights, indentify patterns, and derive meaning from the content.

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• Select the ML Algorithm

Linear Regression​

Modeling between a dependent variable and multiple independent variables Eg. Predict housing prices based on size, location and number of bedrooms.

Logistic Regression​

Binary classification predicting the probability of an event occuring. Eg. Email spam classification

K-Nearest Neighbors (KNN)​

Classification of data points based on classification of neighbors. Eg. Product recommendation based on user preferences.

Principal component Analysis (PCA)​

Condensing data while retaining the most important features Eg. facial recognition

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• Train the model on the data

Supervised Learning​

Supervises learning is a machine learning approach where a model is trained on labeled data, using input-output pairs to learn the relationship between them. The model makes predictions on new, unseen data by applying the patterns it has learned from the training examples.

Unsupervised learning​

Unsupervised learning is a machine learning approach where a model is trained on unlabled data, aiming to identify patterns, structures, or groupings within the data without predefined output categories. It is commonly used for tasks such as clustering, dimensionality reduction, and anomaly detection, helping to uncover hidden relationships in the data.

Reinforcement Learning​

Reinforcement learning is a machine learning approach where an agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties. The agent aims to maximize cumulative rewards over time by exploring different actions and learning from the consequences of its choices.

• Evaluate Model performance. Perform a series of tests to validate whether the model generates usable output

Batch inferencing​

Batch inferencing is the process of making predictions or decisions on a large set of data at once, rather than individually processing each data point. This approach allows for efficient and scalable analysis by handling multiple inputs in a single operation. Batch inferencing is used when accuracy is more important than speed of response.

Real time inferencing​

Real-time inferencing is the process of making predictions or decisions on data instantly as it is received, enabling immediate responses. This approach is crucial for applications requiring quick, dynamic interaction, such as live video analysis or online recommendation systems. Self-driving cars use real-time inferencing while in motion.

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Real World Examples of AI applications​

• Computer Vision Autonomous vehicles utilize computer vision to interpret and navigate their environment. They rely on a combination of sensors, cameras, and AI algorithms to perceive the world around them.

• NLP Speech recognition Virtual assistants leverage NLP and speech recognition to understand and respond to user queries in natural language. They allow for hands-free operation of devices, providing users with a seamless interaction experience.

• Recommendation systems E-commerce platforms employ recommendation systems to provide personalized shopping experiences for users. These systems analyze user behavior and preferences to suggest products that are most likely to be purchases.

• Fraud detection Financial institutions, including banks and credit card companies, employ fraud detection systems to identify and prevent fraudulent transactions in real-time. These systems use ML algorithms to analyze transaction data and flag suspicious activities.

• Forecasting In supply chain management, accurate demand forecasting is crucial for ensuring products are available to meet customer demand while minimizing excess inventory costs. Companies use AI to analyze historical sales data and predict future demand.

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Introduction to RAG​

• Retrieval Augmented Generation - The process of augmenting LLM output by referencing a knowledge base that is outside the context of the LLM training sources

knowledge base option​

• Traditional Database or Indexing Syatem Use a traditional database or an indexing system like Elasticsearch. Here, the documents are indexed based on keywords or phrases. the retreval process invovles searching these indices to identify documents that match the query terms, which can then be sent to the LLM for generating responses.

• Vector Database In this method, structured or unstructured data are split into chunks, then embedded into vectors using a model (often a transformer-based encoder). These vectors are then stored in a vecotr database that supports efficient similarity search. When a prompt is submitted, this database is searched first, using a vecotr representing the query. It then retrives the most relevant documents based on vector similarity, and adds this data to the prompt.

RAG Benefits​

• Enhanced factuality and accuracy
• LLM contextual relevance
• Improved handling of specific verticals

RAG Challenges​

• Pipeline complexity
• Latency issues
• Dependence on the quality of the Retrieval set
• Resource Requirements
• Difficulty in Tuning and maintenance

How do you understand if an AI model is delivering business objectives ?​

Below are key considerations

• Alignement with Business Objectives - Ensure that the model addresses specific goals.
• Performance metrics - Define KPIs to measure effectiveness
• User Feedback - collect qualitative insights from end users.
• Integration and Usability - Evaluate how well the model integrates into existing workflows

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Generative AI​

• Transformer-based LLMs are models that can understand and generate human-like text. They are trained on text data various sources, and learn patterns and relationships between words and phrases.

• Tokens - Units of text that the model processes individually. It represents a fragment of the input text, which can be a word, subword, characters, or even punctuation mark depending on the specific tokenization method used by the model.

• Chunking - The practice of breaking down a large text input or output into smaller, more manageable pieces for procesessing. Chunk size (tokens) is an important parameter when creating a vector database.

• Vectors - A mathematical representation of data (word, sentence or document) as a series of numerical values organized in a specific order. This representation captures various features or dimensions of the data, enabling the calculation of relationship or similarities.

Foundation Model Types for Generative AI​

• A Large Language Model (LLM) is an AI model designed to understand and generate human-like text based on vast amounts of data. It uses advanced algorithms to process and respond to language, enabling tasks like text generation, translation and question-answering

• Diffusion Models start with noise or random data, and gradually add information until a recognizable pattern is obtained. This is often applied to image generation but can also be used for text or audio genreation.

• Multimodal models are foundation models which have been trained on multiple media types. These media types can include text, audio, video, and images. The models can both interpret and generate these media types.

• Generative Adversarial Networkds (GANs) - This model consists of two neural networks which compete with each other. One generates content, and the other attempts to differentiate that generated content from real data. the competition continues until the generated content and real data are indistinguishable from each other.

Generative AI Advantages​

• Adaptability - Generative AI excels in adapting to diverse tasks and problem domains, making it useful across a wide range of industries. It can seamlessly switch between languge, visual, and data-centric applications without needed extensive reconfiguration. This flexibility helps organizations leverage AI to tackle varied challenges with a single adaptable system.

• Responsiveness - Generative AI models can rapidly produce outputs and insights in real-time, enabling swift responses to use queries and changing requirements. Their ability to process information and generate relevant content makes them suitable for interactive applications, such as chatbots and customer support. This responsiveness enhances user experience by providing instant and contextually appropriate answers.

• Simplicity - Generative AI models often simplify complex workflows by automating content generation and decision-making processes. They reduce the need for manual intervention or domain-specific coding, making AI-driven solutions more accessible to non-technical users. As a result, businesses can deploy sophisticated solutions with minimal setup and oversight.

• Creativity and Exploration - Generative AI opens up new avenues for creativity by suggesting novel ideas, designs, or content based on learned patterns. It can assist with brainstorming, creative writing, and design prototyping, providing users with unexpected and innovative options. This capability helps push the boundaries of traditional problem-solving and artistic creation.

• Data Efficiency - Many generative AI models are designed to learn effectively from relatively small datasets through pre-training and fine-tuning techniques. This data efficiency reduces the dependency on massive labeled datasets, lowering costs and effort associated with data preparation. It also allows models to generate meaningful outputs even in data-sparse environments.

Generative AI DisAdvantages​

• Regulatory Violations - Generative AI models can inadvertently generate content that violets regulatory guidelines, such as producing misleading financial advice or content that doesn't comply with advertising standards. Organizations using these models may face complaince challenges, especially in highly regulated industries like healthcare and finance. this risk underscores the need for strict oversight and adherence to legal requirements when deploying AI systems.

• Social Risks - Generative AI can be used to create deepfakes disinformation, or biased content, potentially amplifying harmful social impacts. Such outputs can erode trust, manipulate public opinion, or contribute to social polarization. The misuse of generative AI for malicious purpose poses significant ethical and societtal concerns that require careful mitigation strategies.

• Data Security and Privacy Concerns - Generative AI models often require access to sensitive datasets, raising risks of data leakage or unintended exposure of personal information. If improperly handled, these models may inadvertently reveal private data points from training data. Ensuring data security and maintaining user privacy is a critical challenge when deploying generative models, especially in sensitive applications.

• Toxicity - Generative models can sometimes produce toxic or harmful content, such as offensive language or inappropriate suggestions, if they are not carefully monitored. This issue is often due to biases or toxic patterns present in the training data. It necessitates rigorous content moderation and filtering techniques to prevent harmful outputs in public-facing applications.

• Hallucinations - Generative AI may produce outputs that are factually incorrect or completely fabricated, known as "hallucinations". This problem is particularly challenging when using AI for tasks requiring high accuracy, such as generating technical documentation or answering factual questions. Hallucinations can undermine trust and reliability, making it difficult to use generative AI in mission-critical scenarios.

• Nondeterminism - Generative AI models can produce different outputs even when given the same input, due to their probabilistic nature. This nondeterminism complicates tasks that require consistency, such as legal document generation or standardized communication. It also makes debugging and validating AI-generated outputs more complex, limiting their applicability in certain use cases.

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Model Selection Decision Tree​

What content are you trying to generate?

• Text
• Image
• Audio
• Video
• Multimodal

Other model consideration

• Performance and latency
• Customization
• Constraints and Resource
• GRC (Governance Risk and Compliance)

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What is Prompt Engineering?​

The process of desiging and refining input prompts to optimize the performance of AI models. Enhances the quality of responses, guides model behavior, and can lead to more accurate results.

Key components of Prompt Engineering​

• Context - Information surrounding the prompt that helps the model understand the scenario
• Instruction - The specific tasks or question being posed to the model.
• Negative Prompts - Instructions that specify what the model should avoid or exclude in its response.

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ML Development Lifecycle​

1. Business Goal - Objectively measure business value of the outcomes against the defined business gola. Is ML the appropriate technology choice to solve the problem statement?

• Business Goal Definition Workflow
• Business consideration
• Frame the ML problem
• Determine the optimization objective
• Review data requirements
• Cost and performance optimization
• Production consideration

2. ML problem framing Definition Define what is observed and what should be predicted. Identify dependent and/or independent variables. Define inputs and outputs.

3. Collect Data

• Data labeling
• Ingest (streaming, batch)
• Data aggregation

4. Data pre-processing workflow

• Clean
• Partition
• Scale
• Unbalance, Balance
• Augment

5. Feature Engineering tasks (Features are inputs to ML models used during training and inference)

• Feature selection: The process of selecting a subset of extracted features. This is the subset that is relevant and contributes to minimizing the error rate of a trained model.
• Feature transformation - Steps for replacing missing features or fetaures that are not valid.
• Feature Creation - The creation of new features from existing data to help with better predictions
• Feature Extraction - The process of reducing the data to be processed using dimensionality reduction techniques.

6. Train, tune and evaluate: The process of training a machine learning model involves providing the algorithm with training data to learn from.

7. Hyperparameters are settings that can control the beahvior of the ML algorithm. Hyperparameter tuning, or optimization, is the process of choosing the optimal hyperparameters for an algorithm.