⭐️ What is Generative AI?
Generative AI typically uses machine learning models, especially deep learning models, to learn from input data and then generate new data based on the patterns and trends it has learned. This can be applied for many different purposes, from creating images, videos, sounds, text or 3D models. Generative AI is also being widely adopted in many business and industrial sectors to optimize processes, create new products and services, and improve overall organizational performance.
The latest breakthroughs like ChatGPT, a chatbot developed by OpenAI (USA) is a typical example of Generative AI. GPT Chat has the ability to create content in a variety of genres such as text responses, blogging, poetry, song lyrics… without limiting language or any topic. In addition to ChatGPT, many Generative AI products are available on the market and can fully handle programming, painting, video making, data analysis…
Hekate has successfully applied Generative AI in many fields: Retail and E-commerce (Coca-Cola; Pla18); Real Estate (Masterise); Public area; Governmental and non-governmental organizations.
Generative AI typically uses machine learning models, especially deep learning models, to learn from input data and then generate new data based on the patterns and trends it has learned. This can be applied for many different purposes, from creating images, videos, sounds, text or 3D models. Generative AI is also being widely adopted in many business and industrial sectors to optimize processes, create new products and services, and improve overall organizational performance.
The latest breakthroughs like ChatGPT, a chatbot developed by OpenAI (USA) is a typical example of Generative AI. GPT Chat has the ability to create content in a variety of genres such as text responses, blogging, poetry, song lyrics… without limiting language or any topic. In addition to ChatGPT, many Generative AI products are available on the market and can fully handle programming, painting, video making, data analysis…
Hekate has successfully applied Generative AI in many fields: Retail and E-commerce (Coca-Cola; Pla18); Real Estate (Masterise); Public area; Governmental and non-governmental organizations.
⭐️ How to evaluate Generative AI models?
Three important things for a successful generative AI model are:
Quality: For applications that interact directly with users, it is most important to have high quality output. For example, in speech production, if the quality is poor, it will be difficult for the listener to understand. Similarly, when creating images, the desired results should resemble natural images.
Diversity: A good generative model is one that is capable of capturing rare cases in the data without sacrificing output quality. This helps reduce unwanted biases in learning models.
Speed: Many interactive applications require rapid creation, such as instant photo editing for use in the content creation workflow.
Three important things for a successful generative AI model are:
Quality: For applications that interact directly with users, it is most important to have high quality output. For example, in speech production, if the quality is poor, it will be difficult for the listener to understand. Similarly, when creating images, the desired results should resemble natural images.
Diversity: A good generative model is one that is capable of capturing rare cases in the data without sacrificing output quality. This helps reduce unwanted biases in learning models.
Speed: Many interactive applications require rapid creation, such as instant photo editing for use in the content creation workflow.
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⭐️ What are the applications of Generative AI?
Generative AI is a powerful tool to standardize the workflow of innovators, engineers, researchers, scientists, and more. Use cases and capabilities span all sectors and individuals.
Generative AI models can take inputs like text, images, audio, video, and code and generate new content in any of the methods mentioned. For example, it can turn input text into images, turn images into songs, or turn videos into text.
Generative AI is a powerful tool to standardize the workflow of innovators, engineers, researchers, scientists, and more. Use cases and capabilities span all sectors and individuals.
Generative AI models can take inputs like text, images, audio, video, and code and generate new content in any of the methods mentioned. For example, it can turn input text into images, turn images into songs, or turn videos into text.
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⭐️ Generative AI Use Cases
Below are popular Generative AI applications
Language:
Text is the foundation of many AI models, and large language models (LLMs) are a popular example. LLM can be used for a variety of tasks such as essay creation, code development, translation, and even understanding genetic sequences.
Sound:
AI is also applied in music, audio and speech. Models can develop songs, generate audio from text, recognize objects in videos, and even generate audio for different scenes.
Image:
In the visual field, AI is widely used to create 3D images, avatars, videos, graphs, and illustrations. Models have the flexibility to create images with a variety of aesthetic styles and editing techniques.
Synthetic data:
Synthetic data is extremely important for training AI models when data is insufficient, limited, or simply cannot solve difficult cases with the highest accuracy. Synthetic data spans all methods and use cases and is made possible through a process called label efficient learning. Generative AI models can reduce labeling costs by generating training data automatically or by learning how to use less labeled data.
Innovative AI models are highly influential in many fields. In cars, they can help develop 3D worlds and simulations, as well as train autonomous vehicles. In medicine, they can aid in medical research and weather prediction. In entertainment, from games to movies and virtual worlds, AI models help create content and enhance creativity.
Below are popular Generative AI applications
Language:
Text is the foundation of many AI models, and large language models (LLMs) are a popular example. LLM can be used for a variety of tasks such as essay creation, code development, translation, and even understanding genetic sequences.
Sound:
AI is also applied in music, audio and speech. Models can develop songs, generate audio from text, recognize objects in videos, and even generate audio for different scenes.
Image:
In the visual field, AI is widely used to create 3D images, avatars, videos, graphs, and illustrations. Models have the flexibility to create images with a variety of aesthetic styles and editing techniques.
Synthetic data:
Synthetic data is extremely important for training AI models when data is insufficient, limited, or simply cannot solve difficult cases with the highest accuracy. Synthetic data spans all methods and use cases and is made possible through a process called label efficient learning. Generative AI models can reduce labeling costs by generating training data automatically or by learning how to use less labeled data.
Innovative AI models are highly influential in many fields. In cars, they can help develop 3D worlds and simulations, as well as train autonomous vehicles. In medicine, they can aid in medical research and weather prediction. In entertainment, from games to movies and virtual worlds, AI models help create content and enhance creativity.
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⭐️ Benefits of Generative AI
Generative AI is one of the outstanding technologies today with many practical benefits such as:
Create Unique Content: Innovative AI algorithms are capable of generating new and unique content such as images, videos, and text that are difficult to distinguish from human-generated content. This benefits many applications such as entertainment, advertising, and creative arts.
Enhancing AI System Efficiency: Generative AI can be applied to improve the performance and accuracy of current AI systems, such as natural language processing and computer vision. For example, general AI algorithms can generate synthetic data to train and test other AI algorithms.
Discovering New Data: Innovative AI has the ability to explore and analyze complex data in new ways, helping businesses and researchers learn about hidden patterns and trends that raw data can reveal. not shown clearly.
Process Automation and Acceleration: Generative AI algorithms can help automate and accelerate a variety of tasks and processes. This saves businesses and organizations time and resources, while increasing productivity.
Generative AI is one of the outstanding technologies today with many practical benefits such as:
Create Unique Content: Innovative AI algorithms are capable of generating new and unique content such as images, videos, and text that are difficult to distinguish from human-generated content. This benefits many applications such as entertainment, advertising, and creative arts.
Enhancing AI System Efficiency: Generative AI can be applied to improve the performance and accuracy of current AI systems, such as natural language processing and computer vision. For example, general AI algorithms can generate synthetic data to train and test other AI algorithms.
Discovering New Data: Innovative AI has the ability to explore and analyze complex data in new ways, helping businesses and researchers learn about hidden patterns and trends that raw data can reveal. not shown clearly.
Process Automation and Acceleration: Generative AI algorithms can help automate and accelerate a variety of tasks and processes. This saves businesses and organizations time and resources, while increasing productivity.
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🖥 Large Language Model Course
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
The popular free LLM course has just been updated.
This is a step-by-step guide with useful resources and notebooks for both beginners and those who already have an ml-base.
The course is divided into 3 parts:
1️⃣ LLM Fundamentals : The block provides fundamental knowledge of mathematics, Python and neural networks.
2️⃣ LLM Scientist : This block focuses on the internal structure of LLMs and their creation using the latest technologies and frameworks.
3️⃣ The LLM Engineer : Here you will learn how to write applications in a hands-on way and how to deploy them.
⭐️ 41.4k stars on Github
📌 https://github.com/mlabonne/llm-course
#llm #course #opensource #ml
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🔰 Machine Learning Roadmap for Beginners 2025
├── 🧠 What is Machine Learning?
├── 🧪 ML vs AI vs Deep Learning
├── 🔢 Math Foundation (Linear Algebra, Calculus, Stats Basics)
├── 🐍 Python Libraries (NumPy, Pandas, Scikit-learn)
├── 📊 Data Preprocessing & Cleaning
├── 📉 Feature Selection & Engineering
├── 🧭 Supervised Learning (Regression, Classification)
├── 🧱 Unsupervised Learning (Clustering, Dimensionality Reduction)
├── 🕹 Model Evaluation (Confusion Matrix, ROC, AUC)
├── ⚙️ Model Tuning (Hyperparameter Tuning, Grid Search)
├── 🧰 Ensemble Methods (Bagging, Boosting, Random Forests)
├── 🔮 Introduction to Neural Networks
├── 🔁 Overfitting vs Underfitting
├── 📈 Model Deployment (Streamlit, Flask, FastAPI Basics)
├── 🧪 ML Projects (Classification, Forecasting, Recommender)
├── 🏆 ML Competitions (Kaggle, Hackathons)
Like for the detailed explanation ❤️
#machinelearning
├── 🧠 What is Machine Learning?
├── 🧪 ML vs AI vs Deep Learning
├── 🔢 Math Foundation (Linear Algebra, Calculus, Stats Basics)
├── 🐍 Python Libraries (NumPy, Pandas, Scikit-learn)
├── 📊 Data Preprocessing & Cleaning
├── 📉 Feature Selection & Engineering
├── 🧭 Supervised Learning (Regression, Classification)
├── 🧱 Unsupervised Learning (Clustering, Dimensionality Reduction)
├── 🕹 Model Evaluation (Confusion Matrix, ROC, AUC)
├── ⚙️ Model Tuning (Hyperparameter Tuning, Grid Search)
├── 🧰 Ensemble Methods (Bagging, Boosting, Random Forests)
├── 🔮 Introduction to Neural Networks
├── 🔁 Overfitting vs Underfitting
├── 📈 Model Deployment (Streamlit, Flask, FastAPI Basics)
├── 🧪 ML Projects (Classification, Forecasting, Recommender)
├── 🏆 ML Competitions (Kaggle, Hackathons)
Like for the detailed explanation ❤️
#machinelearning
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Essential Skills to Master for Using Generative AI
1️⃣ Prompt Engineering
✍️ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2️⃣ Data Literacy
📊 Understand data sources, biases, and how AI models process information.
3️⃣ AI Ethics & Responsible Usage
⚖️ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4️⃣ Creativity & Critical Thinking
💡 AI enhances creativity, but human intuition is key for quality content.
5️⃣ AI Tool Familiarity
🔍 Get hands-on experience with tools like ChatGPT, DALL·E, Midjourney, and Runway ML.
6️⃣ Coding Basics (Optional)
💻 Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7️⃣ Business & Marketing Awareness
📢 Leverage AI for automation, branding, and customer engagement.
8️⃣ Cybersecurity & Privacy Knowledge
🔐 Learn how AI-generated data can be misused and ways to protect sensitive information.
9️⃣ Adaptability & Continuous Learning
🚀 AI evolves fast—stay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! 🔥
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
1️⃣ Prompt Engineering
✍️ Learn how to craft clear, detailed prompts to get accurate AI-generated results.
2️⃣ Data Literacy
📊 Understand data sources, biases, and how AI models process information.
3️⃣ AI Ethics & Responsible Usage
⚖️ Know the ethical implications of AI, including bias, misinformation, and copyright issues.
4️⃣ Creativity & Critical Thinking
💡 AI enhances creativity, but human intuition is key for quality content.
5️⃣ AI Tool Familiarity
🔍 Get hands-on experience with tools like ChatGPT, DALL·E, Midjourney, and Runway ML.
6️⃣ Coding Basics (Optional)
💻 Knowing Python, SQL, or APIs helps customize AI workflows and automation.
7️⃣ Business & Marketing Awareness
📢 Leverage AI for automation, branding, and customer engagement.
8️⃣ Cybersecurity & Privacy Knowledge
🔐 Learn how AI-generated data can be misused and ways to protect sensitive information.
9️⃣ Adaptability & Continuous Learning
🚀 AI evolves fast—stay updated with new trends, tools, and regulations.
Master these skills to make the most of AI in your personal and professional life! 🔥
Free Generative AI Resources: https://whatsapp.com/channel/0029VazaRBY2UPBNj1aCrN0U
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Top 10 machine Learning algorithms 👇👇
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
1. Linear Regression: Linear regression is a simple and commonly used algorithm for predicting a continuous target variable based on one or more input features. It assumes a linear relationship between the input variables and the output.
2. Logistic Regression: Logistic regression is used for binary classification problems where the target variable has two classes. It estimates the probability that a given input belongs to a particular class.
3. Decision Trees: Decision trees are a popular algorithm for both classification and regression tasks. They partition the feature space into regions based on the input variables and make predictions by following a tree-like structure.
4. Random Forest: Random forest is an ensemble learning method that combines multiple decision trees to improve prediction accuracy. It reduces overfitting and provides robust predictions by averaging the results of individual trees.
5. Support Vector Machines (SVM): SVM is a powerful algorithm for both classification and regression tasks. It finds the optimal hyperplane that separates different classes in the feature space, maximizing the margin between classes.
6. K-Nearest Neighbors (KNN): KNN is a simple and intuitive algorithm for classification and regression tasks. It makes predictions based on the similarity of input data points to their k nearest neighbors in the training set.
7. Naive Bayes: Naive Bayes is a probabilistic algorithm based on Bayes' theorem that is commonly used for classification tasks. It assumes that the features are conditionally independent given the class label.
8. Neural Networks: Neural networks are a versatile and powerful class of algorithms inspired by the human brain. They consist of interconnected layers of neurons that learn complex patterns in the data through training.
9. Gradient Boosting Machines (GBM): GBM is an ensemble learning method that builds a series of weak learners sequentially to improve prediction accuracy. It combines multiple decision trees in a boosting framework to minimize prediction errors.
10. Principal Component Analysis (PCA): PCA is a dimensionality reduction technique that transforms high-dimensional data into a lower-dimensional space while preserving as much variance as possible. It helps in visualizing and understanding the underlying structure of the data.
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1700001429173.pdf
427.3 KB
Top Python libraries for generative AI
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Generative AI is a branch of artificial intelligence that focuses on the creation of new content, such as text, images, music, and code. This is done by training models on large datasets of existing content, which the model then uses to generate new content.
Python is a popular programming language for generative AI, as it has a wide range of libraries and frameworks available.
Programming Practice Python 2023.pdf
5.4 MB
Programming Practice Python
Like for more
Like for more
Artificial Intelligence for Learning.pdf
2.8 MB
Artificial Intelligence for Learning
Donald Clark, 2024
Donald Clark, 2024
Masato_Hagiwara_Real_World_Natural_Language_Processing_Practical.pdf
11.5 MB
Real-World Natural Language Processing
Masato Hagiwara, 2021
Masato Hagiwara, 2021
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5 Free NLP Courses I’d Recommend for 2025
1. NLP in Python: 🔗 Course
Learn fundamental NLP techniques using Python with hands-on projects.
2. AI Chatbots (No Code): 🔗 Course
Build AI-powered chatbots without programming in this IBM course.
3. Data Science Basics: 🔗 Course
Beginner-friendly tutorials on data analysis, mining, and modeling.
4. NLP on Google Cloud: 🔗 Course
Advanced NLP with TensorFlow and Google Cloud tools for professionals.
5. NLP Specialization: 🔗 Course
All the best 👍👍
1. NLP in Python: 🔗 Course
Learn fundamental NLP techniques using Python with hands-on projects.
2. AI Chatbots (No Code): 🔗 Course
Build AI-powered chatbots without programming in this IBM course.
3. Data Science Basics: 🔗 Course
Beginner-friendly tutorials on data analysis, mining, and modeling.
4. NLP on Google Cloud: 🔗 Course
Advanced NLP with TensorFlow and Google Cloud tools for professionals.
5. NLP Specialization: 🔗 Course
All the best 👍👍
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Here's a step-by-step beginner's roadmap for learning machine learning:🪜📚
Learn Python: Start by learning Python, as it's the most popular language for machine learning. There are many resources available online, including tutorials, courses, and books.
Understand Basic Math: Familiarize yourself with basic mathematics concepts like algebra, calculus, and probability. This will form the foundation for understanding machine learning algorithms.
Learn NumPy, Pandas, and Matplotlib: These are essential libraries for data manipulation, analysis, and visualization in Python. Get comfortable with them as they are widely used in machine learning projects.
Study Linear Algebra and Statistics: Dive deeper into linear algebra and statistics, as they are fundamental to understanding many machine learning algorithms.
Introduction to Machine Learning: Start with courses or tutorials that introduce you to machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.
Explore Scikit-learn: Scikit-learn is a powerful Python library for machine learning. Learn how to use its various algorithms for tasks like classification, regression, and clustering.
Hands-on Projects: Start working on small machine learning projects to apply what you've learned. Kaggle competitions and datasets are great resources for this.
Deep Learning Basics: Dive into deep learning concepts and frameworks like TensorFlow or PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Advanced Topics: Explore advanced machine learning topics such as ensemble methods, dimensionality reduction, and generative adversarial networks (GANs).
Stay Updated: Machine learning is a rapidly evolving field, so it's important to stay updated with the latest research papers, blogs, and conferences.
🧠👀Remember, the key to mastering machine learning is consistent practice and experimentation. Start with simple projects and gradually tackle more complex ones as you gain confidence and expertise. Good luck on your learning journey!
Learn Python: Start by learning Python, as it's the most popular language for machine learning. There are many resources available online, including tutorials, courses, and books.
Understand Basic Math: Familiarize yourself with basic mathematics concepts like algebra, calculus, and probability. This will form the foundation for understanding machine learning algorithms.
Learn NumPy, Pandas, and Matplotlib: These are essential libraries for data manipulation, analysis, and visualization in Python. Get comfortable with them as they are widely used in machine learning projects.
Study Linear Algebra and Statistics: Dive deeper into linear algebra and statistics, as they are fundamental to understanding many machine learning algorithms.
Introduction to Machine Learning: Start with courses or tutorials that introduce you to machine learning concepts such as supervised learning, unsupervised learning, and reinforcement learning.
Explore Scikit-learn: Scikit-learn is a powerful Python library for machine learning. Learn how to use its various algorithms for tasks like classification, regression, and clustering.
Hands-on Projects: Start working on small machine learning projects to apply what you've learned. Kaggle competitions and datasets are great resources for this.
Deep Learning Basics: Dive into deep learning concepts and frameworks like TensorFlow or PyTorch. Understand neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).
Advanced Topics: Explore advanced machine learning topics such as ensemble methods, dimensionality reduction, and generative adversarial networks (GANs).
Stay Updated: Machine learning is a rapidly evolving field, so it's important to stay updated with the latest research papers, blogs, and conferences.
🧠👀Remember, the key to mastering machine learning is consistent practice and experimentation. Start with simple projects and gradually tackle more complex ones as you gain confidence and expertise. Good luck on your learning journey!
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Generative AI is a multi-billion dollar opportunity!
There will be some winners and losers emerging directly or indirectly impacted by Gen AI 🚀 💹
But, how to leverage it for the business impact? What are the right steps?
✔️Clearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
👀 Uses public models with minimal customization at a lower cost.
🤖 Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
🚀Develops a unique foundation model for a specific business case, which requires substantial investment.
✔️Develop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
✔️Quickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
✔️Integrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
✔️Create a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
✔️Use existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
✔️Upgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
✔️Develop a data architecture that can process both structured and unstructured data.
✔️Establish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
✔️Upskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
✔️Assess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
✔️For many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
There will be some winners and losers emerging directly or indirectly impacted by Gen AI 🚀 💹
But, how to leverage it for the business impact? What are the right steps?
✔️Clearly define and communicate company-wide policies for generative AI use, providing access and guidelines to use these tools effectively and safely.
Your business probably falls into one of these types of categories, make sure to identify early and act accordingly:
👀 Uses public models with minimal customization at a lower cost.
🤖 Integrates existing models with internal systems for more customized results, suitable for scaling AI capabilities.
🚀Develops a unique foundation model for a specific business case, which requires substantial investment.
✔️Develop financial AI capabilities to accurately calculate the costs and returns of AI initiatives, considering aspects such as multiple model/vendor costs, usage fees, and human oversight costs.
✔️Quickly understand and leverage Generative AI for faster code development, streamlined debt management, and automation of routine IT tasks.
✔️Integrate generative AI models within your existing tech architecture and develop a robust data infrastructure and comprehensive policy management.
✔️Create a cross-functional AI platform team, developing a strategic approach to tool and service selection, and upskilling key roles.
✔️Use existing services or open-source models as much as possible to develop your own capabilities, keeping in mind the significant costs of building your own models.
✔️Upgrade enterprise tech architecture to accomodate generative AI models with existing AI models, apps, and data sources.
✔️Develop a data architecture that can process both structured and unstructured data.
✔️Establish a centralized, cross-functional generative AI platform team to provide models to product and application teams on demand.
✔️Upskill tech roles, such as software developers, data engineers, MLOps engineers, ethical and security experts, and provide training for the broader non-tech workforce.
✔️Assess the new risks and hav an ongoing mitigation practices to manage models, data, and policies.
✔️For many, it is important to link generative AI models to internal data sources for contextual understanding.
It is important to explore a tailored upskilling programs and talent management strategies.
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