Boost Your AI: Smarter Strategies for Continuous Model Improvement
Founder’s Note
You must ensure continuous improvement for your AI solution.
Think of a new team member, that starts off strong but continues to lose course each day, they probably wouldn't remain your colleague for too long. This analogy holds true for AI models too. One mistake ML engineers make when developing models is that they have no feedback loop or model upgrade strategy and without that even the most impressive models can quickly become obsolete. Imagine if Open AI expected GPT3 to always work effectively and did not invest more time in retraining the model on new observations, tuning its prompts, or upgrading its architecture to GPT3.5 and then 4. Allowing it to be multi-modal and smarter? We surely wouldn't have a leading LLM in GPT-4.
There are many ways to continuously improve deep learning models, and not all of them require computing resources. For instance retraining techniques like LoRA, and now Spectrum are only a few ways of improving these models. In this newsletter, we dive into smarter ways of improving model performance both on general and specific tasks.
Contact us and seize the opportunity to improve your organisation's machine learning models on specialists@edenai.co.za. We would be happy to contribute to your machine learning process.
Bami Oni
Our Blog Posts
Exploring Model Retraining Techniques
Model retraining is essential for maintaining the accuracy and relevance of machine learning models as data and patterns evolve. It involves updating models with new data to address issues like data drift and concept drift, ensuring they perform well over time. Techniques include scheduled updates, performance-based triggers, online learning, active learning, domain adaptation, and ensemble methods. Automated retraining pipelines and data-centric approaches also play crucial roles. By keeping models up-to-date with continuous and adaptive retraining processes, businesses can prevent performance degradation, maintain customer trust, and increase revenue.
Continuous Machine Learning Models
This post discusses the importance and benefits of continuous machine learning in maintaining model accuracy and effectiveness in a rapidly evolving data landscape. Continuous machine learning allows models to adapt incrementally with new data, rather than requiring complete retraining. This approach ensures models remain relevant and high-performing, particularly in real-time applications like e-commerce recommendations and fraud detection.
Programmer’s Humour
Shots From Our Social Media Timelines
Common Errors When Applying Data
Data entry is crucial for accurate record-keeping and efficient operations in any organisation, but it is prone to various errors such as inaccurate inputs, wrong data formatting, transposition errors, representation/unit inconsistencies, and data misinterpretation. These errors can lead to significant inaccuracies and workflow disruptions. Preventing such errors is essential for maintaining the accuracy and efficiency of organisational processes
AI in Healthcare
Artificial Intelligence is revolutionising many industries today, including the Healthcare sector. The adoption of AI in healthcare isn’t only limited to process automation or data science but beyond that. Here are some trends emerging from the AI industry in healthcare by Mehul Rajput in an article on RTInsights
Challenges In Data Analysis
In the fast-paced world of data-driven decision-making, organisations face challenges like relying on outdated historical data, underutilizing insights due to inaction, untapped data from poor quality or limited processing, human bias in model selection, slow and resource-intensive analytics projects, data-security concerns, and time-consuming manual tasks.
Recommended by LinkedIn
How Are LLMs Used
Large Language Models (LLMs) are advanced AI systems trained on extensive text data to perform tasks such as translation, summarization, and text generation. They enhance customer service with AI-driven chatbots and virtual assistants, improve market research and sentiment analysis, generate consistent and authentic content, and provide personalised recommendations on ecommerce platforms and streaming services. These capabilities revolutionise customer service, market analysis, content creation, and user experiences across various sectors.
Other Articles
Multimodal Models and Computer Vision: A Deep Dive
The article explores the advancements in multimodal deep learning, which combines data from multiple sources such as text, images, video, and audio to create more robust and accurate machine learning models. It covers the importance, goals, and techniques of multimodal deep learning, including encoding, fusion, and classification stages. Additionally, it highlights practical applications in computer vision, such as visual question answering, text-to-image generation, and natural language for visual reasoning.
Multimodal AI Models: Understanding Their Complexity
By Edwin Lisowski
The article delves into multimodal AI, a subset of artificial intelligence that integrates multiple types of data (e.g., text, images, audio, video) to create more accurate and comprehensive models. It explores the fundamentals, benefits, and challenges of multimodal AI, contrasting it with unimodal AI, which relies on a single data source. The article highlights applications in various fields like healthcare, automotive, and web search, showcasing how multimodal AI enhances contextual understanding, accuracy, and natural interaction in AI systems. Additionally, it discusses different techniques like combining models and multimodal learning to leverage diverse data for improved AI capabilities.
More Reads
Data Analytics Maturity Levels
The article discusses the importance of data analytics maturity for organisations aiming to enhance their data-driven decision-making capabilities. It outlines the concept of analytics maturity, which evaluates an organisation's proficiency in utilising analytics, and emphasises the correlation between analytics maturity and company performance. The framework includes four dimensions: data maturity, organisational dynamics, analytics team dynamics, and usage and technology. The article details the progression through four levels of analytics maturity—descriptive, diagnostic, predictive, and prescriptive—and provides strategies for improving each level, such as centralising data, focusing on high-value projects, assembling cross-functional teams, scaling analytics infrastructure, and democratising analytics skills across the organisation. By advancing through these maturity levels, businesses can drive innovation and gain competitive advantage.
State of AI Adoption in Enterprises: Trends and Challenges
Artificial intelligence (AI) is transforming industries by automating tasks, enhancing data analysis, and improving customer experiences, exemplified by a leading retail company's AI-powered chatbot that reduced support tickets by 30%. Key drivers of AI adoption in enterprises include automation, data analysis, and customer experience improvements. However, challenges such as ensuring data quality, bridging the skills gap, and addressing ethical concerns like bias and privacy must be overcome. The future of business is increasingly AI-driven, and organisations must navigate these challenges to fully leverage AI's potential.
E-Token
Our team here at Eden AI is creating E-Token. E-Token is a way to incentivize people for their positive energy habits and drive them towards a climate neutral goal.
It is a project that was selected from amongst 100s of projects to be presented worldwide and presented in Fountainbleau, Paris at the Blue Ocean Awards.
E-Token provides a platform where these non-customers can now become customers of the energy efficiency industry. We also ensure that people imbibe positive energy habits in order to meet their climate goals.
A Look Behind The Curtain
Bami Oni founded Eden AI to improve life by providing services such as Computer Vision, Machine Learning, Data Science and Analytics, and AI advisory.
Join us in actively using AI to apply effective solutions in societal and business contexts.
Contact us: specialists@edenai.co.za
Check out our website: https://edenai.co.za