How do you optimize your AI software development lifecycle for image and video recognition?

In my experience with AI projects, aligning stakeholder expectations early on, especially in domains like image and video recognition, can significantly streamline the development process and ensure the solution truly addresses the core business needs. In the image processing there might be significant trade offs between speed of processing and accuracy. It is important to clarify the use case to ensure that your implementation suits well the business needs.

It can also be nice to define what metrics you are looking for and how you will consider your model a success. There are also a lot of available models you can retrain or build on top of them to make it easier for you to complete the project faster.

Defining the problem is key in every single AI Project. In image and more in video recog, clarity is the key due to diverse apps like security VS cinematic analysis. Pointing out the issue aids in setting precise expectations, addresing genuine user needs and charting the roadmap. This step clearly acts as a compass, ensuring the project aligns with both tech, functional and business goals

In the realm of wound care, optimizing AI for image and video recognition can revolutionize diagnosis and treatment. By clearly defining problems like wound categorization, infection detection, or monitoring healing progression, AI can assist clinicians in providing more accurate and timely interventions. Coupling this with stakeholder needs, such as the urgency for faster wound healing or reducing infection risks, can truly streamline the integration of AI in wound care. The precision and consistency AI offers promise a future where wound assessments are not just visually done, but backed by data-driven insights.

It is important from the right very beginning clarify what AI can and can not do with the stakeholders, nowadays due to the massive propagation of the term AI, many people may have very high expectations on what the existing models and algorithms can do, maybe this will lead to redefine the AI strategy and even consider use less complex but reliable models.

In wound imaging and documentation, the collection and preparation of data are key for AI effectiveness. High-quality, diverse datasets encompassing various wound types, stages, and complications empower AI models to make precise assessments. By ensuring the data is noise-free and standardized, AI can consistently analyze wound images, aiding in accurate diagnosis and tracking of healing progress. This not only streamlines documentation but also provides clinicians with a reliable tool to enhance patient care. With AI, the potential for advancements in wound care imaging is vast.

Collecting and preparing data is the vital second step in AI development. Quality and quantity significantly impact image and video recognition system accuracy. Gather diverse, representative data and preprocess it by removing noise, enhancing features, and standardizing formats. Adequate, well-processed data ensures optimal AI model performance.

Data is the BLOOD of AI: gathering different, quality data and prprocessing it for noise reduction, enhacing features and formatting is KEY. This will make data accurate, reliable in both image and video recognition

Vital is to go through the data and understand the nature of it, many times principal component analysis and dimension reduction can help a lot to avoid overfitted models, and even very diverse and preprocessed data may enable more simple models achieving high accuracy.

When collecting data for image and video recognition, the focus should not just be on quantity, but on representativeness and ethical sourcing. It's crucial to recognize that datasets are not mere collections of pixels but are reflections of real-world diversity and complexity. Ensuring the dataset encapsulates varied scenarios, lighting conditions, and demographic diversity is paramount. Furthermore, ethical considerations in data collection – such as privacy concerns and informed consent – must be addressed. This approach enhances the model's effectiveness in real-world applications and upholds ethical AI standards.

choosing the right model: CNNs, RNNS, GANs, is another key point. Depending on the task, data and resources. Customizing the model's architecture and parameters this step will be key for optimal performance

Choose the right AI model like CNNs, RNNs, or GANs for image and video tasks based on data complexity and resources. Customize the model's architecture and parameters to optimize its performance, ensuring it aligns perfectly with the project's specific recognition challenges.

Selecting and designing a model for image and video recognition isn't just about technical prowess; it's about fitting the model to the problem like a key to a lock. This involves understanding the nuances of different architectures – like CNNs for spatial relationships or RNNs for temporal sequences in videos – and aligning them with the specific needs of the application. Customizing models for specific tasks, like distinguishing between subtle facial expressions or detecting motion in varying light conditions, requires deep domain knowledge and creative engineering, ultimately leading to more precise and efficient models.

Train your AI model by adjusting its parameters to minimize prediction errors, using the prepared data. Simultaneously, test the model's performance on unseen data mimicking real-world situations. Employ metrics like accuracy, precision, recall, or F1-score to measure and compare the model's results accurately.

This step is where the AI model comes to life. Training involves refining the model's internal parameters to minimize predictions erros, aligning it better with data. Testing on the other hand, assesses its real-world readiness.

Preprocessing data and training are highly interconnected, adequating the data to the problem to solve can help to avoid exploding gradients or vanishing gradient due to the presence of many zeroes in the data or many outliers.

Deploying makes AI accessible, while monitoring ensures its reliability, security and scalability. Tools like tensorflow definetely simplify these tasks.

Deployment is an art itself, containerizing AI based backend or applications to deploy in scalable aplications can raise many issues non seen during development phase, hence planning the right deployment strategy such as testing in an environment close to production is crucial in the final stage on System Integration Testing and choosing the right tools have a big impact in the quality of the product

Gen AI models are now in use for data augmentation and synthetic data production. AI and ML are now in use to automate and maintain model fairness and bias testing within. A Stanford University and other premium institutes studies showed annotated photos of COVID-19’s dermatologic symptoms revealed inadequate images of darker skin, confirming the observations from dermatologists lacking representative tones example. Another study from millions of online photos of various workers labelled in India to tag explicit material found bias within and flagged that data paired with the photos classified as indecent are often images of same-sex couples. Hyperscalers' use AI to manage the workload distribution and carbon-foot print optimization.

At eKare, our unwavering commitment to leveraging AI to revolutionize wound imaging and assessment sets us apart. We constantly challenge the status quo, aiming for more accurate, rapid, and patient-focused assessments. By harnessing the prowess of AI, we're not just integrating technology with healthcare; we're spotlighting the importance of data-driven decisions in uplifting patient outcomes. I'm incredibly proud of the strides eKare is making in redefining the future of wound care.

In the AI SDLC for image and video recognition, it's crucial to focus on "Continuous Learning." Unlike traditional SDLCs that treat the model as static post-deployment, a Continuous Learning phase helps the model adapt to new data patterns. Implement real-time feedback loops to let the model learn from its errors. Use automated pipelines for periodic retraining based on monitored metrics. This ensures your model is not just maintained but continually improved, staying relevant and accurate.

MLOps or Machine Learning Operations is a core function of Machine Learning engineering including model / algorithm developments across various ML streams such as texts, images, audio, video, automating the end-to-end data acquisition training to production, and then maintaining and continuous monitoring of these models. MLOps is a collaborative function, often comprising a spectrum of personas -data engineer, ML Engineer, data scientists, dev-sec-ops engineers, and many others. MLOps helps continuous performance monitoring, training, managing data-drift and other operationalization activities.

In the broader context of optimizing AI SDLC for image and video recognition, it's crucial to embed a culture of ethical AI within the development team. This involves regularly revisiting and reassessing the project's ethical framework, ensuring compliance with evolving global standards, and fostering an environment of open discussion around the ethical implications of AI technologies. Such a culture not only enhances the societal acceptance and sustainability of the technology but also fosters innovation by encouraging developers to think beyond conventional boundaries and address deeper human-centric problems.