How can you use machine learning for specific domains?

Machine learning can be used for domains, such as anomaly detection. Anomaly detection identifies unusual or abnormal patterns or events in data, such as fraud, errors, or outliers. Machine learning can help perform anomaly detection by using techniques such as clustering, which groups similar data points together, or isolation forest, which isolates anomalous data points from normal ones. Machine learning frameworks such as TensorFlow, PyTorch, or Keras can be used to build and train anomaly detection models. Incorporating domain knowledge and expert feedback can enhance the performance and accuracy of the models. To govern data models, it is essential to ensure they are high-quality, standardized, secure, reusable, and evolvable

Machine learning-driven image recognition is revolutionizing sectors like advanced wound care. eKare's inSight harnesses this technology, offering clinicians a precise tool for wound assessments. With its ability to quickly analyze wound images, inSight reduces manual measurement times, streamlining care processes. As the tool continually adapts, the potential for enhancing accuracy and patient outcomes grows, positioning machine learning as a pivotal player in modern wound care advancements.

Well CNNs does the job, but now Transformer based models are doing better with less efforts to build and train your custom models. Go to Huggingface and you will find great models

Critical Considerations: - Data Augmentation: Leveraging data augmentation strategies can greatly amplify the efficacy of Convolutional Neural Networks (CNNs) in image recognition tasks. - Pre-trained Models: Utilizing pre-trained models as a foundation and fine-tuning for specific tasks can significantly reduce computational costs and accelerate deployment.

Now cloud providers like Microsoft Azure have native services that offer prebuilt natural language models. This is a chance to build on the shoulders of giants and create a secure, accurate, and protected way to leverage conversational AI safely and responsibly. Before you think about creating a natural language model from scratch, think about how you could leverage a model like a Azure OpenAI Service or one of the hundreds of thousands of models made available on Azure Machine Learning through Huggingface. Take advantage of all the work you have done to create a secure private cloud, and leverage a native LLM to do more, with less, faster than ever.

LLMs have taken the world by storm especially the corporate world. Why? It's because of the promise of hyper productivity and being able to do more with the same number of employees. Of course this would be appealing as it should increase profits. The key to actual increased productivity and profits will depend on the quality and volume of data used to train your specialized models. Think of it as "bad information in, worse information out" that's the power of a poorly trained LLM. Companies can minimize this risk by slowing down (not too much). Truly getting a grasp on what problems they are trying to solve. Focusing on smaller more specialized models instead of broader generalist ones.

In my opinion, NLP problem is solved and nothing does better than Transformer based models. RNNs struggles with keeping longer context and semantics, but now Transformer based models are doing better with less efforts to build and train your custom models. Go to Huggingface and you will find great models

I'll share my perspectives on the user experience. Many years ago, I had the opportunity to work with ML primitive algorithms that frequently encountered technical problems and provided a dull and monotonous user experience. However, the integration of Natural Language (NL) in AI tools has revolutionized user experience engagement. This is giving the experience of conversation with a smart friend who understands your point of view, improves your ideas, expands your horizons, and much more.

With NLP, clinicians can seamlessly convert their verbal observations and notes into structured data. This not only streamlines documentation but also facilitates faster and more comprehensive analysis of patient histories and wound progress. In a field where details and timely interventions matter, leveraging NLP can lead to improved patient outcomes and more efficient care delivery.

Collaborative filtering (Linden) is an extremely simple and performing tool, which I recommend for every first implementation.

Anomaly detection through AI is a powerful tool that transcends industries – one of my favorite applications. Customer Success: Consider its potential in call centers, determining the authenticity of a caller. Healthcare: detecting patterns in patient data, which could signify rare diseases or underlying health issues. Retail: Analyzing customer purchase behavior to identify potential fraud or develop personalized marketing strategies. Cybersecurity: Detecting unusual network behavior that might signify a security breach or vulnerability. These are just a few examples of how machine learning can amplify human capability in various fields, replacing manual efforts with precise and automated insights.

Anomaly detection in the realm of wound care offers a proactive approach to patient care. By utilizing algorithms that identify patterns and deviations, clinicians can promptly spot unusual changes or trends in wound healing that may not be immediately apparent. This early detection of potential complications, such as infections or deteriorating wound conditions, allows for timely interventions, potentially reducing healing time and improving overall patient outcomes. As a tool, anomaly detection underscores the importance of data-driven insights in modern healthcare practices.

Anomaly detection is a topic deserving the attention of every data scientist working in industrial contexts. One great example is predictive maintenance, where the ability to effectively predict failure can improve OEE and impact balance sheet in the short run. However, implementing an effective anomaly detection system is not copy-paste, and requires to combine data science skills with specific field knowledge. Reviewing the existing literature and fine tuning tools and platforms available online might be a good starting point for everyone interested in exploring this fascinating research area.

Utilizing machine learning for anomaly detection involves distinct approaches for time series and non-time series data. In time series scenarios, methods like ARIMA, LSTM, or Prophet, complemented by feature engineering, capture temporal patterns. For non-time series data, emphasis is on feature extraction and employing models like Isolation Forest, One-Class SVM, LOF, or clustering methods. Adapting these techniques according to data types ensures effective anomaly identification leveraging machine learning.

Time Series and Machine Learning: Proceed with Caution ML techniques applied to temporal datasets rely on autocorrelation to make predictions (i.e., they use past values to predict the future). This only works insofar as all variables involved in the predictive analysis are stationary. Common metrics such as R-squared fail to identify bogus ML models for time series prediction. For instance, I can create a time series dataset using a random walk (100% stochastic) as a training dataset, and an ML engineer may then use an LSTM model to predict future values for dataset and can be easily fooled into thinking he’s discovered a highly predictive function that was trained on a random walk. Autocorrelation only works on stationary time series.

Time series forecasting, leveraging sequential data to predict future values, has profound implications for wound care. By analyzing a wound's healing trajectory based on historical data, clinicians can project its future healing rate and adjust treatments accordingly. This predictive approach aids in optimizing therapy plans, anticipating potential complications, and setting accurate recovery benchmarks. For patients, this means a more tailored care pathway, potentially speeding up recovery and enhancing the quality of care. Integrating time series forecasting into wound care showcases the potential of AI to revolutionize traditional medical practices.

This is an interesting one as time series forecasting has been accomplished by actuaries for hundreds of years. A modern approach I enjoy is using an ensemble model: use a blend of established techniques by professional bodies with newer models like LightGBM. Inspect the results. Where do the models align? Where do they differ? Why? A solution can be reached more confidently when approached from a variety of angles.

It is crucial to be extra careful when dealing with time series forecasting, as future target values are inherently dependent on past data. Therefore, employing standard techniques like random data shuffling, cross-validation, and relying solely on basic model accuracy metrics can inadvertently lead to data leakage. This can ultimately result in a model that appears accurate and efficient on the surface but lacks predictive capability in reality.

Having experience in time series forecasting and anomaly detection, I've found Prophet to be exceptionally beneficial for time series forecasting tasks. It stands out due to its capability to handle various complexities inherent in time series data. Prophet adeptly manages intricate patterns like seasonality, holidays, and abrupt shifts in trends or anomalies. Its automated pattern detection, flexibility in modeling diverse data types, and user-friendly nature make it an invaluable tool, ensuring precise and interpretable forecasts.

Coding, debugging & documentation use cases: Machine Learning can help with coding & debugging with a natural language interface. The recent availability of the OpenAI GPT3.5 model that lies underneath GitHub Copilot has the ability to receive prompts such as "write python code to sort N numbers" and the language model underneath trained with millions of lines of code from the entire GitHub repository is able to translate the request into python code. It can also assist in debugging by automatically writing test cases, unit tests, etc. Another big task these tools can do is automatically produce documentation for the code, which can help easily understand what the code does to other stakeholders. This improves productivity in a big way!

BEWARE: Machine Learning Charlatans “Forecasting” Financial Market Prices ML algorithms typically require stationary features. The reason is that we need to map a previously unseen observation to a collection of labeled (seen) examples, and infer from them the label of that new observation (relying on autocorrelation). But stationarity does not ensure predictive power. Stationarity is a necessary, non-sufficient condition for the high performance of an ML algorithm. The problem is, there is a trade-off between stationarity and memory. We can always make a series more stationary through differentiation, but it will be at the cost of erasing some memory, which will defeat the forecasting purpose of the ML algorithm.

Machine learning (ML) is still in its early stages, making it the perfect time for learning and specialization within a specific application, niche, or industry. Years ago, the evolution was dependable on the continuous interaction between scientists and ML models. However, in the present and coming years, we will witness an accelerated and sophisticated evolution of ML, because it possesses the ability to autonomously enhance itself over time.