What are the stages of an applied ML project?

The important things here are: - to translate business expectations into evaluation metrics that can be optimized via ML algorithms. - reflect on the availability of data to solve that problem ( and if the data is not available, the collection process); - ethical considerations about using data should be also considered and documented here; - documentation: having a clear documentation on the goal and the specs;

Insightful article that comprehensively outlines the stages of an applied ML project. However, I'd argue that the section on 'Model Evaluation' could benefit from a deeper dive into explainability and interpretability, critical factors often sidelined. Understanding why a model makes a particular prediction is invaluable, both from an ethical standpoint and for iterative improvement. Additionally, the article could touch upon 'Drift Monitoring' under the model maintenance stage to ensure sustained model accuracy over time.

It is oftentimes very tempting to look at a problem from an engineering perspective first, it is just exciting to start a new project and test a new idea out. This, however, can and - more often than not - does lead to projects failing or not living up to users’ expectations. Instead, a priority in this stage is to understand the problem the users are facing, what is the impact of the problem on their day to day work, what does a good solution look like and how do you know when the users’ needs have been addressed.

Domain knowledge and liaising with stakeholders is key here. You wouldn't have a problem without your stakeholders and you need them to clearly define the problem so that you may be able to make a decision. Having follow up meetings and constant learning about the problem gives you an edge as it helps you determine the approach to take. This forms the foundation of your task and without it, you are bound to fail in addressing the issue. Key takeaways: - Ensure constant communication with the stakeholders. - If need be, have someone at arm's length who will help with the domain knowledge - Research further on similar problems and how ML practitioners handled them.

A very important stage in an applied ML project is asking the questions - how will the model be served to customers? a model per user or one model for very user. - How will the ML project integrate with other foundational software systems or the existing software system? A few things to consider here is monolithic intgration, single service or microservice integration Model deployment is a very crucial stage because it can make or marr the efforts of the entire development process.

Data collection is a pivotal step in any machine-learning project. It involves sourcing data from databases, APIs, web scraping, surveys, or sensors. The collected data must be relevant, cleaned, and labeled for supervised tasks. Quality checks for missing values and outliers are vital. Privacy and compliance with regulations are crucial. Proper data splitting and documentation ensure model evaluation and reproducibility. Additionally, to earn a profile badge, contribute to more articles on diverse machine learning topics, establishing your expertise and credibility.

The data collection stage is important for obtaining quality training data that enables developing an accurate machine learning model, requiring identifying relevant data sources, acquiring the data, preprocessing it into a suitable format, and validating that the data meets the requirements of the defined problem.

After problem definition, we move on to data collection and preparation. In this stage, we identify data sources and gather relevant data. This gathered data can be structured or unstructured and may come from various channels. Data preprocessing is a critical step where we clean, transform, and format the data for machine learning. This includes handling missing values, and outliers, and ensuring data consistency. Proper data preparation is essential to build accurate and robust machine learning models. Data quality directly impacts the model's performance, so this stage requires careful attention.

In the second phase of an applied machine learning project, data collection takes precedence. The data necessary for training and testing the model must be gathered, which may entail obtaining data from diverse origins like databases, APIs, web scraping, surveys, or sensors, depending on the problem at hand. It is imperative to verify that the collected data is pertinent, representative, and dependable for the machine learning task. Preprocessing steps, such as data cleaning, filtering, labeling, or transformation, may be essential to adapt the data appropriately for the model's needs.

Data collection is one of the initial & crucial stages in an applied ML project. It includes gathering relevant & high-quality data from various sources, which serves as the foundation for model development. It requires a clear understanding of the project's objectives and the types of data needed to address those objectives. Additionally, I would say, considerations for data privacy, security, and compliance with regulations becomes critical here. An Effective data collection involves data scraping, data storage, and often data preprocessing steps to ensure data cleanliness and quality. The success of the entire project heavily depends on the data collected during this stage, making it a critical step in the ML project lifecycle.

In the third phase of an applied machine learning project, data analysis plays a crucial role. This stage entails a comprehensive examination of the collected data to discern its attributes, trends, and interconnections. The process involves delving into the data through the use of descriptive statistics, visual representations, and summary reports. Additionally, it is essential to pinpoint any data-related problems or complexities, such as missing data points, outliers, class imbalances, or biases. The insights gained from data analysis serve as a foundation for guiding model design and feature engineering decisions.

In the data analysis phase of an ML project, the collected data is explored and visualized to gain insights into its characteristics, distributions, and potential patterns. Statistical techniques and data visualization tools are used to uncover relationships and anomalies. This stage helps in making informed decisions regarding feature selection, data transformations, and potential issues that may affect the model's performance, contributing to a more effective model development process.

In the data analysis stage of an applied ML project, we dig deeper into the collected and prepared data to gain insights and a better understanding of its characteristics. This involves various tasks, such as exploratory data analysis (EDA) and statistical analysis which helps us visualize and summarize data, and identify patterns. Data analysis also includes feature selection, where we decide which features are most relevant for the machine learning task. It provides the foundation for making informed decisions about model selection, evaluation metrics, and preprocessing steps. This stage is essential for guiding the subsequent phases of the project.

Thorough data analysis and understanding are foundational to training machine learning models that capture meaningful relationships in the data to make accurate predictions.

Data analysis is the crucial third stage in an applied ML project. Here, you delve into the collected data to uncover its inherent characteristics, patterns, and interrelationships. This process encompasses data exploration through descriptive statistics, visual aids, and summary tables. It is essential to pinpoint any data-related challenges, including missing values, outliers, imbalances, or biases. Through data analysis, you'll extract valuable insights that guide your model development and feature engineering.

- Decision with tooling i.e., Decision on packages / library need, how would the UX design look like, - Cross validation data split. how would you evaluate performance on the rest of your training data - how to design an explainability framework for the end user to understand the decision making process and enable trust with the decision making process

The fourth stage of an applied ML project is to design the model. It is the process of designing and building the machine learning model that will be used to solve a specific problem. This involves choosing an appropriate machine learning algorithm or model architecture based on the nature of the problem. Common choices include decision trees, random forests, support vector machines, neural networks, and more. The selection should consider factors such as the problem type (classification, regression, clustering, etc.) and the data characteristics.

Model design is a critical phase that demands a balance of expertise & precision. It involves selecting suitable ML algorithms based on problem definition & data analysis, considering factors like linear regression or neural networks. Defining model parameters & hyperparameters accurately is essential to prevent overfitting or underfitting. Striking the right balance ensures accurate predictions & prevents computational overload, vital for scalability in real-world applications. A well-designed model not only delivers precise predictions but also forms the project's foundation, offering actionable insights. Attention to detail and a thorough understanding of the problem domain are paramount, shaping the project's trajectory toward success.

In the model design stage of an applied ML project, the focus is on creating a model that learns from the data and provides predictions or recommendations. This entails selecting the suitable ML algorithm, like linear regression, decision tree, neural network, or k-means clustering, based on the problem definition and data analysis. Additionally, defining model-specific parameters such as layer count, node configuration, and hyperparameters like learning rate and regularization plays a crucial role, as it significantly impacts the model's performance and complexity.

At this stage, the blueprint for the predictive model is formulated. In my view, it includes crucial decisions about the choice of algorithms, model architecture, & feature selection. Based on the insights gained from analysis, the most suitable ML algorithms are selected. The model's architecture, including the number of layers, nodes, and activation functions for neural networks, is discussed. Also, feature engineering techniques may be applied to create new features or transform existing ones to enhance the model's predictive power. The goal in this stage is to design a model that can effectively learn from the data and make accurate predictions. Design sets the foundation for steps like model training, evaluation, & deployment

The model training stage involves feeding the prepared training data into the designed architecture to learn the parameters or patterns that minimize prediction error, using validation data to tune hyperparameters and avoid overfitting. Proper training data splitting, cross-validation, regularization, and early stopping produce an accurate machine learning model that generalizes well to new data.

The fifth stage of an applied ML project is to train the model. The goal is to find a set of model parameters that minimize the loss function and produce accurate predictions. We divide our dataset into three subsets: a training set, a validation set, and a test set. In this phase, we use our prepared data to teach a machine-learning model to make predictions. We do this by adjusting the model's parameters to fit the data and learn patterns, relationships, and features in the data so that it can make accurate predictions on new, unseen data.

Here are some tips: 1. Consider dynamic split for time-sensitive data for realistic training and better generalization. 2. Data Augmentation: Expand training datasets artificially in fields like image and speech recognition for diverse learning. 3. Transfer Learning: Start with pre-trained models and fine-tune them to save time and resources, when possible. 4. Anomaly Detection: Implement it during training to handle outliers that might affect performance. 5. Custom Loss Functions: Tailor loss functions to focus on specific training aspects, especially in imbalanced datasets. 6. Resource Optimization: Use model quantization and pruning for efficient size and speed, ideal for limited-resource environments.

In the model training stage of an applied ML project, the collected and preprocessed data is used to train the model. This process includes providing the data to the model, fine-tuning its parameters to minimize errors or maximize accuracy. Data is typically split into training, validation, and test sets, and techniques like cross-validation are employed to prevent overfitting or underfitting. The quality of model training influences its ability to generalize to new, unseen data.

Here, the selected ML model is fed with the prepared data to learn & adapt its parameters to make predictions. The model is trained on the training dataset, which typically consists of labeled examples where the target variable is known. and Training includes optimizing the model's parameters to minimize the difference between its predictions & the actual target values. The process may need several iterations, especially when hyperparameter tuning is involved. Model training is computationally intensive and may be empowered by hardware accelerators like GPUs. Later, the trained model serves as the foundation for making predictions on new, unseen data and is a central component in the success of the machine learning project.

Model evaluation is pivotal in ML. Here's how to maximize its value: Prioritize Early Checks: In uncertain projects, evaluate outcomes swiftly. Even a basic model can set a vital baseline, guiding progress and maintaining momentum. Embrace Simplicity: Veer away from complex metrics when basics like precision and recall suffice, especially in tasks like object detection. They’re intuitive and communicate performance effectively. Use the Baseline as a Guidepost: Compare every outcome against an established baseline. This anchors results, showing both achievements and areas needing iteration.

The model evaluation stage involves thoroughly assessing model performance on unseen test data using relevant evaluation metrics aligned with the problem definition to quantify accuracy, generalizability, and reliability compared to project goals and baselines. Rigorous model evaluation highlights model strengths, flaws, biases, and limitations to determine if additional iteration on data, features, or modeling is required before deployment.

Model evaluation is the project's reality check, akin to a report card for the machine learning creation. It rigorously assesses how well the model performs, separating the stars from the stumbles. Just as a skilled judge critiques a performance, model evaluation ensures that the predictive creation is fine-tuned, reliable, and ready to shine in real-world scenarios.

In the model evaluation stage of an applied ML project, the model's performance is assessed using the test set, A/B testing, or user feedback. This assessment includes measuring performance with relevant metrics like accuracy, precision, recall, F1-score, or mean squared error, aligning with the project's objectives. Comparisons are made with baseline or expected outcomes, and the model's errors and limitations are analyzed. Model evaluation highlights both strengths and weaknesses, offering insights for potential improvements.

In the sixth stage of an applied ML project, model evaluation takes the spotlight. Here, you assess your model using the test set, A/B testing, or user feedback. By measuring performance with relevant metrics like accuracy, precision, recall, F1-score, or mean squared error, aligned with your objectives, you gain insights into how well your model fulfills its purpose. Additionally, comparing results to baselines and analyzing errors highlights your model's strengths and areas for enhancement, shaping the path for improvements.

In my experience, it's crucial to understand the deployment infrastructure fully. Whether it's a serverless setup, an edge device, or a cloud platform, each has its own challenges that can affect model performance but can also save you time and resources. However, there's no need to reinvent the wheel. Established services like Azure ML, AWS SageMaker, and others simplify the process of deploying ML models, providing tools for seamless management and scalability. When transitioning from model training to deployment, tools like ONNX or Pickle become essential due to their standardized encapsulation and model conservation. They ensure consistent model serialization, preserve configurations, and facilitate model reuse across various platforms!

We need a deployment platform : cloud platform (AWS SageMaker, Azure Machine Learning Studio, Google Cloud AI Platform) or on-premises platform (Kubernetes, Docker, TensorFlow Serving) Model deployment tools: MLflow, BentoML, TorchServe Secondly, packaging the model. Converting the model to a TensorFlow SavedModel or ONNX format. And then these stages can be said: Deploy the model, Monitor the model, Redeploy the model (As new data becomes available, it is important to retrain the model and deploy the updated model).

In the seventh stage of an applied ML project, model deployment takes center stage. This involves seamlessly integrating the model into the target environment, be it a web app, mobile app, or cloud service, making it accessible to end users. The process includes ensuring compatibility, scalability, and security within the existing system. Ongoing monitoring, feedback collection, and necessary updates guarantee the model's continued real-world usefulness, providing solutions to real-world problems.

The model deployment stage involves seamlessly integrating the validated model into the production environment through robust engineering that ensures scalability, reliability, and compatibility with existing systems and workflows.

In the model deployment stage of an applied ML project, the model is placed into the intended environment, whether it's a web app, mobile app, or cloud service, for end-users or customers to utilize. This process entails integrating the model with the existing system, ensuring compatibility, scalability, and security. Continuous monitoring of model performance, collecting user feedback, and making necessary updates are crucial aspects of this stage. Model deployment makes the ML model accessible and functional for solving real-world problems.

In the final stage of an applied ML project, model maintenance is crucial to ensure the model's ongoing reliability and relevance. This process includes quality checks, bug fixes, and periodic retraining with fresh data. Additionally, the model's objectives, metrics, and assumptions are reviewed, and adjustments or enhancements are made as needed. Model maintenance ensures that the ML model remains effective and efficient in addressing evolving problems and data.

Model should be monitored periodically and systematically and adjusted when it is needed. ?? Usually, Model Validators take part in this process. The model should be validated anually or semi-annually even if there are no changes made. ?? The Data should be updated, the latest data should be added. ?? It is good to have a dynamic and real-time dashbord that shows the results of metrics like GINI, MSE, R-squared, F1, etc. ?? Also back-testing results will provide us real insight about how our model works. ?? Sometimes we will just let the model retire. ?? Additionally, take into cosinderation that every major and minor change is a reason for validation again. External or internal validators should verify the model's reliability.

Model Maintenance (MM) is a crucial but often overlooked stage in the lifecycle of an applied ML project. Once a model is deployed & operational, it needs ongoing attention and care. Here, it includes monitoring the model's performance in the production environment, ensuring that it continues to make accurate predictions as data distributions evolve. Maintenance also includes updating the model to adapt to changing data patterns or business needs, addressing issues like concept drift, & retraining the model as necessary. Also, MM includes regular audits for ethical & compliance considerations. I would say a well-managed MM processes ensures the sustained effectiveness & relevance of ML models throughout their operational lifetimes.

Setting up drift monitors is a great way to identify potential problems with the model early before it starts to impact the business. Other than that, automatic retraining can also be setup for training with newly collected/generated data.

Model maintenance, the final stage of an applied ML project, ensures the model's ongoing relevance and efficiency. Regular quality checks, bug fixes, and retraining with new data are vital to preserve accuracy. Continuous review of objectives, metrics, and assumptions allows alignment with evolving needs. This iterative process safeguards the model's effectiveness, ensuring it remains a valuable solution for changing real-world problems. Model maintenance is a commitment to excellence, reflecting dedication to delivering consistent and reliable results, essential for the project's long-term success.

Remember that review, feedback and documentation are a continuous part of development. However, ensure you have concrete documentation post data analysis and design to get feedback. In addition, after each model experiment is complete hold a meeting to review results and get feedback on hacks/tricks to possible improve model or engineer features

The most important stage of any applied ML project would be to get the end user to engage with the feature. Convincing the end user to change their way of working to accommodate a new process and share their feedback at the same time on a project that may not deliver high accuracy results in the initial versions is probably the biggest challenge any ML team faces. Conducting demos through the development cycle, getting the user excited about the new feature with the promise of better future versions is an important part of the process.

- Frame the problem and look at the big picture. - Get the data. - Explore the data to gain insights. - Prepare the data to better expose the underlying data patterns to Machine Learning algorithms. - Explore many different models and short-list the best ones. - Fine-tune your models and combine them into a great solution. - Launch, monitor, and maintain your system using MLOps

The industry's growing interest in artificial intelligence methodologies has been accompanied by a formalization of the solution development process. This phenomenon is particularly notable in the area of intelligent data analysis, and therefore, in the area of machine learning. CRISP-DM remains the most recognized methodology, particularly in the industry, for managing intelligent data analysis projects. This methodology offers a framework that allows planning the development of a data analysis project based on 6 phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment It is convenient to use a methodology such as CRISP-DM as a guide for the development of any ML project.

Model deployment and poor adherence to the MLOps process is where most Data Science projects go to die. Outside of research and academia, being able to successfully launch, and maintain, an ML model in production is what delivers value to the organization. Without a strong ML delivery process, Data Scientists will find themselves not generating value for the organization, or spending their time maintaining previously-release ML models instead of working on new use cases.