You’re building machine learning models. What’s the best way to get started?

I often find the best way to get started in building machine learning models is to NOT start building machine learning models but to really understand the problem at hand, understand the domain, understand all the factors and variables, understand the data and the challenges associated with the data, understand why this is needed at all, and also understand what are the desired requirements and outcome/output of the model. It is through this understanding of problem motivation and problem formulation that will allow for the building of models in a meaningful manner. Don't rush into the problem.

AI does not apply to all the scenarios. Hence, it is important to first analyze the existing automation & AI needs and identify AI opportunities. However, if you are new as an individual/ company for AI it is recommended to start with small proof of concepts. Identify the opportunities, assess how AI can help, understand the existing data, identify the AI technology and platform, and start building small concept solutions. These concepts can be applied and tested on real data to see how it can improve the way of working. Analyze the results of the AI model and later decide whether to go for full implementation. Giving the start, you can also find opportunities to scale and build more solutions for other departments too.

Learn foundational math, stats, Python. Take online courses on platforms like Coursera, edX, and read "Hands-On ML with Scikit-Learn, Keras, TensorFlow." Gain practical experience with Kaggle projects. Explore algorithms, from basics to advanced. Dive into deep learning with TensorFlow and PyTorch. Refer to library docs (scikit-learn, TensorFlow). Learn Git for version control. Stay updated through conferences, blogs, research. Engage in online communities, collaborate, seek feedback. Embrace continuous learning, taking on progressively complex projects.

The first step to any ML project (and really any project in general) is to figure out what problem you're trying to solve. This may sound simple, but far too often, teams miss this step and want to get right into the project. Following a best practices AI/ML methodology such as CPMAI, the first step is Business Understanding. Figure out the problem you're solving, and then what parts of the problem are best suited for ML. After that, you can move onto data needs.

The best way to get started is to do the classic tutorial so you learn the basic theory, how AI framework works, and start to internalize how to approach the problem. When a Google engineer ask me where to start I always recommend to go through Deep Learning with Python by Francois Chollet the creator of Keras as it is the clearest intro I know. Doing those tutorial will get you up to speed fast. The other one I like is fast.ai which is a totally different style of teaching. Both are a great way to get started. Pick the one that resonate the best on how you learn -- you can't get wrong with either of those.

Once a problem has been identified that requires Machine Learning (ML), the next step is data collection and preparation. This step is crucial to the overall success of the ML implementation. The quality and correctness of the data used in model building determine the quality and accuracy of the resulting model. Bias can also enter at this point if the collected data does not properly represent the predictive groups identified. Therefore, data quality must be double-checked, and diverse teams along with seasoned professionals should be involved in defining the type of data the problem requires and how it can be obtained ethically.

Once your problem is defined, the next step is to gather and prepare the data needed to train your model. This involves collecting relevant datasets, cleaning the data to remove errors or inconsistencies, and performing preprocessing tasks like normalization, feature extraction, and data splitting. Good quality data is key to developing effective machine learning models.

Data Collection: Gather data from reliable sources relevant to your problem. Use keywords to search and identify datasets that align with your model requirements. Data Preprocessing: Clean, label, and format the collected data. Handle missing values, outliers, and imbalances using appropriate techniques. Splitting Data: Divide the dataset into training, validation, and test sets. Ensure a suitable distribution to effectively train and evaluate the model. Quality Assurance: Ensure data quality by validating its reliability and relevance. Remove any irrelevant or noisy data that may impact model performance.

This is an intricate process where we meticulously source data for its quality and relevance to our project's needs. Think of it as curating the ingredients for a gourmet meal – each dataset is selected for its potential to enrich our machine learning models. The transformation of this data is where our technical prowess shines: cleaning, labeling, formatting – each step is carefully executed to align with our model's requirements. This preparation isn't just routine; it's an art form. We're not just splitting data into sets; we're strategizing its use in training, validating, and testing our models, ensuring that each part serves its purpose in the larger scheme. Addressing the quirks of our datasets is something we embrace every day.

The next important step after problem definition is Data collection, cleaning and preparation. This step is also to understand the data as it is a crucial step in machine learning model. This step involves obtaining data, exploring its characteristics (performing EDA), cleaning the data, addition of features and preparing it for use in building and training machine learning model. Key note is the performance of model will depend upon this step.(remember - 'garbage in gives garbage out' and 'validated data in shall give high performance / accuracy of model').

There is no one-size-fits-all when it comes to model selection, and today's increasing complexities call for very thoughtful considerations. This step significantly influences your success of the initiatives. For example you are solving for customer churn prediction use case, and are considering Random Forest vs. Logistic Regression - some of the factors to consider are complexity of underlying patterns, what are the interpretability and predictive accuracy requirements, need for model robustness and computational resources available. Then there would be considerations for ensemble and hybrid approaches to compare.

Generally, we choose a set of models depending on the nature of the machine learning task, be it classification, regression, or clustering, and also based on the data size and the number of features involved. For instance, in medical datasets where we often have a smaller number of records, sometimes fewer than 100, we usually opt for Support Vector models, such as a classifier or regressor. This is because Support Vector Machines (SVM) tend to perform better with smaller datasets. On the other hand, for larger datasets, neural network-based models are more appropriate.

Selecting a suitable ML model is crucial for effective problem-solving. Linear regression is ideal for predicting continuous outcomes, while logistic regression is suitable for binary classification problems. Decision trees are versatile for both classification and regression, offering interpretability. Neural networks excel in handling complex patterns and large datasets but may lack interpretability. Support vector machines are powerful for classification tasks, especially in high-dimensional spaces. Consider the complexity, interpretability, and scalability of each model, aligning with your problem's nature and data characteristics. Evaluate performance metrics to ensure the chosen model meets your goals.

3 tips for choosing the right ML model or algorithm: 1. Consider various types of ML models (e.g., linear regression, logistic regression, decision trees, neural networks, support vector machines). 2. Match the model to your problem type, data characteristics, and performance metrics. 3. Take into account factors such as model complexity, interpretability, and scalability when selecting a model.

Diving into an AI project, the task of selecting the right ML model is a pivotal moment in our startup's process. It's like choosing the right tool for a job, where each with its unique strengths and limitations. Selecting the right model in our AI projects is akin to charting a course in uncharted waters. It requires not just technical knowledge but also strategic thinking, ensuring that our choice aligns with our immediate project needs and long-term vision. It’s a decision that underscores our commitment to building AI solutions that are not only technically proficient but also strategically sound. We have tried many of the models suggested in the prompt; like vector, linear regression, and others. Vector has been good to us thus far.

Training and evaluating a model involves adjusting parameters to minimize training data error. Techniques like regularization, cross-validation, and early stopping mitigate overfitting or underfitting. Evaluation metrics include accuracy, precision, recall, and F1-score on validation and test data. Regularization prevents excessive parameter weights, cross-validation assesses generalization performance, and early stopping halts training when validation performance plateaus. Balancing bias-variance trade-off enhances model robustness. Optimal hyperparameters and architecture are crucial. Regularly monitor and update models for evolving data patterns.

This is a daily thing for me. Perhaps even when I am sleeping is the model being trained on data. There have been many challenges in the beginning because of the type of AI product I am building and the tendency for some LLM's to extremely hallucinate. I have had to implement different evaluation tools for my metrics and accuracy and am testing a new one as we speak. But its simply part of the deal. It has to constantly be trained here in the beginning, the parameters of the algorithm are constantly being changed, but the accuracy continues to improve. None of this can happen with the consistency of the Two T's: Training & Testing, then rinse and repeat.

Training and evaluating a model is where the true art of machine learning comes into play. It's more than a technical process; it's akin to teaching a student. Just as a good teacher recognizes the unique strengths and weaknesses of each student, a skilled ML practitioner must understand the nuances of their model. When I train models, I approach it as a careful balancing act. It's not just about minimizing error on training data; it's about crafting a model that can generalize well to new, unseen data. This means being vigilant about overfitting, which is like teaching to the test. A model that's too tailored to the training data loses its ability to perform in the real world.

Optimizing your model's performance is crucial. Consider using advanced optimization techniques like Adam, which combines the benefits of AdaGrad and RMSProp, for efficient parameter updates. Additionally, implementing learning rate decay can help avoid overshooting during training. Exploring other optimization strategies, such as SGD or momentum, can also be beneficial, depending on your model's specific requirements.

Machine learning models are akin to students; they require instruction to excel at specific tasks. Training is the heart of this instruction, where a model is fed vast amounts of data to uncover patterns and correlations. This process allows the model to 'learn' how to make predictions or decisions. Evaluation then acts as the critical exam, gauging the model's performance on unseen data. It reveals strengths, pinpoints weaknesses, and guides further refinement. Without training and evaluation, machine learning models cannot effectively learn from data or demonstrate their potential to solve real-world problems. These steps are the foundational building blocks upon which the success of machine learning applications rests.

The deployment and monitoring of a machine learning model is not simply an endpoint within the development process, but rather represents the crucial first step in the realization of a model's true potential. Deployment brings the model into the real world, where it can deliver tangible value by solving problems and offering insights. Monitoring provides vital feedback on its performance, identifying areas for improvement and guarding against degradation. This continuous feedback loop allows for refinement and enhancement, ensuring that the model evolves to meet changing needs. Thus, deployment and monitoring create a foundation for iterative development, ultimately driving the creation of more effective and impactful ML models.

Plan for scalability to accommodate varying workloads. Determine whether your deployment strategy can handle an increase in the number of requests and data volume. Cloud-based solutions often offer auto-scaling capabilities for efficient resource allocation. and Establish a feedback loop that allows users to provide feedback on the model's predictions. User feedback is valuable for understanding real-world performance, identifying edge cases, and refining the model based on practical insights.

Robust and scalable model deployment ensure minimum issues in production environment. - Ensure precise CI/CD (ML Ops) cycles to automate non-creative tasks. - Plan scalability and robustness measures to ensure seamless model deployment. - Ensure proper mechanism for documentation, logging, alerts and audits - Monitoring of model and/or data drift is crucial. Ensure robust pipelines for model retraining. - Regular security and compliance audits will ensure the model performance is under guidelines.

Deploying your model puts it into practical use, typically through cloud services or APIs. It's crucial to continuously monitor its real-world performance, ensuring it remains accurate and efficient. Monitoring involves logging predictions, collecting user feedback, and setting alerts for anomalies. Stay responsive by updating and retraining the model with fresh data to maintain its relevance and accuracy, thereby delivering consistent, valuable insights or actions in your production environment.

This is perhaps the most crucial part in the life cycle of a machine learning model. Here are some tips I would recommend: 1. Deploy your model on cloud and plan ahead for scalability. 2. Make use of tools like ML flow so you can properly use version control. 3. Monitor your model's performance and set automatic triggers for finetuning or retraining. 4. Take user feedback to adjust and retrain your model according to user requirements.

Google Cloud AI, Amazon SageMaker, Azure ML, and IBM Watson Studio all have value. They are both a gift and a headache. They're like having a team of experts guiding us through each step but sometimes giving TOO much information, which then costs more time to sift through. However, they allow for AI democratization and a big part of our mantra. It not only accelerates our project timelines but also aligns perfectly with our startup's ethos of innovation and agility.

Don't reinvent the wheel. Leverage the power of existing ML platforms like Amazon Sagemaker or Huggingface. These platforms are designed to simplify and accelerate your ML modeling process. They come equipped with a plethora of tools and features like data preprocessing, model training, validation, and testing. They also provide you with a space to experiment, collaborate, and improve. In the case of hugging face, it also offers a big sandbox to see how others are testing different AI models, and creating interesting and valuable use cases.

Say goodbye to technical woes and infrastructure worries. These platforms, like Google Cloud AI Platform, Amazon SageMaker, Microsoft Azure Machine Learning, and IBM Watson Studio, have got you covered. ML platforms are game-changers for your ML modeling journey. - Simplify and accelerate the process. - Data collection and preparation made easy. - Choose the right model with ease. - Train and evaluate models efficiently. - Deploy and monitor your models effortlessly.

The easiest, and often cheapest, way to get started is to use one of the cloud or SaaS platforms available such as Amazon SageMaker, HuggingFace, IBM Watson etc. These have been designed to be approachable and well integrated. If you choose to take your idea further, then you will have to consider costing, data privacy, performance, availability, what the rest of your technical estate is hosted on and security, amongst other things. Try to keep things simple, if you're an Azure house and the Azure ML services meet your needs, then try to remain consistent.

One thing I found helpful is using machine learning platforms. These platforms, like a multi-tool, provide various tools and services that simplify the machine learning workflow, from data preparation and model selection to deployment and monitoring. They also provide scalability and ease of collaboration, which can be particularly useful for large projects or teams. However, it's important to choose a platform that fits your specific needs and budget. #MLPlatforms

First build a solid foundation in Python, math, and statistics, focusing on libraries like NumPy and pandas. Learn key concepts such as supervised vs unsupervised learning, classification, regression, and model evaluation. Practice basic algorithms like linear regression and decision trees using scikit-learn. Work with freely available datasets from platforms like Kaggle, and use Jupyter Notebooks to prototype and visualize results. As you progress, explore neural networks with frameworks like TensorFlow and PyTorch. Engage in online communities and competitions to apply your skills. Stay patient, as mastering machine learning takes time and practice.

Building machine learning models effectively and efficiently often involves integrating best practices not only in model development but also in managing the lifecycle of these models. This is where MLOps, or Machine Learning Operations, becomes crucial. We need to enforce ML model's lifecycle : data collection, model development, training, evaluation, monitoring and maintenance. MLOps includes automation, continuous integration, testing, and deployment.

Pick a good course to follow! I started out in ML with Andrew Ng's Deep Learning Specialization. Wouldn't have had it any other way. Search for a course that will give you deep and hands-on understanding of the models and data you're working with. And get a good grasp on how simple/more classical models first, before diving into the current state-of-the-art!

Machine Learning has its base in statistics so it is of outright importance that you learn the mathematical and statistical framework behind each model and techniques. You need to understand where we use binary cross entropy and why. The mathematical background ensures a sound reasoning behind your actions. ML is a language and you need intuition to become fluent in it.

Two tips for new AI Engineers building ML models: 1. Make sure you spend time learning the math, concepts, and theory behind machine learning (e.g., linear algebra, statistics, advantages and disadvantages of each algorithm, etc.) before jumping into coding. This will make you an engineer that drives more value to your team rather than just another developer. 2. "Data never lies, but liars use data". Ensure that the data used to train your model is properly randomized, accurate, and cleaned correctly. Data should not be "cherry-picked" to skew training results to a desired outcome but should rather represent a result from which a small variety of outcomes are possible. This ensures re-usability of your model and prevents over-fitting.