There are several key areas you should focus when learning about MLOps. Here’s a breakdown of the topics you should consider:
Source: https://ml-ops.org/content/mlops-principles
- Machine Learning Algorithms: Gain a solid understanding of various machine learning algorithms, such as linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, naive Bayes, clustering algorithms (k-means, hierarchical clustering), and dimensionality reduction techniques (principal component analysis, t-SNE). Learn how these algorithms work, their strengths, weaknesses, and when to apply them.
- Large Language Models (LLM): Familiarize yourself with large language models, which are powerful models trained on vast amounts of text data. Some prominent examples include OpenAI’s GPT models (like GPT-3) or models like BERT, XLNet, and GPT-2. Understand how these models are pre-trained on massive corpora and can be fine-tuned for specific tasks such as natural language understanding, text generation, or sentiment analysis.
- Statistical Modeling: Acquire a strong foundation in statistical modeling techniques. Learn about probability theory, statistical distributions (e.g., Gaussian, Poisson), hypothesis testing, confidence intervals, regression analysis (linear regression, logistic regression), time series analysis, and Bayesian statistics. Familiarize yourself with statistical software tools like R or Python’s statistical libraries (e.g., scipy, statsmodels).
- Data Manipulation and Analysis: Develop skills in data manipulation and analysis. Learn how to clean and preprocess data, handle missing values, perform feature engineering, and work with structured and unstructured data. Gain proficiency in data analysis libraries such as pandas and data visualization libraries like Matplotlib or Seaborn.
- Programming: Master a programming language commonly used in machine learning and data analysis, such as Python or R. Learn the fundamentals of the language, control structures, data types, functions, and libraries relevant to machine learning and statistical modeling (e.g., scikit-learn, TensorFlow, PyTorch).
- Mathematics and Probability: Strengthen your knowledge of mathematical concepts relevant to machine learning, such as linear algebra, calculus, and probability theory. Understand matrix operations, differentiation, optimization algorithms (gradient descent), and probability distributions.
- Experimental Design and Evaluation: Learn about experimental design principles and how to evaluate machine learning models. Gain knowledge of techniques for cross-validation, model selection, performance metrics (accuracy, precision, recall, F1-score), and overfitting/underfitting.
Additionally, staying updated with the latest research papers, attending relevant workshops or conferences, and engaging in hands-on projects will help you deepen your understanding and practical skills in these areas.