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.