Gave a deep thought and decided I'll document this journey, there are two reasons behind blogging this journey, one is to stay consistent and the second is to have something as BTS in life so that when I look back at myself from 3-5 from now thinking where it all started, I should feel good. So yeah let's dive in 🚤
before we dive in these articles are going to be in a very dry format (no additional make up of paraphrasing ,asking AI to write a blog , etc.) so keeping that in mind i'll try to communicate as good as possible with no spelling mistakes .
now the time is around 2230hrs and it's been 5 hours already after researching all the required things to get started with this exciting journey, I've researched how to approach MLops, curriculum for MLops, curriculum for machine learning, I've used chatGPT for 90% of my research as it has all the knowledge of millions if no billions of webpages. and a few blogs to know the experience of being an MLops engineer. There are different job profiles involved in MLops such as MLops engineer, Machine Learning Engineer, data engineer, DevOps Engineer, Data Scientist, AI/ML Architect, ML Operations Manager and chat GPT explains each profile as follows :
MLOps Engineer: MLOps engineers focus on implementing and maintaining the infrastructure, tools, and processes required for deploying and managing machine learning models in production. They work on developing CI/CD pipelines, monitoring systems, and automation frameworks to streamline the deployment and operational aspects of machine learning models.
Data Engineer: Data engineers play a crucial role in MLOps by managing data pipelines, data infrastructure, and data storage systems. They are responsible for data ingestion, data preprocessing, feature engineering, and ensuring the availability and quality of data for training and inference.
Machine Learning Engineer: Machine learning engineers focus on developing and optimizing machine learning models for deployment in production. They are skilled in algorithm development, model training, hyperparameter tuning, and model evaluation. They collaborate closely with data scientists and software engineers to implement models that meet production requirements.
DevOps Engineer: DevOps engineers bridge the gap between development and operations, focusing on building and maintaining scalable, reliable, and efficient software systems. They handle tasks such as infrastructure provisioning, configuration management, containerization, and orchestration. In MLOps, DevOps engineers ensure smooth deployment and management of machine learning models.
Data Scientist: Data scientists work on the development and improvement of machine learning models. They conduct exploratory data analysis, build predictive models, perform statistical analysis, and validate and optimize model performance. In the context of MLOps, data scientists collaborate with other roles to ensure smooth deployment and monitoring of models in production.
AI/ML Architect: AI/ML architects provide high-level guidance and strategic direction in designing and implementing AI and ML solutions. They define the overall architecture, data flow, technology stack, and integration points for MLOps initiatives. They work closely with other stakeholders to ensure scalable, efficient, and robust AI/ML systems.
ML Operations Manager: ML Operations managers oversee the production's end-to-end management of machine learning models. They coordinate with different teams, set goals, manage resources, and ensure adherence to best practices and compliance requirements. They focus on optimizing the efficiency and effectiveness of MLOps processes and workflows.
now after the job roles I've searched more about what should be learned to get into this field, I don't believe in getting an expert in any field to get started with searching for jobs or contributing to any org. what I believe is getting a brief knowledge of all the things that are involved in the field and then understanding how those things work layer-wise, through exploring the real world org(s). So I later asked Chat GPT to give a list of all the topics to get started. I asked it to give in a curriculum format so that I would now any problem in categorizing the topic (and low key that gives me relief ) it gave the list as follows
I’ve asked chat gpt the best curriculum possible to learn machine learning, and it suggested me this, so if you want you can consider this as well and join me in this journey
Unit 1: Introduction to Machine Learning
Overview of machine learning concepts and Applications
Supervised, unsupervised, and reinforcement learning
Model representation: features, labels, and target variables
Evaluation metrics: accuracy, precision, recall, F1-score
Feature engineering: handling missing data, encoding categorical variables
Data preprocessing: scaling, normalization, outlier detection
Introduction to Python for machine learning: libraries (NumPy, Pandas)
Unit 2: Supervised Learning Algorithms
Linear regression: simple, multiple, polynomial regression
Logistic regression: binary classification, multi-class classification
Decision trees and random forests: tree construction, feature importance
Support Vector Machines (SVM): linear SVM, kernel methods
Evaluation techniques for supervised learning: train-test split, cross-validation
Unit 3: Unsupervised Learning Algorithms
Clustering algorithms: K-means, hierarchical clustering
Dimensionality reduction: Principal Component Analysis (PCA), t-SNE
Association rule mining: Apriori algorithm, frequent itemsets
Evaluation techniques for unsupervised learning: silhouette score, elbow method
Unit 4: Advanced Topics in Machine Learning
Ensemble methods: bagging, boosting, stacking
Neural networks and deep learning: perceptron, backpropagation
Convolutional Neural Networks (CNN) for computer vision: architecture, filters
Recurrent Neural Networks (RNN) for natural language processing: LSTM, GRU
Transfer learning and model fine-tuning: pre-trained models, feature extraction
Unit 5: Applied Machine Learning and Model Deployment
Model evaluation and selection: precision-recall curve, ROC curve
Hyperparameter tuning and model optimization: grid search, random search
Model deployment and product ionization: APIs, model serving
Introduction to MLOps and model lifecycle management: version control, CI/CD
Ethical considerations in machine learning: bias, fairness, interpretability
this is a great curriculum in my opinion I've glanced through all the topics and having machine learning knowledge is a fundamental part of machine learning. I've also curated a syllabus for mlops as well in a similar format , which goes like this:
Unit 1: Introduction to MLOps
Introduction to MLOps and its Importance
Understanding the machine learning lifecycle
Version control and collaboration in MLOps (Git, GitHub, GitLab)
Continuous integration and continuous deployment (CI/CD) pipelines (Jenkins, CircleCI)
Reproducibility and experiment tracking (MLflow, Neptune)
Unit 2: Data Management for MLOps
Data preprocessing and feature engineering
Data cleaning
Data normalization and scaling
Feature selection and extraction
Data storage and retrieval (AWS S3, Google Cloud Storage)
Data versioning and lineage (DVC, Git Large File Storage)
Data quality and monitoring
Data privacy and security in MLOps
Unit 3: Model Development and Deployment
Model selection and evaluation
Model training and hyperparameter tuning (Scikit-learn, TensorFlow, PyTorch)
Model deployment strategies (Docker, Flask, AWS Lambda)
Managing model versions and updates
A/B testing and experimentation
Unit 4: Infrastructure and Scalability
Cloud computing and MLOps (AWS, Google Cloud Platform, Microsoft Azure)
Scalable and distributed training (TensorFlow, PyTorch, Horovod)
Containerization and orchestration (e.g., Docker, Kubernetes)
Resource allocation and monitoring (Prometheus, Grafana)
Autoscaling and load balancing
Unit 5: Monitoring, Maintenance, and Governance
Performance monitoring and drift detection
Error analysis and debugging in MLOps
Model retraining and reevaluation
Ethical Considerations in MLOps
Compliance and governance in MLOps
the tech stack written in brackets implies that those tech tools are used for the the application of preceding topic written beside. so that's it for today. this is going to be an amazing journey, I'll try my level best for sharing one article a day for 6 months straight (math says around 180 blogs). let's see where this journey takes us. I'm very deterministic this time about my goal. we'll also go for Open source contributions, seeking remote jobs, content creation, and building an online community of people who wants to start their journey in data science and AI.
join the newsletter if you are interested in knowing how this journey goes and follow me on Twitter "@lokstwt" and hashnode. Thank you