60‑Day AI‑Engineer Road‑Map

All 60 days are dedicated to learning & building.
No “demo / polish / launch” days – the final week is a continuation of the technical stack.

Structure –

Week – 7 days (or 8 for the last week)

Each day follows the same template

Learn – Core concepts

Code Challenge – 1–2 h hands‑on exercise

Build – Runnable artifact you commit to the repo

How to use

Create a repo my‑ai‑journey.

For each day create a folder day‑<NN> (or keep files in root).

Follow the “Learn → Code Challenge → Build” sequence.

Commit the artifact with a descriptive message.

Update the root README.md with a table of contents and links to each day’s output.

Week 1 – Python & Foundations (Days 1‑7)

Day	Learn	Code Challenge	Build
01	Syntax, variables, data types	`hello.py` – print “Hello” + simple math	`hello.py`
02	Control flow – if / loops	`fizzbuzz.py` – FizzBuzz 1‑100	`fizzbuzz.py`
03	Functions	`utils.py` – factorial & gcd	`utils.py`
04	Modules & `__init__`	`main.py` – use `utils`	`main.py`
05	File I/O	`csv_reader.py` – read CSV, show stats	`csv_reader.py`
06	Error handling & logging	`safe_div.py` – division with error	`safe_div.py`
07	Virtual env & `requirements.txt`	`setup_env.sh` + `requirements.txt`	`requirements.txt`

Week 2 – Data Handling & Visualization (Days 8‑14)

Day	Learn	Code Challenge	Build
08	Pandas basics	`df_demo.ipynb` – load & describe CSV	`df_demo.ipynb`
09	NumPy basics	`numpy_demo.ipynb` – array ops	`numpy_demo.ipynb`
10	Matplotlib 101	`plot_demo.py` – line & scatter plots	`plot_demo.py`
11	Seaborn 101	`sns_demo.py` – dist & pair plots	`sns_demo.py`
12	Plotly 101	`plotly_demo.py` – interactive chart	`plotly_demo.py`
13	Visualisation best‑practices	`vis_guidelines.md`	`vis_guidelines.md`
14	Mini‑project: dashboard	`dashboard.ipynb` – 3 plots + markdown	`dashboard.ipynb`

Week 3 – Statistics & Machine‑Learning Basics (Days 15‑21)

Day	Learn	Code Challenge	Build
15	Probability fundamentals	`prob_demo.py` – dice simulation	`prob_demo.py`
16	Descriptive statistics	`stats_demo.py` – mean, median, std	`stats_demo.py`
17	Hypothesis testing	`ttest_demo.py` – iris t‑test	`ttest_demo.py`
18	Supervised learning intro	`train_simple.py` – linear regression	`train_simple.py`
19	Model evaluation	`eval_demo.py` – MSE, R², residuals	`eval_demo.py`
20	Pipeline & cross‑validation	`pipeline_demo.py` – sklearn pipeline	`pipeline_demo.py`
21	Mini‑project: housing price	`housing_predict.ipynb` – train & evaluate	`housing_predict.ipynb`

Week 4 – Deep Learning Foundations (Days 22‑28)

Day	Learn	Code Challenge	Build
22	NN theory – layers & forward	`nn_theory.md` – diagram	`nn_theory.md`
23	PyTorch basics	`pytorch_demo.py` – tensor ops	`pytorch_demo.py`
24	MNIST Hello‑World	`mnist_simple.py` – 2‑layer NN	`mnist_simple.py`
25	Training loop	`mnist_train.py` – optimizer, loss	`mnist_train.py`
26	CNN basics	`mnist_cnn.py` – conv + pool	`mnist_cnn.py`
27	Regularization	`mnist_dropout.py` – dropout	`mnist_dropout.py`
28	Mini‑project: CIFAR‑10	`cifar10_classifier.ipynb` – train & evaluate	`cifar10_classifier.ipynb`

Week 5 – NLP & Large‑Language‑Models (Days 29‑35)

Day	Learn	Code Challenge	Build
29	Tokenization & embeddings	`token_demo.py` – BPE + sentence‑transformer	`token_demo.py`
30	HuggingFace transformers	`bert_demo.py` – fine‑tune BERT	`bert_demo.py`
31	Sentiment analysis	`sentiment_demo.py` – text classification	`sentiment_demo.py`
32	Prompt engineering	`prompt_demo.md` – prompt design	`prompt_demo.md`
33	Chain‑of‑Thought	`cot_demo.py` – few‑shot prompt	`cot_demo.py`
34	LLM inference API	`llm_api.py` – FastAPI wrapper for GPT‑2	`llm_api.py`
35	Mini‑project: chatbot	`chatbot.ipynb` – conversational agent	`chatbot.ipynb`

Week 6 – Retrieval‑Augmented Generation (RAG) (Days 36‑42)

Day	Learn	Code Challenge	Build
36	Vector embeddings	`embeddings_demo.py` – sentence‑transformers	`embeddings_demo.py`
37	Vector store – FAISS	`faiss_demo.py` – build & query	`faiss_demo.py`
38	Retrieval pipeline	`rag_pipeline.py` – embed + retrieve	`rag_pipeline.py`
39	LangChain RAG	`langchain_rag.py` – build chain	`langchain_rag.py`
40	RAG summarization	`rag_summary.py` – generate answer	`rag_summary.py`
41	LLM fine‑tune on docs	`llm_finetune.py`	`llm_finetune.py`
42	Mini‑project: knowledge‑base assistant	`kb_assistant.ipynb`	`kb_assistant.ipynb`

Week 7 – MLOps Foundations (Days 43‑49)

Day	Learn	Code Challenge	Build
43	Docker basics	`Dockerfile`, `docker-compose.yml`	`Dockerfile`
44	FastAPI deployment	`api.py` – expose RAG service	`api.py`
45	CI/CD with GitHub Actions	`ci.yml` – test & build	`ci.yml`
46	Experiment tracking – MLflow	`mlflow_demo.py`	`mlflow_demo.py`
47	Monitoring – Prometheus & Grafana	`metrics.py`, `prometheus.yml`	`metrics.py`
48	Model registry & versioning	`model_registry.py`	`model_registry.py`
49	Mini‑project: MLOps pipeline	`mlops_demo.ipynb` – end‑to‑end	`mlops_demo.ipynb`

Week 8 – Advanced Topics & Capstone (Days 50‑60)

Day	Learn	Code Challenge	Build
50	Advanced MLOps – canary deploy	`canary_demo.py` – roll‑out strategy	`canary_demo.py`
51	Explainability – SHAP / LIME	`explain_demo.py`	`explain_demo.py`
52	Model compression	`compress_demo.py` – quantization, pruning	`compress_demo.py`
53	Multimodal models	`multimodal_demo.py` – image‑text fusion	`multimodal_demo.py`
54	Reinforcement learning	`rl_demo.py` – DQN on OpenAI Gym	`rl_demo.py`
55	Scaling inference	`scale_demo.py` – GPU cluster & batching	`scale_demo.py`
56	Cloud deployment	`cloud_demo.py` – SageMaker / GCP AI	`cloud_demo.py`
57	Security & privacy	`security_demo.py` – API key, encryption	`security_demo.py`
58	Performance profiling	`profile_demo.py` – PyTorch profiler	`profile_demo.py`
59	Research survey	`papers.md` – summary of two recent papers	`papers.md`
60	Future learning roadmap	`future_roadmap.md` – 3‑month plan	`future_roadmap.md`

Summary

8 weeks, 60 days of hands‑on learning and building.
Every day produces a runnable artifact that you commit to your GitHub repo.
By the end of week 8 you will own a full stack: data ingestion → ML/DL model → RAG + LLM service → MLOps pipeline → advanced topics.
The repo itself becomes your live portfolio and interview‑ready showcase.

Happy coding, and enjoy the sprint!

60‑Day AI‑Engineer Road‑Map

All 60 days are dedicated to learning & building.

Day 1 – Python Basics

Learn
  • Variables & data types
     – int, float, str, bool
     – Naming conventions (snake_case)
  • Syntax fundamentals
     – Indentation, comments, simple expressions
  • Basic I/O
     – print(), input()
  • Simple math
     – Arithmetic operators (+, –, *, /, **, //, %)
     – Order of operations

Code Challenge (≈ 30 min)
  • Create a file `hello.py`
     – print("Hello, World!")
     – Prompt user for a number `n`
     – Print `n * 2`, `n / 3`, and `n ** 2`
  • Test with multiple inputs (positive, negative, zero)

Build (≈ 1 h)
  • Write a command‑line calculator `calc.py`
     – Prompt: “Enter first number: ”
     – Prompt: “Enter operator (+, –, *, /): ”
     – Prompt: “Enter second number: ”
     – Compute and display result
     – Handle division‑by‑zero gracefully
  • Add a help menu: if user types `--help`, show usage
  • Commit `hello.py` & `calc.py` to the repo
  • Add a short `README` snippet explaining how to run the calculator:
        python calc.py

Day 2 – Control Flow & Functions

Learn
  • Conditional statements: if / elif / else
  • Looping: for…in, while
  • Function definition, arguments, return, scope
  • Basic try/except error handling

Code Challenge (≈ 45 min)
  • Implement `fizz_buzz(n)` that prints “Fizz”, “Buzz”, or “FizzBuzz” as appropriate for numbers 1‑100
  • Test with edge cases (0, negative numbers)

Build (≈ 1 h)
  • Build `number_tool.py`: a CLI menu with options to sum, average, find min/max, or exit
  • Validate input, use functions from the challenge
  • Add `unittest` tests in `test_number_tool.py`
  • Commit the CLI, tests, and README updates

Day 3 – Modules & Packages

Learn
  • Importing built‑in & third‑party modules
  • Creating a module (`utils.py`) with reusable functions
  • Using `__init__.py` to make a package

Code Challenge (≈ 30 min)
  • Write `utils.py` containing `factorial(n)` and `gcd(a,b)`
  • Import and use these functions in a `main.py`

Build (≈ 1 h)
  • Commit the package `my_pkg/` and `main.py`
  • Add documentation in `README` on how to import and use the package

Day 4 – File I/O

Learn
  • Reading/writing text files with open()
  • CSV handling via the `csv` module
  • Context managers (`with`)

Code Challenge (≈ 30 min)
  • `csv_reader.py` that reads a CSV, prints header and row count

Build (≈ 1 h)
  • `csv_stats.py` that outputs mean and std of numeric columns
  • Commit the scripts and a sample CSV (`sample_data.csv`)

Day 5 – Error Handling & Logging

Learn
  • Custom exception classes
  • Raising vs catching exceptions
  • Basic logging with the `logging` module

Code Challenge (≈ 30 min)
  • `calculator.py` that raises `ZeroDivisionError` on division by zero and handles it gracefully

Build (≈ 1 h)
  • CLI calculator `calc_cli.py` that accepts bad input and logs errors to a file
  • Commit all code, log file, and README notes

Day 6 – Virtual Environments

Learn
  • Creating and activating a virtual environment (`venv`)
  • Managing `requirements.txt`
  • Pinning package versions

Code Challenge (≈ 15 min)
  • Set up `venv`, install `pandas` and `numpy`

Build (≈ 30 min)
  • Add `requirements.txt` and `setup_env.sh`
  • Commit to repo

Day 7 – Git Basics

Learn
  • `git init`, `git add`, `git commit`, `git push`
  • Branching, merging, rebasing
  • Remote repositories on GitHub

Code Challenge (≈ 30 min)
  • Create a feature branch, commit the code from Day 1–5, merge into `main`

Build (≈ 1 h)
  • Push repo to GitHub, update `README` with repo description
  • Commit the entire history

Day 8 – Pandas Basics

Learn
  • Series & DataFrame creation
  • Indexing, filtering, aggregation

Code Challenge (≈ 30 min)
  • Load a public CSV into a DataFrame, compute mean of a numeric column

Build (≈ 1 h)
  • `df_demo.ipynb` showing EDA on the dataset
  • Commit notebook

Day 9 – NumPy Basics

Learn
  • Array creation, broadcasting
  • Indexing, slicing, reshaping
  • Element‑wise operations

Code Challenge (≈ 30 min)
  • Create a 2‑D array, compute row sums & transpose

Build (≈ 1 h)
  • `numpy_demo.ipynb` with visualizations
  • Commit notebook

Day 10 – Matplotlib 101

Learn
  • Plotting basics: line, scatter, bar
  • Axis labels, titles, legends
  • Saving figures

Code Challenge (≈ 30 min)
  • `plot_demo.py` – plot a sine wave and a histogram

Build (≈ 1 h)
  • Commit `plot_demo.py` and generated PNG

Day 11 – Seaborn 101

Learn
  • Distribution plots: KDE, histogram, box
  • Pair plots & heatmaps
  • Styling and themes

Code Challenge (≈ 30 min)
  • `sns_demo.py` – KDE of a numeric column and a pairplot of 3 columns

Build (≈ 1 h)
  • Commit `sns_demo.py` and generated plots

Day 12 – Plotly 101

Learn
  • Interactive charts: scatter, line, heatmap
  • Dashboards with layout & callbacks
  • Exporting to HTML

Code Challenge (≈ 30 min)
  • `plotly_demo.py` – interactive scatter plot of a dataset

Build (≈ 1 h)
  • Commit `plotly_demo.py` and `plotly_demo.html`

Day 13 – Visualisation Best‑Practices

Learn
  • Choosing the right chart type
  • Color palettes & accessibility
  • Storytelling with data visualisation

Code Challenge (≈ 30 min)
  • Write `vis_guidelines.md` summarising 5 key rules

Build (≈ 1 h)
  • Commit `vis_guidelines.md`

Day 14 – Mini‑Project: Data Dashboard

Learn
  • Combining multiple plots in a single notebook
  • Adding Markdown explanations

Code Challenge (≈ 45 min)
  • Build `dashboard.ipynb`: 3 plots (line, bar, heatmap) + Markdown summary

Build (≈ 1 h)
  • Commit `dashboard.ipynb`

Day 15 – Probability Fundamentals

Learn
  • Sample space, events, probability rules
  • Conditional probability

Code Challenge (≈ 30 min)
  • `prob_demo.py` – calculate probability of rolling a 4 on a fair die

Build (≈ 1 h)
  • Commit `prob_demo.py` and results

Day 16 – Descriptive Statistics

Learn
  • Mean, median, mode, standard deviation
  • Quartiles, inter‑quartile range

Code Challenge (≈ 30 min)
  • `stats_demo.py` – compute stats for a list of numbers

Build (≈ 1 h)
  • Commit `stats_demo.py` and printed output

Day 17 – Hypothesis Testing (t‑Test)

Learn
  • One‑sample vs two‑sample t‑test
  • p‑value interpretation

Code Challenge (≈ 30 min)
  • `ttest_demo.py` – perform t‑test on Iris sepal width

Build (≈ 1 h)
  • Commit `ttest_demo.py` and results

Day 18 – Supervised Learning Intro (Linear Regression)

Learn
  • Least‑squares solution
  • Normal equation

Code Challenge (≈ 30 min)
  • `train_simple.py` – implement linear regression from scratch on a toy dataset

Build (≈ 1 h)
  • Commit `train_simple.py` and demo run

Day 19 – Model Evaluation

Learn
  • MSE, MAE, R²
  • Residual analysis

Code Challenge (≈ 30 min)
  • `eval_demo.py` – evaluate the linear regression model

Build (≈ 1 h)
  • Commit `eval_demo.py` and plots

Day 20 – Pipeline & Cross‑Validation

Learn
  • Scikit‑learn Pipeline
  • K‑fold cross‑validation

Code Challenge (≈ 30 min)
  • `pipeline_demo.py` – create a pipeline that scales + regresses

Build (≈ 1 h)
  • Commit `pipeline_demo.py` and output

Day 21 – Mini‑Project: Housing Price Prediction

Learn
  • Regression on tabular data
  • Feature engineering

Code Challenge (≈ 45 min)
  • `housing_predict.ipynb` – load Boston Housing, train, evaluate

Build (≈ 1 h)
  • Commit `housing_predict.ipynb`

Day 22 – Neural‑Network Theory – Architecture

Learn
  • Neurons, layers, activations
  • Forward propagation

Code Challenge (≈ 30 min)
  • `nn_theory.md` – diagram a 3‑layer perceptron in Markdown

Build (≈ 1 h)
  • Commit `nn_theory.md`

Day 23 – PyTorch Basics

Learn
  • Tensors, device handling
  • Basic arithmetic & broadcasting

Code Challenge (≈ 15 min)
  • `pytorch_demo.py` – create a tensor, move to GPU if available

Build (≈ 30 min)
  • Commit `pytorch_demo.py`

Day 24 – MNIST “Hello World” (2‑layer NN)

Learn
  • Dataset loading with `torchvision`
  • Simple feed‑forward network

Code Challenge (≈ 30 min)
  • `mnist_simple.py` – train a 2‑layer NN, achieve >95 % accuracy

Build (≈ 1 h)
  • Commit `mnist_simple.py` and trained model

Day 25 – Training Loop, Optimizer & Scheduler

Learn
  • PyTorch training loop
  • Adam optimizer, learning‑rate scheduler

Code Challenge (≈ 30 min)
  • `mnist_train.py` – add Adam, scheduler, and log loss

Build (≈ 1 h)
  • Commit `mnist_train.py` and training logs

Day 26 – Convolutional Neural Networks (CNN)

Learn
  • Convolution, padding, stride
  • Pooling, activation

Code Challenge (≈ 30 min)
  • `mnist_cnn.py` – build a 3‑layer CNN, train on MNIST

Build (≈ 1 h)
  • Commit `mnist_cnn.py` and model artefacts

Day 27 – Regularization (Dropout, Weight Decay)

Learn
  • Dropout, weight decay
  • Early stopping

Code Challenge (≈ 30 min)
  • `mnist_dropout.py` – add dropout to CNN, compare accuracy

Build (≈ 1 h)
  • Commit `mnist_dropout.py` and comparison plots

Day 28 – Mini‑Project: CIFAR‑10 Image Classifier

Learn
  • Transfer learning with pre‑trained models
  • Fine‑tuning on small datasets

Code Challenge (≈ 45 min)
  • `cifar10_classifier.ipynb` – load CIFAR‑10, fine‑tune ResNet‑18

Build (≈ 1 h)
  • Commit `cifar10_classifier.ipynb` and trained weights

Day 29 – Tokenization & Sentence Embeddings

Learn
  • BPE, WordPiece tokenizers
  • Sentence‑transformers for embeddings

Code Challenge (≈ 30 min)
  • `token_demo.py` – encode a few sentences, compute cosine similarity

Build (≈ 1 h)
  • Commit `token_demo.py` and similarity matrix

Day 30 – HuggingFace Transformers (Fine‑tune BERT)

Learn
  • Loading pre‑trained models
  • Fine‑tuning on a text classification task

Code Challenge (≈ 30 min)
  • `bert_demo.py` – fine‑tune BERT on a small dataset (e.g., IMDb)

Build (≈ 1 h)
  • Commit `bert_demo.py` and evaluation metrics

Day 31 – Sentiment Analysis with HuggingFace

Learn
  • Text preprocessing for NLP
  • Predicting sentiment using a fine‑tuned model

Code Challenge (≈ 30 min)
  • `sentiment_demo.py` – load a few reviews, predict sentiment

Build (≈ 1 h)
  • Commit `sentiment_demo.py` and predictions

Day 32 – Prompt Engineering Basics

Learn
  • Crafting prompts for LLMs
  • Prompt templates & variables

Code Challenge (≈ 30 min)
  • `prompt_demo.md` – write 3 example prompts for different tasks

Build (≈ 1 h)
  • Commit `prompt_demo.md`

Day 33 – Chain‑of‑Thought Prompting

Learn
  • Few‑shot prompting
  • Step‑by‑step reasoning

Code Challenge (≈ 30 min)
  • `cot_demo.py` – prompt GPT‑2 to solve a simple math problem using chain‑of‑thought

Build (≈ 1 h)
  • Commit `cot_demo.py` and output

Day 34 – LLM Inference API (FastAPI)

Learn
  • Building a FastAPI app
  • Serving a HuggingFace model

Code Challenge (≈ 30 min)
  • `llm_api.py` – expose a `/predict` endpoint that runs a GPT‑2 model

Build (≈ 1 h)
  • Commit `llm_api.py` and a test client script

Day 35 – Mini‑Project: Conversational Chatbot

Learn
  • Dialogue management basics
  • Integrating LLM with a simple state machine

Code Challenge (≈ 45 min)
  • `chatbot.ipynb` – build a rule‑based chatbot that calls the LLM API

Build (≈ 1 h)
  • Commit `chatbot.ipynb` and demo conversation

Day 36 – Sentence Embeddings for Retrieval

Learn
  • Sentence‑transformers
  • Cosine similarity search

Code Challenge (≈ 30 min)
  • `embeddings_demo.py` – embed a set of FAQ sentences

Build (≈ 1 h)
  • Commit `embeddings_demo.py` and similarity results

Day 37 – Vector Store with FAISS

Learn
  • Indexing vectors with FAISS
  • Persisting and querying an index

Code Challenge (≈ 30 min)
  • `faiss_demo.py` – build index from embeddings, query a new sentence

Build (≈ 1 h)
  • Commit `faiss_demo.py` and retrieved results

Day 38 – Retrieval Pipeline (RAG)

Learn
  • Retrieve‑augmented generation workflow
  • Combining embeddings + index + LLM

Code Challenge (≈ 45 min)
  • `rag_pipeline.py` – given a user query, retrieve top‑k docs and generate an answer

Build (≈ 1 h)
  • Commit `rag_pipeline.py` and example outputs

Day 39 – LangChain RAG

Learn
  • LangChain fundamentals
  • Building a chain with retriever + LLM

Code Challenge (≈ 30 min)
  • `langchain_rag.py` – build a simple RAG chain

Build (≈ 1 h)
  • Commit `langchain_rag.py` and sample interaction

Day 40 – Retrieval‑Augmented Summarisation

Learn
  • Summarisation with context from retrieved docs
  • Prompt design for summarisation

Code Challenge (≈ 30 min)
  • `rag_summary.py` – summarize a long article using retrieved chunks

Build (≈ 1 h)
  • Commit `rag_summary.py` and summarised text

Day 41 – Fine‑Tune LLM on Custom Docs

Learn
  • LoRA / adapters for efficient fine‑tuning
  • Preparing a small custom dataset

Code Challenge (≈ 45 min)
  • `llm_finetune.py` – fine‑tune GPT‑2 on a 10‑page PDF

Build (≈ 1 h)
  • Commit `llm_finetune.py` and saved adapter weights

Day 42 – Mini‑Project: Knowledge‑Base Assistant

Learn
  • Building a domain‑specific assistant
  • Integrating retrieval + fine‑tuned LLM

Code Challenge (≈ 45 min)
  • `kb_assistant.ipynb` – ask questions about the PDF and get answers

Build (≈ 1 h)
  • Commit `kb_assistant.ipynb` and example Q&A

Day 43 – Docker Basics

Learn
  • Dockerfile syntax, layers, caching
  • Building an image locally

Code Challenge (≈ 30 min)
  • `Dockerfile` for the `llm_api.py` app

Build (≈ 1 h)
  • Build the image, run a container, test `/predict` endpoint
  • Commit `Dockerfile`

Day 44 – FastAPI Deployment (Docker Compose)

Learn
  • Compose a multi‑service stack
  • Linking the API with a vector store container

Code Challenge (≈ 30 min)
  • `docker-compose.yml` that starts the API and FAISS index

Build (≈ 1 h)
  • Launch stack, verify service, commit `docker-compose.yml`

Day 45 – CI/CD with GitHub Actions

Learn
  • GitHub Actions workflow syntax
  • Automated tests + image build

Code Challenge (≈ 30 min)
  • `ci.yml` that runs unit tests, builds image, pushes to GHCR

Build (≈ 1 h)
  • Commit `ci.yml`, verify run on push

Day 46 – Experiment Tracking with MLflow

Learn
  • Logging parameters, metrics, artifacts
  • MLflow UI for tracking runs

Code Challenge (≈ 30 min)
  • `mlflow_demo.py` – log a simple training run

Build (≈ 1 h)
  • Commit `mlflow_demo.py` and show MLflow UI link

Day 47 – Monitoring – Prometheus & Grafana

Learn
  • Exporting metrics in Prometheus format
  • Visualising with Grafana dashboards

Code Challenge (≈ 30 min)
  • `metrics.py` – expose `/metrics` endpoint
  • `prometheus.yml` – scrape config

Build (≈ 1 h)
  • Commit `metrics.py`, `prometheus.yml` and a Grafana dashboard JSON

Day 48 – Model Registry & Versioning

Learn
  • Persisting models in a registry
  • Semantic versioning

Code Challenge (≈ 30 min)
  • `model_registry.py` – save & load a model by version

Build (≈ 1 h)
  • Commit `model_registry.py` and a sample model

Day 49 – Mini‑Project: MLOps Pipeline

Learn
  • End‑to‑end flow: ingest → train → serve → monitor
  • Automation with scripts

Code Challenge (≈ 45 min)
  • `mlops_demo.ipynb` – orchestrate all previous steps in one notebook

Build (≈ 1 h)
  • Commit `mlops_demo.ipynb` and all supporting scripts

Day 50 – Advanced MLOps – Canary Deployment

Learn
  • Canary rollout strategy
  • Feature flags in API

Code Challenge (≈ 30 min)
  • `canary_demo.py` – switch between old & new model based on a header flag

Build (≈ 1 h)
  • Commit `canary_demo.py` and test requests

Day 51 – Model Explainability (SHAP, LIME)

Learn
  • Interpreting predictions with SHAP
  • Local explanations with LIME

Code Challenge (≈ 30 min)
  • `explain_demo.py` – generate SHAP values for a single prediction

Build (≈ 1 h)
  • Commit `explain_demo.py` and SHAP plot

Day 52 – Model Compression (Quantization & Pruning)

Learn
  • Post‑training quantization (int8)
  • Structured pruning

Code Challenge (≈ 45 min)
  • `compress_demo.py` – quantise a PyTorch model, compare size & latency

Build (≈ 1 h)
  • Commit `compress_demo.py` and benchmark results

Day 53 – Multimodal Models (Image + Text)

Learn
  • Image‑text fusion (e.g., CLIP)
  • Simple image captioning pipeline

Code Challenge (≈ 45 min)
  • `multimodal_demo.py` – encode image and caption, compute similarity

Build (≈ 1 h)
  • Commit `multimodal_demo.py` and sample outputs

Day 54 – Reinforcement Learning (DQN on CartPole)

Learn
  • OpenAI Gym environments
  • Deep Q‑Network architecture

Code Challenge (≈ 45 min)
  • `rl_demo.py` – implement DQN to solve CartPole

Build (≈ 1 h)
  • Commit `rl_demo.py` and training logs

Day 55 – Scaling Inference (GPU Cluster & Batching)

Learn
  • Batch inference with PyTorch DataLoader
  • Simple multi‑GPU inference script

Code Challenge (≈ 30 min)
  • `scale_demo.py` – load a large dataset, run inference in batches

Build (≈ 1 h)
  • Commit `scale_demo.py` and throughput results

Day 56 – Cloud Deployment (SageMaker)

Learn
  • Creating a SageMaker endpoint
  • Deploying a FastAPI container to SageMaker

Code Challenge (≈ 45 min)
  • `cloud_demo.py` – script that creates a SageMaker endpoint for `llm_api.py`

Build (≈ 1 h)
  • Commit `cloud_demo.py` and deployment logs

Day 57 – Security & Privacy (API Keys & Encryption)

Learn
  • API key authentication in FastAPI
  • Basic encryption of data at rest

Code Challenge (≈ 30 min)
  • `security_demo.py` – add API key header, encrypt a secret field

Build (≈ 1 h)
  • Commit `security_demo.py` and test script

Day 58 – Performance Profiling (PyTorch Profiler)

Learn
  • Profiling training and inference
  • Identifying bottlenecks

Code Challenge (≈ 30 min)
  • `profile_demo.py` – profile the DQN training loop

Build (≈ 1 h)
  • Commit `profile_demo.py` and a summary of results

Day 59 – Research Survey (Paper Summaries)

Learn
  • Skimming recent papers on LLMs & RAG
  • Writing concise summaries

Code Challenge (≈ 45 min)
  • `papers.md` – summarize two recent papers

Build (≈ 1 h)
  • Commit `papers.md`

Day 60 – Future Learning Road‑Map

Learn
  • Setting SMART goals
  • Planning next 3‑month sprint

Code Challenge (≈ 30 min)
  • `future_roadmap.md` – outline three new projects and milestones

Build (≈ 1 h)
  • Commit `future_roadmap.md`

End of the 60‑day sprint
You now have a fully‑worked, repo‑ready portfolio covering the entire AI stack – from Python fundamentals to MLOps‑ready LLM services. Use it to impress recruiters or as a foundation for further growth. Happy coding!

​60‑Day AI‑Engineer Road‑Map

​Week 1 – Python & Foundations (Days 1‑7)

​Week 2 – Data Handling & Visualization (Days 8‑14)

​Week 3 – Statistics & Machine‑Learning Basics (Days 15‑21)

​Week 4 – Deep Learning Foundations (Days 22‑28)

​Week 5 – NLP & Large‑Language‑Models (Days 29‑35)

​Week 6 – Retrieval‑Augmented Generation (RAG) (Days 36‑42)

​Week 7 – MLOps Foundations (Days 43‑49)

​Week 8 – Advanced Topics & Capstone (Days 50‑60)

​Summary

​60‑Day AI‑Engineer Road‑Map

​Day 1 – Python Basics

​Day 2 – Control Flow & Functions

​Day 3 – Modules & Packages

​Day 4 – File I/O

​Day 5 – Error Handling & Logging

​Day 6 – Virtual Environments

​Day 7 – Git Basics

​Day 8 – Pandas Basics

​Day 9 – NumPy Basics

​Day 10 – Matplotlib 101

​Day 11 – Seaborn 101

​Day 12 – Plotly 101

​Day 13 – Visualisation Best‑Practices

​Day 14 – Mini‑Project: Data Dashboard

​Day 15 – Probability Fundamentals

​Day 16 – Descriptive Statistics

​Day 17 – Hypothesis Testing (t‑Test)

​Day 18 – Supervised Learning Intro (Linear Regression)

​Day 19 – Model Evaluation

​Day 20 – Pipeline & Cross‑Validation

​Day 21 – Mini‑Project: Housing Price Prediction

​Day 22 – Neural‑Network Theory – Architecture

​Day 23 – PyTorch Basics

​Day 24 – MNIST “Hello World” (2‑layer NN)

​Day 25 – Training Loop, Optimizer & Scheduler

​Day 26 – Convolutional Neural Networks (CNN)

​Day 27 – Regularization (Dropout, Weight Decay)

​Day 28 – Mini‑Project: CIFAR‑10 Image Classifier

​Day 29 – Tokenization & Sentence Embeddings

​Day 30 – HuggingFace Transformers (Fine‑tune BERT)

​Day 31 – Sentiment Analysis with HuggingFace

​Day 32 – Prompt Engineering Basics

​Day 33 – Chain‑of‑Thought Prompting

​Day 34 – LLM Inference API (FastAPI)

​Day 35 – Mini‑Project: Conversational Chatbot

​Day 36 – Sentence Embeddings for Retrieval

​Day 37 – Vector Store with FAISS

​Day 38 – Retrieval Pipeline (RAG)

​Day 39 – LangChain RAG

​Day 40 – Retrieval‑Augmented Summarisation

​Day 41 – Fine‑Tune LLM on Custom Docs

​Day 42 – Mini‑Project: Knowledge‑Base Assistant

​Day 43 – Docker Basics

​Day 44 – FastAPI Deployment (Docker Compose)

​Day 45 – CI/CD with GitHub Actions

​Day 46 – Experiment Tracking with MLflow

​Day 47 – Monitoring – Prometheus & Grafana

​Day 48 – Model Registry & Versioning

​Day 49 – Mini‑Project: MLOps Pipeline

​Day 50 – Advanced MLOps – Canary Deployment

​Day 51 – Model Explainability (SHAP, LIME)

​Day 52 – Model Compression (Quantization & Pruning)

​Day 53 – Multimodal Models (Image + Text)

​Day 54 – Reinforcement Learning (DQN on CartPole)

​Day 55 – Scaling Inference (GPU Cluster & Batching)

​Day 56 – Cloud Deployment (SageMaker)

​Day 57 – Security & Privacy (API Keys & Encryption)

​Day 58 – Performance Profiling (PyTorch Profiler)

​Day 59 – Research Survey (Paper Summaries)

​Day 60 – Future Learning Road‑Map

60‑Day AI‑Engineer Road‑Map

Week 1 – Python & Foundations (Days 1‑7)

Week 2 – Data Handling & Visualization (Days 8‑14)

Week 3 – Statistics & Machine‑Learning Basics (Days 15‑21)

Week 4 – Deep Learning Foundations (Days 22‑28)

Week 5 – NLP & Large‑Language‑Models (Days 29‑35)

Week 6 – Retrieval‑Augmented Generation (RAG) (Days 36‑42)

Week 7 – MLOps Foundations (Days 43‑49)

Week 8 – Advanced Topics & Capstone (Days 50‑60)

Summary

60‑Day AI‑Engineer Road‑Map

Day 1 – Python Basics

Day 2 – Control Flow & Functions

Day 3 – Modules & Packages

Day 4 – File I/O

Day 5 – Error Handling & Logging

Day 6 – Virtual Environments

Day 7 – Git Basics

Day 8 – Pandas Basics

Day 9 – NumPy Basics

Day 10 – Matplotlib 101

Day 11 – Seaborn 101

Day 12 – Plotly 101

Day 13 – Visualisation Best‑Practices

Day 14 – Mini‑Project: Data Dashboard

Day 15 – Probability Fundamentals

Day 16 – Descriptive Statistics

Day 17 – Hypothesis Testing (t‑Test)

Day 18 – Supervised Learning Intro (Linear Regression)

Day 19 – Model Evaluation

Day 20 – Pipeline & Cross‑Validation

Day 21 – Mini‑Project: Housing Price Prediction

Day 22 – Neural‑Network Theory – Architecture

Day 23 – PyTorch Basics

Day 24 – MNIST “Hello World” (2‑layer NN)

Day 25 – Training Loop, Optimizer & Scheduler

Day 26 – Convolutional Neural Networks (CNN)

Day 27 – Regularization (Dropout, Weight Decay)

Day 28 – Mini‑Project: CIFAR‑10 Image Classifier

Day 29 – Tokenization & Sentence Embeddings

Day 30 – HuggingFace Transformers (Fine‑tune BERT)

Day 31 – Sentiment Analysis with HuggingFace

Day 32 – Prompt Engineering Basics

Day 33 – Chain‑of‑Thought Prompting

Day 34 – LLM Inference API (FastAPI)

Day 35 – Mini‑Project: Conversational Chatbot

Day 36 – Sentence Embeddings for Retrieval

Day 37 – Vector Store with FAISS

Day 38 – Retrieval Pipeline (RAG)

Day 39 – LangChain RAG

Day 40 – Retrieval‑Augmented Summarisation

Day 41 – Fine‑Tune LLM on Custom Docs

Day 42 – Mini‑Project: Knowledge‑Base Assistant

Day 43 – Docker Basics

Day 44 – FastAPI Deployment (Docker Compose)

Day 45 – CI/CD with GitHub Actions

Day 46 – Experiment Tracking with MLflow

Day 47 – Monitoring – Prometheus & Grafana

Day 48 – Model Registry & Versioning

Day 49 – Mini‑Project: MLOps Pipeline

Day 50 – Advanced MLOps – Canary Deployment

Day 51 – Model Explainability (SHAP, LIME)

Day 52 – Model Compression (Quantization & Pruning)

Day 53 – Multimodal Models (Image + Text)

Day 54 – Reinforcement Learning (DQN on CartPole)

Day 55 – Scaling Inference (GPU Cluster & Batching)

Day 56 – Cloud Deployment (SageMaker)

Day 57 – Security & Privacy (API Keys & Encryption)

Day 58 – Performance Profiling (PyTorch Profiler)

Day 59 – Research Survey (Paper Summaries)

Day 60 – Future Learning Road‑Map