-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathblog.html
More file actions
315 lines (295 loc) · 24.4 KB
/
blog.html
File metadata and controls
315 lines (295 loc) · 24.4 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>Mit Patel - Blog</title>
<meta name="description" content="Mit Patel - Highly experienced and results-oriented lecturer with 7 years of expertise in educating and guiding students in mathematics, statistics, and programming. Possessing a Master's degree in Data Analytics and a strong foundation in Information Technology, I have successfully developed and delivered comprehensive curricula in core subjects such as Machine Learning, Data Analysis, and Python. Proven ability to simplify complex concepts, enhance student performance, and foster a positive learning environment. Driven to leverage my analytical, problem-solving, and programming skills, coupled with continuous upskilling in cutting-edge AI/ML technologies (including LLMs and MLOps), to contribute to innovative solutions in the Data Science domain, developing and implementing advanced machine learning models to solve complex business problems.">
<meta name="author" content="Mit Patel">
<!-- Open Graph -->
<meta property="og:title" content="Mit Patel">
<meta property="og:type" content="website">
<meta property="og:url" content="127.0.0.1:5500/blog.html">
<meta property="og:image" content="127.0.0.1:5500/portfolio_media/photo_2.jpg">
<meta property="og:image:alt" content="Mit Patel Profile Image">
<!-- Fonts -->
<link rel="preconnect" href="https://fonts.googleapis.com">
<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
<link href="https://fonts.googleapis.com/css2?family=Montserrat:ital,wght@0,100..900;1,100..900&family=Nunito:ital,wght@0,200..1000;1,200..1000&display=swap"
rel="stylesheet">
<!-- Stylesheets -->
<link rel="stylesheet" href="css/modern_normalize.css">
<link rel="stylesheet" href="css/html5bp.css">
<link rel="stylesheet" href="css/main.css">
<link rel="stylesheet" href="css/blog.css">
<meta name="theme-color" content="#fafafa">
<!-- Favicon -->
<link rel="icon" href="/favicon.ico" sizes="any">
<link rel="icon" href="/icon.svg" type="image/svg+xml">
<link rel="apple-touch-icon" sizes="180x180" href="/icon.png">
<link rel="manifest" href="/site.webmanifest">
<!-- Icons -->
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.1/css/all.min.css"
integrity="sha512-DTOQO9RWCH3ppGqcWaEA1BIZOC6xxalwEsw9c2QQeAIftl+Vegovlnee1c9QX4TctnWMn13TZye+giMm8e2LwA=="
crossorigin="anonymous" referrerpolicy="no-referrer">
</head>
<body>
<header class="page-header">
<div class="container">
<div class="header-top flex-responsive">
<div class="header-info">
<h1>Mit Patel's Blog</h1>
<nav>
<ul class="inline-list flex-responsive">
<li><a href="index.html">Home</a></li>
<li><a href="resume.html">Resume</a></li>
<li><a href="projects.html">Projects</a></li>
<li><a href="tech-stack.html">Tech Stack</a></li>
<li><a href="contact.html">Contact</a></li>
</ul>
</nav>
</div>
</div>
</div>
</header>
<div class="page-content">
<div class="container">
<main>
<section>
<div class="blog-heading-row">
<h2 class="section-heading">
<span style="display: inline-flex; align-items: baseline; gap: 0.5rem;">
Recent Posts
<span id="total-posts-count"
style="font-size: 0.65em; font-weight: 500; opacity: 0.6; text-transform: uppercase; font-style: italic; letter-spacing: 0.5px;">Total
Posts: 12</span>
</span>
</h2>
<button id="mobile-filter-toggle" class="mobile-filter-toggle" aria-label="Filter by technology">
<i class="fas fa-filter"></i>
<span class="mobile-filter-label">Filter</span>
</button>
</div>
<!-- Mobile filter dropdown -->
<div id="mobile-filter-panel" class="mobile-filter-panel">
<div id="mobile-tag-filter-container" class="mobile-tag-container">
<button class="tag filter-tag active" data-tag="all">Show All</button>
<button class="tag filter-tag" data-tag="aws">AWS</button>
<button class="tag filter-tag" data-tag="aws ec2">AWS EC2</button>
<button class="tag filter-tag" data-tag="aws elastic beanstalk">AWS Elastic Beanstalk</button>
<button class="tag filter-tag" data-tag="aws s3">AWS S3</button>
<button class="tag filter-tag" data-tag="aws sagemaker">AWS SageMaker</button>
<button class="tag filter-tag" data-tag="agno">Agno</button>
<button class="tag filter-tag" data-tag="cloudwatch">CloudWatch</button>
<button class="tag filter-tag" data-tag="docker">Docker</button>
<button class="tag filter-tag" data-tag="fastapi">FastAPI</button>
<button class="tag filter-tag" data-tag="flask">Flask</button>
<button class="tag filter-tag" data-tag="github actions">GitHub Actions</button>
<button class="tag filter-tag" data-tag="google gemini">Google Gemini</button>
<button class="tag filter-tag" data-tag="groq">Groq</button>
<button class="tag filter-tag" data-tag="huggingface transformers">HuggingFace Transformers</button>
<button class="tag filter-tag" data-tag="mlops">MLOps</button>
<button class="tag filter-tag" data-tag="mlflow">MLflow</button>
<button class="tag filter-tag" data-tag="nlp">NLP</button>
<button class="tag filter-tag" data-tag="numpy">NumPy</button>
<button class="tag filter-tag" data-tag="pandas">Pandas</button>
<button class="tag filter-tag" data-tag="pytorch">PyTorch</button>
<button class="tag filter-tag" data-tag="python">Python</button>
<button class="tag filter-tag" data-tag="scikit-learn">Scikit-learn</button>
<button class="tag filter-tag" data-tag="streamlit">Streamlit</button>
<button class="tag filter-tag" data-tag="tensorboard">TensorBoard</button>
<button class="tag filter-tag" data-tag="weights & biases">Weights & Biases</button>
<button class="tag filter-tag" data-tag="yfinance">YFinance</button>
<button class="tag filter-tag" data-tag="torchvision">torchvision</button>
</div>
</div>
<!-- Blog post cards -->
<div id="blog-posts-container">
<article class="blog-post" data-tags="python,streamlit,google gemini,agno">
<h3 class="post-meta">
<a href="posts/building-an-intelligent-video-analyst-crafting-the-agents-brain-part-2.html">Building an Intelligent Video Analyst: Crafting the Agent's Brain (Part 2)</a>
</h3>
<p class="section-label">Published on: 2025-09-22</p>
<p>A robust pipeline is just the beginning; the real intelligence comes from the agent's 'brain.' This post is a deep dive into the art of prompt engineering, showing how carefully crafted instructions enable our `Agno` agent to not only analyze video with Google Gemini but also to autonomously use web search tools for a richer, more contextual analysis. See how the final pieces came together to create an intelligent video research assistant.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="python,streamlit,google gemini,agno">
<h3 class="post-meta">
<a href="posts/building-an-intelligent-video-analyst-the-foundational-pipeline-part-1.html">Building an Intelligent Video Analyst: The Foundational Pipeline (Part 1)</a>
</h3>
<p class="section-label">Published on: 2025-09-21</p>
<p>My journey to build an intelligent video analyst started with what seemed like a simple task: uploading a video. I quickly hit a wall with Google's asynchronous File API, a common real-world challenge that requires more than just basic code. This post covers the architectural decisions, the code for building a resilient video processing pipeline, and the "aha!" moment that made it all work, setting the stage for the AI agent.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="python,agno,groq,fastapi,yfinance">
<h3 class="post-meta">
<a href="posts/multi-agent-chaos-when-ai-agents-wouldnt-cooperate.html">Multi-Agent Chaos: When AI Agents Wouldn't Cooperate</a>
</h3>
<p class="section-label">Published on: 2025-09-20</p>
<p>My journey to build an AI financial analyst started with a single, confused bot that failed miserably. The solution wasn't a better model, but a better architecture: a coordinated team of AI specialists. Here's a deep dive into the code, the struggles, and the key lessons learned from building a multi-agent system with Agno and Groq.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="aws,python,flask,scikit-learn,pandas,numpy,aws ec2,aws elastic beanstalk,mlops">
<h3 class="post-meta">
<a href="posts/student-performance-prediction-when-simple-isnt-always-better.html">Student Performance Prediction: When Simple Isn't Always Better</a>
</h3>
<p class="section-label">Published on: 2025-09-19</p>
<p>A candid, in-depth account of developing an end-to-end Student Performance Prediction system. This post explores the journey from an experimental Jupyter Notebook to a production-ready, modular ML pipeline, covering the critical roles of custom logging, exception handling, and a component-based architecture. It also details the real-world challenges and hard-won lessons from deploying a Flask application on AWS.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="huggingface transformers,pytorch,fastapi,docker,python,weights & biases,mlops,nlp">
<h3 class="post-meta">
<a href="posts/text-summarizer-journey-serving-the-model-part-3.html">Text Summarizer Journey: Serving the Model (Part 3)</a>
</h3>
<p class="section-label">Published on: 2025-09-18</p>
<p>The final step in our MLOps journey is making the trained model useful. This post covers the "last mile" of deployment, showing how to wrap the text summarization model in a high-performance API using FastAPI. I then walk through creating a Dockerfile to containerize the entire application, ensuring a consistent and portable service that can be deployed anywhere.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="huggingface transformers,pytorch,fastapi,docker,python,weights & biases,mlops,nlp">
<h3 class="post-meta">
<a href="posts/text-summarizer-journey-the-ml-engine-room-part-2.html">Text Summarizer Journey: The ML Engine Room (Part 2)</a>
</h3>
<p class="section-label">Published on: 2025-09-17</p>
<p>With a robust MLOps pipeline in place, this post dives into the core machine learning workflow of the Text Summarizer project. I explore each critical stage: transforming the raw SAMSum dataset for the model, fine-tuning a pre-trained Pegasus Transformer using a configuration-driven approach, and quantitatively evaluating its performance with ROUGE metrics.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="huggingface transformers,pytorch,fastapi,docker,python,weights & biases,mlops,nlp">
<h3 class="post-meta">
<a href="posts/text-summarizer-journey-the-mlops-blueprint-part-1.html">Text Summarizer Journey: The MLOps Blueprint (Part 1)</a>
</h3>
<p class="section-label">Published on: 2025-09-16</p>
<p>My Text Summarizer project started in a Jupyter notebook—a great place for experimentation, but a fragile foundation for a real application. This post details the journey of refactoring that initial script into a robust, production-ready MLOps pipeline, tackling the challenges of hardcoded paths, scattered configuration, and monolithic execution with a modular, component-based architecture.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="aws sagemaker,scikit-learn,aws s3,python,mlops,cloudwatch,aws">
<h3 class="post-meta">
<a href="posts/when-sagemaker-humbled-me-a-cloud-native-ml-reality-check.html">When SageMaker Humbled Me: A Cloud-Native ML Reality Check</a>
</h3>
<p class="section-label">Published on: 2025-09-15</p>
<p>I thought taking a scikit-learn model to the cloud would be simple, but AWS SageMaker taught me more about cloud architecture and DevOps than I ever expected. This post details my entire journey, from battling IAM roles and structuring S3 buckets to mastering the SageMaker training patterns that finally bridged the gap between my local machine and a production-ready endpoint. It's a story of the struggles, the breakthroughs, and the hard-won lessons learned while building a real-world ML pipeline.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="pytorch,fastapi,flask,torchvision,python,tensorboard">
<h3 class="post-meta">
<a href="posts/from-notebook-to-ui-the-web-deployment-journey-part-2.html">From Notebook to UI: The Web Deployment Journey (Part 2)</a>
</h3>
<p class="section-label">Published on: 2025-09-13</p>
<p>A machine learning model is useless if no one can use it. This post covers the "last mile" problem: giving my PyTorch model an interactive body. This is the story of my struggle against frozen UIs, the architectural epiphany that led to a framework-agnostic core, and the final lessons learned while building identical apps in both Flask and FastAPI.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="pytorch,fastapi,flask,torchvision,python,tensorboard">
<h3 class="post-meta">
<a href="posts/from-notebook-to-ui-the-local-development-journey-part-1.html">From Notebook to UI: The Local Development Journey (Part 1)</a>
</h3>
<p class="section-label">Published on: 2025-09-12</p>
<p>My FoodVision Mini project started with a simple question in a Jupyter Notebook, but the answer required a real engineering journey. This post tells the story of how I tamed chaotic experiments with a systematic process, unlocked the true power of transfer learning through trial and error, and refactored a monolithic script into a robust, modular ML pipeline.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="python,aws,docker,fastapi,scikit-learn,mlflow,github actions">
<h3 class="post-meta">
<a href="posts/from-messy-data-to-production-mlops-my-network-security-journey-part-2.html">From Messy Data to Production MLOps: My Network Security Journey (Part 2)</a>
</h3>
<p class="section-label">Published on: 2025-09-09</p>
<p>With a working local pipeline, the "easy" part was next: deployment. This turned into a multi-day AWS nightmare. After successfully automating the CI/CD pipeline with GitHub Actions, the app was live but unreachable. The culprit? A single, critical line of code related to container networking. This post dives into the humbling, real-world challenges of cloud infrastructure, debugging EC2 security groups, and the final "aha!" moment that brought the entire system online.</p>
<div class="tech-stack">
</div>
</article>
<article class="blog-post" data-tags="python,aws,docker,fastapi,scikit-learn,mlflow,github actions">
<h3 class="post-meta">
<a href="posts/from-messy-data-to-production-mlops-my-network-security-journey-part-1.html">From Messy Data to Production MLOps: My Network Security Journey (Part 1)</a>
</h3>
<p class="section-label">Published on: 2025-09-08</p>
<p>My journey began with a classic MLOps mistake: underestimating messy data. My model worked locally, but I spent weeks debugging failures until a breakthrough came from implementing rigorous <strong>data validation schemas</strong> and <strong>drift detection</strong>. This post covers the foundational engineering—modular architecture, custom logging, and experiment tracking—that's essential *before* you even think about the cloud. It’s the story of building a resilient pipeline from the ground up.</p>
<div class="tech-stack">
</div>
</article>
</div>
<div class="load-more-wrapper" style="text-align: center; margin-top: 2rem;">
<button id="load-more-btn" class="load-more-btn">Load More Posts</button>
</div>
</section>
</main>
<!-- Desktop sidebar filter -->
<aside>
<section>
<h2 class="section-heading">Filter by Technology</h2>
<div id="tag-filter-container" class="tech-stack">
<button class="tag filter-tag active" data-tag="all">Show All</button>
<button class="tag filter-tag" data-tag="aws">AWS</button>
<button class="tag filter-tag" data-tag="aws ec2">AWS EC2</button>
<button class="tag filter-tag" data-tag="aws elastic beanstalk">AWS Elastic Beanstalk</button>
<button class="tag filter-tag" data-tag="aws s3">AWS S3</button>
<button class="tag filter-tag" data-tag="aws sagemaker">AWS SageMaker</button>
<button class="tag filter-tag" data-tag="agno">Agno</button>
<button class="tag filter-tag" data-tag="cloudwatch">CloudWatch</button>
<button class="tag filter-tag" data-tag="docker">Docker</button>
<button class="tag filter-tag" data-tag="fastapi">FastAPI</button>
<button class="tag filter-tag" data-tag="flask">Flask</button>
<button class="tag filter-tag" data-tag="github actions">GitHub Actions</button>
<button class="tag filter-tag" data-tag="google gemini">Google Gemini</button>
<button class="tag filter-tag" data-tag="groq">Groq</button>
<button class="tag filter-tag" data-tag="huggingface transformers">HuggingFace Transformers</button>
<button class="tag filter-tag" data-tag="mlops">MLOps</button>
<button class="tag filter-tag" data-tag="mlflow">MLflow</button>
<button class="tag filter-tag" data-tag="nlp">NLP</button>
<button class="tag filter-tag" data-tag="numpy">NumPy</button>
<button class="tag filter-tag" data-tag="pandas">Pandas</button>
<button class="tag filter-tag" data-tag="pytorch">PyTorch</button>
<button class="tag filter-tag" data-tag="python">Python</button>
<button class="tag filter-tag" data-tag="scikit-learn">Scikit-learn</button>
<button class="tag filter-tag" data-tag="streamlit">Streamlit</button>
<button class="tag filter-tag" data-tag="tensorboard">TensorBoard</button>
<button class="tag filter-tag" data-tag="weights & biases">Weights & Biases</button>
<button class="tag filter-tag" data-tag="yfinance">YFinance</button>
<button class="tag filter-tag" data-tag="torchvision">torchvision</button>
</div>
</section>
</aside>
</div>
</div>
<footer class="page-footer">
<div class="container">
<p>© 2026 Mit Patel. All rights reserved.</p>
</div>
</footer>
<!-- Theme toggle -->
<div class="theme-toggle-container">
<div id="theme-toggle-icon" class="theme-icon"><i class="fas"></i></div>
</div>
<!-- Search -->
<button id="search-fab" class="search-fab" aria-label="Search blog posts">
<i class="fas fa-magnifying-glass"></i>
</button>
<div id="search-overlay" class="search-overlay">
<div class="search-modal">
<div class="search-header">
<i class="fas fa-magnifying-glass"></i>
<input type="text" id="search-input" placeholder="Search blog posts…" autocomplete="off">
<span class="search-kbd">⌘K</span>
<button id="search-clear-btn" class="search-action-btn" aria-label="Clear search"
style="display:none;"><i class="fas fa-delete-left"></i></button>
<button id="search-close-btn" class="search-action-btn" aria-label="Close search"><i
class="fas fa-xmark"></i></button>
</div>
<div id="search-results" class="search-results">
<p class="search-hint">Type at least 2 characters to search…</p>
</div>
</div>
</div>
<!-- Scripts -->
<script src="js/search-index.js"></script>
<script src="js/search.js"></script>
<script src="js/blog.js"></script>
<script src="js/app.js"></script>
</body>
</html>