# blog_post

title: A Gentle Introduction to Distributed Training with DeepSpeed
slug: a-gentle-introduction-to-distributed-training-with-deepspeed
meta_description: An introduction to distributed training with Microsoft's DeepSpeed library
pub_date: 2022-01-30
keywords: deep learning; distributed training; deepspeed
authors: Miguel González-Fierro
abstract:
  DeepSpeed is an open-source library that facilitates the training of large deep learning models based on PyTorch. With minimal code changes, a developer can train a model on a single GPU machine, a single machine with multiple GPUs, or on multiple machines in a distributed fashion. In this post, we review DeepSpeed and explain how to get started.

content:
  <h4 class="section">Distributed Training</h4>

  <p>With more and more data being created every day, we need more computational resources to create AI. Distributed training is a scalable solution to this problem.&nbsp; &nbsp;&nbsp;</p>

  <p>There are 2 general methodologies to perform distributed training: data parallelism and model parallelism.</p>

  <p>In data parallelism, we copy the entire model on each worker, processing different subsets of the training data set on each.&nbsp;</p>

  <p>In model parallelism, different machines in the distributed system are responsible for the computations of&nbsp;different parts of a single network,&nbsp;for example, each layer in the neural network may be assigned to a different machine.</p>

  <p>Data parallelism is easier to implement, stronger to fault tolerance, and has higher utilization of GPUs. On the other hand, model parallelism&nbsp;has better scalability of large models and it uses less GPU memory.</p>

  <p>Generally speaking, data parallelism is more common, but some people even use a combination of the two techniques.</p>

  <h4 class="section">DeepSpeed</h4>

  <p>Being able to train machine learning models in a distributed fashion has always been a pain. In contrast to Spark systems,&nbsp;which automatically leverages a distributed infrastructure on top of the <a href="https://en.wikipedia.org/wiki/Java_virtual_machine" target="_blank">JVM</a>, computing&nbsp;deep learning on multiple GPU clusters with Python and CUDA is complex. DeepSpeed aims to facilitate this task.</p>

  <p><a href="https://www.deepspeed.ai/" target="_blank">DeepSpeed</a> is an open-source library that facilitates model development and training in a distributed mode.</p>

  <p>The library is a light wrapper on top of <a href="https://pytorch.org/" target="_blank">PyTorch</a>.&nbsp;With minimal code changes, a developer can train a model on a single GPU machine, a single machine with multiple GPUs, or on multiple machines in a distributed fashion.</p>

  <p>One of the advantages is that it enables massive models. When the library was first released, it was able to train a model of 200B parameters, by the end of 2021, the team was able to train <a href="https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/" target="_blank">Megatron-Turing NLG 530B</a>, the largest generative language model to date with 530 billion parameters. They are working to support soon a model of 1 trillion parameters.</p>

  <p>The other important feature is its speed. According to their experiments, DeepSpeed trains 2&ndash;7x faster than other solutions by reducing communication volume during distributed training.</p>

  <p>Last, but not least, the library only requires minimal code changes to use. In comparison to other distributed training libraries, DeepSpeed does not require a code redesign or model refactoring.</p>

  <p>In the following table, the authors show the training time of <a href="https://arxiv.org/abs/1810.04805" target="_blank">BERT-large</a>.</p>

  <p><img alt="" height="190" src="/img/upload/2022/01/30/deepspeed1.png" width="320" /></p>

  <h4 class="section">Training API</h4>

  <p>The core functionality of DeepSpeed comes from its engine. This engine can wrap any model in the form of <span style="font-family:Courier New,Courier,monospace">torch.nn.module</span> with a minimal set of code changes for training and checkpointing the model.</p>

  <p>DeepSpeed model training is accomplished using the DeepSpeed engine. The engine can wrap any arbitrary model of type torch.nn.module and has a minimal set of APIs for training and checkpointing the model.</p>

  <p>The three main components are the initialization, with&nbsp;<span style="font-family:Courier New,Courier,monospace">initialize </span>method, the training, with&nbsp;<span style="font-family:Courier New,Courier,monospace">backward </span>and <span style="font-family:Courier New,Courier,monospace">step </span>methods, and checkpointing, with&nbsp;<span style="font-family:Courier New,Courier,monospace">load_checkpoint </span>and <span style="font-family:Courier New,Courier,monospace">save_checkpoint</span>.&nbsp;</p>

  <p>The method <span style="font-family:Courier New,Courier,monospace">initialize</span> sets up of the model, training optimizer, data loader, and the learning rate scheduler. It also initializes the engine for distributed data parallel or mixed precision training.</p>

  <pre>
  <code class="language-python">model, *_ = initialize(args=cmd_args,
                         model=model_architecture,
                         model_parameters=params)
  </code></pre>

  <p>After the engine is initialized, we can train the model with three small changes in the standard PyTorch API:&nbsp;forward propagation, backward propagation, and weight updates.</p>

  <pre>
  <code class="language-python">for step, batch in enumerate(data_loader):
      # Forward propagation method
      loss = model(batch)

      # Runs backpropagation
      model.backward(loss)

      # Weights update
      model.step()</code></pre>

  <p>DeepSpeed can automatically save and restore the model, optimizer, and the learning rate scheduler states&nbsp;via the <span style="font-family:Courier New,Courier,monospace">save_checkpoint </span>and <span style="font-family:Courier New,Courier,monospace">load_checkpoint</span>.&nbsp;</p>

  <h4 class="section">Zero Redundancy Optimizer</h4>

  <p>The <a href="https://www.deepspeed.ai/tutorials/zero/" target="_blank">Zero Redundancy Optimizer</a> (ZeRO) is the key component that allows DeepSpeed to train massive models at scale. ZeRO enables the training of models with over 13 billion parameters without model parallelism, and of models with more than 500 billion parameters using model parallelism.</p>

  <p>At the time of writing, ZeRO has gone through 3 iterations, being ZeRO-3 the latest version. ZeRO optimizes the memory consumption by partitioning the weights, gradients, and optimizer components of the model across the CPUs and GPUs of the distributed system.</p>

  <p>To get a&nbsp;quick start on DeepSpeed, see <a href="https://github.com/miguelgfierro/sciblog_support/tree/ddc46963d060fedecc700c09875b8d0a9068c0d2/Distributed_Training_with_DeepSpeed" target="_blank">this short tutorial</a> where it is&nbsp;explained how to quickly transform a PyTorch model to add the DeepSpeed capability.&nbsp;</p>


title: My most popular LinkedIn posts of 2021
slug: my-most-popular-linkedin-posts-of-2021
meta_description: A summary of all my popular 2021 LinkedIn posts organized by category
pub_date: 2022-02-26
keywords: ai; machine learning; data science
authors: Miguel González-Fierro
abstract:
  In 2021, I started posting on LinkedIn more consistently. A lot of my posts are about simplifying AI concepts so people can discover the field, learn something new or even grow their career in Data Science. In 2021 I got around 2 million views in my content, here are some of the most popular posts.

content:
  <h4 class="section">Personal Journeys</h4>

  <p><iframe align="middle" frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6854646399154298880" width="350"></iframe></p>

  <p><iframe align="middle" frameborder="0" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6806854788429426688" width="350"></iframe></p>

  <p><iframe frameborder="0" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6847410784465645568" width="350"></iframe></p>

  <h4 class="section">Building a GitHub Portfolio</h4>

  <p><iframe frameborder="0" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6860835440124035072" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6871695426181980160" width="350"></iframe></p>

  <h4 class="section">On Code Simplicity</h4>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6855736149634236416" width="350"></iframe></p>

  <h4 class="section">Data Science Career Tips</h4>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6857551776783060992" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6868787835239972864" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6881163805942919168" width="350"></iframe></p>

  <h4 class="section">Career Switch Into AI</h4>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6870384651853168640" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6848482860630847488" width="350"></iframe></p>

  <h4 class="section">NLP Concepts</h4>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6766269132712251393" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6783644834587533312" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6800322562409676802" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6866979580561891328" width="350"></iframe></p>

  <h4 class="section">General ML Concepts</h4>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6811407593920045056" width="350"></iframe></p>

  <p><iframe frameborder="0" height="230" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6824027969380483072" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6875332604145491968" width="350"></iframe></p>

  <h4 class="section">Computer Vision Concepts</h4>

  <p><iframe frameborder="0" height="230" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6871321946034843648" width="350"></iframe></p>

  <h4 class="section">Reco Systems Concepts</h4>

  <p><iframe frameborder="0" height="230" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6795597254146179072" width="350"></iframe></p>

  <p><iframe frameborder="0" height="230" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6840900236274102272" width="350"></iframe></p>

  <h4 class="section">Reinforcement Learning Concepts</h4>

  <p><iframe frameborder="0" height="250" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6772061746996768768" width="350"></iframe></p>

  <h4 class="section">Recommend a Library</h4>

  <p><iframe frameborder="0" height="230" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6756166794022543360" width="350"></iframe></p>

  <p><iframe frameborder="0" height="230" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6831122101324279808" width="350"></iframe></p>

  <h4 class="section">Personal Stories</h4>

  <h4 class="section"><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6822229746143768576" width="350"></iframe></h4>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6869156108099969024" width="350"></iframe></p>

  <h4 class="section">Trends in AI</h4>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6865899076710408192" width="350"></iframe></p>

  <p><iframe frameborder="0" height="220" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:6878936112233025536" width="350"></iframe></p>


title: An Analysis of the Adoption of Top Deep Learning Frameworks
slug: an-analysis-of-the-adoption-of-top-deep-learning-frameworks
meta_description: An analysis of the adoption and growth of 5 deep learning frameworks
pub_date: 2022-07-27
keywords: deep learning; ai; machine learning
authors: Miguel González-Fierro
abstract:
  There are a lot of people comparing deep learning frameworks in terms of speed or performance, but I haven't seen anyone studying their adoption over time and analyzing the underlying reasons why some attracted more users than others. In this post, I share what I have observed since 2015, and based on historical data, I provide guidance on one of the top questions I get: what deep learning framework should I use?

content:
  <p>The frameworks I want to analyze are Tensorflow, Keras, PyTorch, MXNet, and CNTK. I have used all of them and contributed to MXNet and CNTK.</p>

  <p>In terms of adoption, the current ranking is PyTorch &gt; Tensorflow &gt;&nbsp;Keras &gt; MXNet &gt; CNTK. In the next image obtained from this <a href="http://horace.io/pytorch-vs-tensorflow/" target="_blank">website</a>, we show the percentage of PyTorch papers over Tensorflow.&nbsp;</p>

  <p><img alt="" height="245" src="/img/upload/2022/07/27/pytorch-papers-of-total.svg" width="350" /></p>

  <h4 class="section">Tensorflow</h4>

  <p><a href="https://www.tensorflow.org/" target="_blank">Tensorflow</a>&nbsp;was initially released by Google Brain in 2015 and all the company uses it.</p>

  <p>In terms of adoption, Google&nbsp;has&nbsp;produced some of the most impactful deep learning&nbsp;advances recently, like BERT or AlphaZero, so a lot of research labs and companies use&nbsp;Tensorflow. Google was a pioneer in open source deep learning, so it had the first mover advantage.</p>

  <p>The main weakness of Tensorflow is its complexity. It was designed by researchers for researchers, it wasn&rsquo;t a tool for the general machine learning community.&nbsp;In version 2, Google refactored much of the API, getting rid of things like the data placeholders and simplifying the code. These details and some security issues reduced its adoption.</p>

  <h4 class="section">Keras</h4>

  <p><a href="https://github.com/keras-team/keras" target="_blank">Keras</a> was released in 2015 by Fran&ccedil;ois Chollet as a high-level interface for Tensorflow. It was designed as a user-friendly, modular, and extensible alternative to Tensorflow.&nbsp;After Chollet joined Google, Keras became part of Tensorflow.</p>

  <p>In terms of adoption, Keras gained a lot of popularity quickly because its learning curve was much easier. It particularly attracted a lot of interest in the industry.</p>

  <p>The main weakness of Keras was that it took them a while to implement some important features like multi-gpu training, distributed training, and other features. So if you wanted to create complex solutions, you still needed to use Tensorflow.</p>

  <h4 class="section">PyTorch</h4>

  <p><a href="https://pytorch.org/" target="_blank">PyTorch</a> was published in 2016 by Facebook Research (FAIR). A previous project called Torch, written in the programming language Lua, didn&rsquo;t have much adoption, and FAIR decided to pivot and focus on Python.</p>

  <p>In terms of adoption, PyTorch has gained a lot of momentum since 2018 and has become the most used deep learning framework in the research community. PyTorch was designed by developers that clearly mastered the Python language and know good software engineering practices.&nbsp;In addition to its simplicity, PyTorch also had the push by a top-class research group, which published all new research in PyTorch. Other companies like Microsoft or Huggingface started to support it creating a lot of adoption.</p>

  <p>Its main weakness was that PyTorch started later than Tensorflow, so initially, there were not that many algorithms implemented in PyTorch.</p>

  <h4 class="section">MXNet</h4>

  <p><a href="https://mxnet.apache.org/" target="_blank">MXNet</a> was started by Tianqi Chen from the University of Washington, who is also the creator of XGBoost. Even though it initially had the support of Microsoft (yours truly included), eventually Amazon became its main supporter. Amazon wanted to replicate the strategy of Google with Tensorflow and have its own deep learning framework.</p>

  <p>In terms of adoption, MXNet gained interest because it was very easy to do multi-GPU training. Tianqi had a lot of experience in distributed systems, so MXNet&nbsp;natively works in multi-GPU.&nbsp;It also has a great user experience.</p>

  <p>Its main weakness was that MXNet didn&rsquo;t have much support for new research, so no new ML breakthroughs were developed initially in MXNet. Instead, MXNet reimplemented other algorithms developed&nbsp;in Tensorflow or PyTorch. Several years ago, Amazon introduced Gluon, a high-level API for MXNet in an attempt to increase its adoption. I think this was a mistake because the problem with MXNet was not that it was difficult to use. Instead of losing their time building Gluon, they could have doubled down on the strengths of MXNet, like its distributed capability.</p>

  <h4 class="section">CNTK</h4>

  <p><a href="https://github.com/Microsoft/CNTK" target="_blank">CNTK</a> was released by Microsoft in April 2015. Initially, it used a scripting language called BrainScript, and then in October 2016, with its version 2, it supported Python and C++. In 2019, Microsoft decided to move away from CNTK and use PyTorch as its main deep learning framework, a decision that I supported.</p>

  <p>In terms of adoption, CNTK got an initial push from Microsoft&nbsp;due to its efficiency in&nbsp;distributed training.</p>

  <p>In terms of weaknesses, it was designed following the patterns of Tensorflow, which initially people thought was going to win the deep learning race, so the Python wrapper looked a lot like C++. At Microsoft, we are not forced to use a specific framework like at other companies, so not everyone used CNTK. The lack of adoption and its lack of support by the research community made Microsoft pivot and focus on PyTorch.</p>

  <h4 class="section">What framework to use</h4>

  <p>Based on the previous analysis, here is my advice on what framework you could use in order of my personal preference.</p>

  <p>My first option is PyTorch. PyTorch has a strong support from the research community and it is very easy to use. If you want to do distributed training, mixing it with <a href="https://miguelgfierro.com/blog/2022/a-gentle-introduction-to-distributed-training-with-deepspeed/" target="_blank">DeepSpeed</a> is really difficult to beat.</p>

  <p>As a second option, I recommend using whatever&nbsp;framework the algorithm is implemented in. Basically, if you find a working piece of code, just use it.</p>

  <p>My third option is MXNet, because of&nbsp;how easy it is to use, and&nbsp;do multi-GPU training.</p>

  <p>Next, I would use Keras, then Tensorflow, and the last one would be CNTK.</p>


title: D4 Data Podcast with Miguel Fierro - Recommendation Systems
slug: d4-data-podcast-with-miguel-fierro-recommendation-systems
meta_description: D4 Data Podcast hosted by Deepak JR with Miguel Fierro - Applied Recommendation Systems
pub_date: 2022-10-01
keywords: recommendation systems; ai; machine learning
authors: Deepak JR and Miguel González-Fierro
youtube_video_id: Dm-DYatNuGQ
abstract:
  Deepak JR interviewed me for his podcast D4 Data Podcast where we discussed everything about recommendation systems. Recommendation systems are one of the most impactful machine learning systems in production in the world. It is not a surprise that they are one of the most complex and demanding solutions, with multiple moving parts that need to be tuned in the right way to improve the user experience and increase revenue.

content:
  <p>During the interview we cover several interesting topics:</p>

  <ul>
  	<li>The evolution of recommendation systems.</li>
  	<li>Best recommendation engine available online (not saying the best is <a href="https://github.com/microsoft/recommenders" target="_blank">Recommenders</a>&nbsp;🤓).</li>
  	<li>Best recommendation engine in streaming platforms.</li>
  	<li>User privacy when collecting data for recommendations.&nbsp;</li>
  	<li>Filter bubbles in recommendation systems.</li>
  	<li>Importance of business rules in recommendation system.</li>
  	<li>Removing noise in recommendations.</li>
  	<li>Advancements in algorithms and effectiveness in training.</li>
  	<li>Implementation procedures for a large-scale&nbsp;recommendation system.</li>
  	<li>Consequences &amp; Responsible AI design for recommendation systems.</li>
  	<li>Future of recommendation systems.</li>
  </ul>


title: Discover my day to day at Microsoft - Doing a data science project
slug: discover-my-day-to-day-at-microsoft-doing-a-data-science-project
meta_description: Discover how a professional Data Sciencist at Microsoft creates a machine learning project
pub_date: 2022-10-09
keywords: data science; machine learning; programming
authors: Miguel González-Fierro
youtube_video_id: HVSYiKTOWg8
abstract:
  Whether you are interested in changing your career to an AI position or you want to improve your skills as a Data Scientist, it is key to know how to effectively create machine learning projects. In this post, I show how to start a machine learning project, by combining two of the most useful tools we have at our disposal, Jupyter notebooks and Python libraries. See the full video to discover how I create projects in my job at Microsoft.

content:
  <p>There are two key concepts that are important when working as a Data Scientist.</p>

  <p>First, you have to experiment. When I approach a&nbsp;machine learning problem, most of the time, I don&#39;t know the best way to solve it. There is some intuition, but I always try multiple machine learning models, features, and parameters.</p>

  <p>Second, you need to be able to share your solution. It is not enough that the code works on your local machine, otherwise, it is just a personal project. You need to be able to work with others in your team&nbsp;and to productionize your code.</p>

  <p>In the video, I show the setup I use in my machine learning projects, what is the folder structure I normally use, and how to combine Jupyter notebooks and Python libraries to effectively solve problems with AI.</p>

  <p>If you think this is valuable, consider joining&nbsp;<a href="https://bit.ly/emaillistmiguel" rel="noopener noreferrer" target="_blank">https://bit.ly/emaillistmiguel</a></p>


title: Building Recommender Systems
slug: building-recommender-systems
meta_description: How to build recommendation systems using Recommenders repository
pub_date: 2022-11-26
keywords: ai; recommendation systems; machine learning
authors: Seth Juarez and Miguel González-Fierro
youtube_video_id: Th5IHCbRyUk
abstract:
  Recommendation systems are one of the most exciting AI solutions available today. They are information filters that learn users' behavior based on their historical interactions with items, and then predict their preferences for a given item. In this post, we make an overview of Recommenders, the open-source repository that helps Data Scientists build recommendation systems.

content:
  <p>I had a chat with Seth Juarez about Recommenders.</p>

  <p><a href="https://github.com/microsoft/recommenders/" target="_blank">Recommenders</a> is the top open-source repository in recommendation systems, containing more than 30 algorithms and examples to help people learn and build recommendations systems.</p>

  <p>We first discuss recommendation systems&#39; impact on improving user experience and increasing revenue.</p>

  <p>Then we go through the main components of the repository: the test infrastructure, the Python library, and the examples.</p>

  <p>Finally, we discuss strategies if you want to start a recommendation system project.</p>


title: My Most Popular LinkedIn Posts Of 2022
slug: my-most-popular-linkedin-posts-of-2022
meta_description: A summary of all my popular 2022 LinkedIn posts organized by category
pub_date: 2023-01-02
keywords: ai; machine learning; data science
authors: Miguel González-Fierro
abstract:
  In 2022 around 11 million people saw my posts on LinkedIn. I've been posting to help people understand and apply AI. In particular, I focused on how people can switch their careers to AI because that's the journey I followed.  Here are some of the most popular posts.

content:
  <h4 class="section">Career Switch into AI</h4>

  <p><iframe align="middle" frameborder="1" height="279" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6906489852540653568" width="350"></iframe></p>

  <p><iframe align="middle" frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6910828789342056448" width="350"></iframe></p>

  <p><iframe align="middle" frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6896341269350858752" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6968831554491965441" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6945249698995695616" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6969183463841136640" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6989858103668142080" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6931127941439315968" width="350"></iframe></p>

  <h4 class="section">Building a GitHub Portfolio</h4>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:7006164511686561792" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6914434362885185536" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6890912204540248064" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6993452749992419329" width="350"></iframe></p>

  <h4 class="section">NLP Concepts</h4>

  <h4 class="section"><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:7005786940956381184" width="350"></iframe></h4>

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  <h4 class="section">Reco Concepts</h4>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6928585780810416128" width="350"></iframe></p>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6992748189644668928" width="350"></iframe></p>

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  <h4 class="section">MLOps Concepts</h4>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:7000689843001307136" width="350"></iframe></p>

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  <h4 class="section">General ML Concepts</h4>

  <p><iframe frameborder="0" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:6954676191748341761" width="350"></iframe></p>

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  <h4 class="section">Mixed</h4>

  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:ugcPost:6954316612497752064" width="350"></iframe></p>

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  <p><iframe frameborder="1" height="270" scrolling="yes" src="https://www.linkedin.com/embed/feed/update/urn:li:share:7007957040555814912" width="350"></iframe></p>

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title: Start with Transformers
slug: start-with-transformers
pub_date: 2023-02-02
keywords: ai; machine learning; data science
authors: Neil Leiser and Miguel González-Fierro
youtube_video_id: OWwM9l2DSsA
meta_description:
  Chat with Neil Leiser about how to learn AI faster, how to switch fields, and the power of practical knowledge.

abstract:
  In this conversation with Neil Leiser, we first dig into my beginning in robotics and the connection between robotics and AI. Then I share a few stories on how to learn AI faster, how to switch fields, taking initiatives, the importance of diversity, and the power of practical knowledge. We finally talk about Data Science at Microsoft where we talk about my career path and the learnings I got from some of the best in the field.

content:
  <p>Neil and I discussed a different perspective on learning AI.</p>

  <p>Many people trying to switch their careers from Software Engineer or other areas to Data Scientist are struggling because they start reading books, doing courses, or spending a lot of money on Master programs, but then they can&#39;t land a job. Why?</p>

  <p>My observation is that the majority of is that these books, courses, and programs teach the AI used 20 years ago.</p>

  <p>This is great if you want to study the history of AI, but not if you want to work in applied AI. Instead of learning the AI that was used 20 years ago, consider learning the AI that is applicable today.</p>

  <p>The key to overcoming this issue is, in my opinion, to practice <strong>reverse learning</strong>: instead of starting from the basic algorithms and going to what is used today, reverse the path, start from the modern techniques, and go back to the basics. This will boost your learning rate 10x.</p>


title: Machine Learning Mastery for Career Growth at Top-Tech Corporations
slug: machine-learning-mastery-for-career-growth-at-top-tech-corporations
pub_date: 2023-02-14
keywords: machine learning; data science; career development
authors: Aishwarya Naresh Reganti and Miguel González-Fierro
youtube_video_id: rOPT4mzPXVo
meta_description:
  A set of advice on how to navigate the corporate ladder in a top tech company if you are interested in a career in machine learning

abstract:
  In this interview hosted by The LevelUp Org I provide my perspective on having an exciting career if you are interested in machine learning. Many of the ideas come from what I have observed in academia, the startup world, and big tech. I hope this is useful to people starting their careers and looking for direction.

content:
  <p>Here is a list of ideas I share.</p>

  <p>1. Reverse Learning: learn the AI that is useful in the industry today.</p>

  <p>2. Practical knowledge: doing one project on GitHub is worth 10 ML courses or books.</p>

  <p>3. The best career profile you can have&nbsp;is the T-shaped professional.</p>

  <p>4. Your resume is your GitHub profile.</p>

  <p>5. The best ML teachers are on GitHub and Kaggle.&nbsp;Contribute to open-source.</p>

  <p>6. Top&nbsp;traits: evidence of exceptional ability, derivative of experience, and culture.</p>

  <p>7. The biggest predictor of your success is your ability to imitate your mentors.</p>

  <p>8. Understand deeply the ML techniques you are using.</p>

  <p>9. The best superpower is being curious.</p>

  <p>10. Your job is to solve a business problem with AI, not to build fancy ML models.</p>


title: The Story of How Nikolaos Got a Data Science Job in Just 3 Months
slug: the-story-of-how-nikolaos-got-a-data-science-job-in-just-3-months
meta_description: How Nikolaos Outsmarted the Competition to Land a Data Science Job in 3 Months
pub_date: 2023-03-05
keywords: machine learning; data science; career development
authors: Miguel González-Fierro
abstract:
  As a mentor of Nikolaos, my primary goal was to guide him toward securing a job in the field of data science. We developed a plan of action that involved improving his resume, LinkedIn, and GitHub profiles, theoretical and practical knowledge, and training on how to approach an interview. Nikolaos was able to get a Data Science position in less than 3 months. In this post, I share his story.

content:
  <p><a href="https://www.linkedin.com/in/nikolaos-zavitsanos-22561a172/" target="_blank">Nikolaos</a> is one of the people I mentored. Through a personalized approach to mentoring, we developed a plan of action to help him reach his goals.</p>

  <p>First, we worked on enhancing his resume, LinkedIn, and GitHub profiles to ensure that they accurately represented his skills and experience in data science.</p>

  <p>Next, we honed Nikolaos&#39; Python coding skills, which are essential for data science roles. We focused on hands-on projects.</p>

  <p>In addition to coding skills, we also focused on enhancing Nikolaos&#39; knowledge of machine learning.</p>

  <p>Finally, we concentrated our energies on preparing Nikolaos for behavioral interview questions,&nbsp;which aim to evaluate his compatibility with the organization&#39;s culture and team.</p>

  <p>Ultimately, our efforts paid off, and Nikolaos landed a data science job within three months of starting our mentorship.</p>

  <p>I am proud to have played a role in his success and grateful for the opportunity to have shared my knowledge and experience with a motivated and hardworking individual like Nikolaos.</p>

  <p>Here is the transcription of the interview I did with him.</p>

  <p><img alt="Nikolaos Zavitsanos" height="229" src="/img/upload/2023/03/Nikolaos.jpg" width="229" /></p>

  <h4 class="section">Tell me a little bit about yourself</h4>

  <p>My name is Nikolaos Zavitsanos and I am from Greece.</p>

  <p>I completed a Bachelor&#39;s degree in Computer Science at City University and then a master&#39;s in Artificial Intelligence at the University of Edinburgh.</p>

  <p>My focus during my master&#39;s was NLP and recommendation systems.</p>

  <p>I am currently working as a Data Scientist in the TUI group.</p>

  <h4 class="section">Where were you working before we met and why did you want to change?</h4>

  <p>When we met I was working in a start-up as a machine learning engineer. While I really enjoyed the projects and the team, I wanted to expand my experience and exposure across multiple areas, as I am still in the early stages of my career.</p>

  <p>TUI Group presented the opportunity of broad exposure to a wide variety of projects, as well as, specifically, recommendation systems which I have been very interested in since my Master&#39;s.</p>

  <h4 class="section">How long did it take from when we started working together to when you got your new position?</h4>

  <p>It was not a lengthy process to secure my new position as I had initiated the application process promptly.</p>

  <p>Approximately two months after our last conversation, I received an offer for the position.</p>

  <h4 class="section">Of everything we discussed and the material I shared with you, what did you use to prepare for the interview?</h4>

  <p>The handbook was really useful to me. I used it all the time for my interviews, and it helped me recall what I learned during my Master&#39;s. The project anatomy section was especially helpful for my own side projects. Overall, having these&nbsp;resources made things so much easier for me.</p>

  <h4 class="section">Can you share details of the hiring process and interview?</h4>

  <p>The hiring process consisted of one 30-minute interview, followed by a 2.5-hour interview and a coding challenge.</p>

  <p>Both of the interviews included a mix of technical questions, behavioral questions, and some questions specific to my background.</p>

  <h4 class="section">What advice would you give to people who want to apply for a Data Science position?</h4>

  <p>My main piece of advice is to be targeted in your application process - only applying to and spending time on applications that you are truly interested in and that match your skill set.</p>

  <p>You should also focus on ensuring that you have the core coding skills required and a comprehensive understanding of key data science concepts.&nbsp;</p>

  <h4 class="section">How do you feel right now?</h4>

  <p>I am thrilled about my new employment opportunity. From the onset, I have been exposed to new and challenging tasks that allow me to expand my skill set. Additionally, I have the privilege of working with recommender systems, which was the subject matter of my dissertation.</p>

  <p>Overall, this is a fantastic experience.</p>

  <h4 class="section">Conclusion</h4>

  <p>In the end, Nikolaos&#39; story is one of determination, hard work, and perseverance.</p>

  <p>Through his dedication to learning and his willingness to seek out mentorship and guidance, he was able to make a remarkable transformation and secure a job in data science within a short period of time.</p>

  <p>If you like this content, consider joining my weekly email newsletter where I share ideas and strategies on how to switch your career to Data Science <a href="https://bit.ly/emaillistmiguel" target="_blank">https://bit.ly/emaillistmiguel</a>.</p>

  <p>&nbsp;</p>


title: From Cloud Solution Architect to Data Scientist in 4 Months
slug: from-cloud-solution-architect-to-data-scientist-in-4-months
pub_date: 2023-07-02
keywords: ai; machine learning; career switch
authors: Miguel González-Fierro
meta_description:
  How Luis Valencia went from Cloud Solution Architect to Data Scientist in 4 months using Reverse Learning

abstract:
  Is it possible to switch your career from Cloud Solution Architect to Data Scientist? Luis Valencia was able to secure an AI job in just 4 months following my mentoring strategy: Reverse Learning. He is currently the Lead Data Scientist in his company working on large language models. This is his story. 

content:
  <p><a href="https://www.linkedin.com/in/levalencia/" target="_blank">Luis Valencia</a> wanted to switch his career to work in AI, and he did it. He went from Cloud Solution Architect to Data Scientist, 4 months after we started working together.</p>

  <p>During our coaching sessions, we worked on different ways to improve his resume, LinkedIn profile, and AI skills. Still, the two strategies that were more impactful were <a href="https://www.linkedin.com/posts/miguelgfierro_ai-machinelearning-datascience-activity-7079333945770749953-xQCD" target="_blank">Reverse Learning</a> and <a href="https://www.linkedin.com/posts/miguelgfierro_t-shaped-professional-activity-7080427669955256320--IYj" target="_blank">T-shaped professional</a>.</p>

  <p><img alt="" height="336" src="/img/upload/2023/07/2023_06_27_Luis_Valencia.jpeg" width="350" /></p>

  <p>Let me go first through one of the concepts, Reverse Learning. The idea is simple, instead of starting from the basic AI algorithms and going to what is used today, reverse the path, start from the techniques used today in the industry, and go back to the basics.&nbsp;</p>

  <p>Data Science is one of the&nbsp;newest and coolest jobs. It&#39;s the hottest job of the XXI century, it has one of the highest salaries and we are changing the world. Many people want to switch their careers to work in AI. Some people think it is impossible for them, some think they require a PhD or an expensive Master&#39;s degree, and some believe you need to be a mathematical genius.&nbsp;</p>

  <p>They are wrong, and Luis is a good example of it. Anyone can learn AI, and anyone can switch their career and work in that field.</p>

  <p>The question is, how?</p>

  <h4 class="section">Reverse Learning</h4>

  <p>Reverse Learning is a learning methodology that maximizes usefulness. You prioritize the skills that companies are requesting to&nbsp;increase the chances to switch your career and find a job in the field.</p>

  <p>Let&#39;s do a thought experiment.&nbsp;It could be argued that the oldest job in human history is fighting. We have been fighting for thousands of years.</p>

  <p>Now, imagine that I am called to become a soldier, so I have to switch my career from Data Scientist to soldier, and I have to do it as fast as possible. What would I do?</p>

  <p>Would I start studying the techniques and strategies of the best generals of history like Alexander the Great or Julius Ceasar? No.</p>

  <p>Would I spend hours and hours reading books and novels about war? No.</p>

  <p>Would I study the history of war, from the Roman Empire to the Middle Ages, to the Napoleonic Wars? No.</p>

  <p>Probably, I would learn how to operate a $100k drone that can destroy a $50M tank.&nbsp;</p>

  <p>Learning how to conquer Asia with Elephants like Alexander the Great did&nbsp;is not useful. Learning to operate a drone is.&nbsp;</p>

  <p>Going back to today, here is the reason why a lot of people can&#39;t switch their careers to AI and fail the interviews: they learn the history of AI, instead of learning the&nbsp;AI that is used in companies today.&nbsp;</p>

  <p>People know everything about linear regression, but nothing about Gradient Boosting Decision Trees (GBDT).&nbsp;They know LSTMs, but not Transformers.</p>

  <p>Companies with tabular data need people who understand GBDT, companies with text data need people who understand Transformers.</p>

  <p>Reverse learning is all about prioritizing what is useful and then reversing to other areas.</p>

  <p>Are you still not convinced? Do you think it is impossible to learn Transformers without learning first SVM, LSTMs, etc? Do you think that others can do it, but not you? Next is the journey of Luis.</p>

  <h4 class="section">Luis&#39; Journey to Data Scientist</h4>

  <p><strong>1.&nbsp;Tell me a little bit about yourself</strong></p>

  <p>I have 20 years of experience in total, as Software Developer, Cloud Solutions Architect, and a few years ago I wanted to move to something more challenging and rewarding.</p>

  <p>As Architect I felt that I wasn&#39;t doing enough to have my brain upskilled.</p>

  <p><strong>2.&nbsp;Where were you working before, we met and why did you want to change?</strong></p>

  <p>In one of the Big 4 in Belgium, I wanted to move because there is no AI team there, they don&#39;t invest in AI or Data Science, and the manager was not great at managing technically skilled people. So, I decided to start looking for something more fun, more rewarding, and with a better manager of course. I was in a team of 1, doing everything with no teamwork at all, it was very frustrating.</p>

  <p><strong>3. How long did it take from when we started working together&nbsp;to when you got your new position?</strong></p>

  <p>Less than 4&nbsp;months</p>

  <p><strong>4. Of everything we discussed and the material I shared with you, what did you use to prepare for the interview?</strong></p>

  <p>I worked a lot with my GitHub portfolio, more than 10 projects are already uploaded, and I worked a lot on an improved Data Science version of my CV, this was indeed the most important aspect, it got me many interviews.<br />
  <br />
  <strong>5. How do you feel right now?</strong></p>

  <p>I am very happy, the company I work for&nbsp;cares a lot about people&#39;s technical skills.&nbsp;There is time allocated for everyone to upskill. And I am working on the State of The Art! LLMs, OpenAI, LangChain, HuggingFace, etc. It&#39;s like a dream come true. Now I have time to learn what I want which was in line with the vision of my employer and I do this by actually practicing and building products for clients.</p>

  <p><strong>6. What advice would you give to people applying for a Data Science position?</strong></p>

  <p>Become a T-shaped professional as advised by Miguel, and Reverse Learning,&nbsp;these 2 things changed the way I was approaching learning.</p>


title: Reverse Learning: How to learn the AI used today in the industry
slug: reverse-learning-how-to-learn-the-ai-used-today-in-the-industry
meta_description: How to learn the AI that is used today in the industry: Practice Reverse Learning
pub_date: 2023-08-02
keywords: ai; machine learning; data science
authors: Miguel González-Fierro
abstract:
  Many people trying to switch their careers to Data Science can't land a job even if they spend tons of time and money on AI training. Why? Because most of the training teaches the AI used 20 years ago instead of the AI that companies use today. Data Science teams want candidates who can contribute to their business from day one. To successfully transition to a Data Science career you need to prioritize the AI that is useful for companies. That's the basis of Reverse Learning.

content:
  <p>Today I want to talk about the concept of <strong>Reverse Learning</strong>.</p>

  <p>It helped me to get into Microsoft, it helped me to get the best thesis award for my PhD, and it helped people that I&rsquo;m coaching like <a href="https://miguelgfierro.com/blog/2023/the-story-of-how-nikolaos-got-a-data-science-job-in-just-3-months/" target="_blank">Nikolaos Zavitsanos</a>, <a href="https://miguelgfierro.com/blog/2023/from-cloud-solution-architect-to-data-scientist-in-4-months/" target="_blank">Luis Valencia</a>, and many others&nbsp;to get their dream job in Data Science.</p>

  <p>Now, hopefully, you will agree with me that an interview process is like an exam to check whether you are a good contribution to their team. If the interviewers think that you have enough knowledge and practical experience to contribute to the team from day 1 with the AI solutions they are currently implementing, then they will hire you (you passed the exam).</p>

  <p>Tell me if you have already gone through this process, you have read some Data Science books, done some courses, or even a Master&rsquo;s degree. However, you still can&rsquo;t land the job you want. Is that you?</p>

  <p>You might be frustrated, you might be tired, and you are about to give up. But somehow you have the suspicion that there is a way to join Google, Microsoft, or whatever company you like.</p>

  <p>The core idea that hopefully changes your belief system is that the reason that you are not getting that job you want is not because you are not valid for that company.<strong>&nbsp;The reason is that you are learning Data Science in the wrong order and you are learning the wrong things</strong>. You need to learn the AI that is used in the industry TODAY, not the AI that was used 20 years ago.</p>

  <p>Now the best way I found to learn the AI that is used today is a process I call Reverse Learning: instead of starting from the basic algorithms and going to what is used today, reverse the path, prioritize the techniques used in the industry today, and go back to the basics.</p>

  <p>Now let me tell you a story about an epiphany moment I had when I was studying for my PhD that made me go from wasting my time for 6 months to starting publishing papers straight away.</p>

  <p>One Professor suggested I read several books related to robotics and control engineering. They were the traditional and most cited books about robotics and control engineering, with hundreds of pages and super small typography. After several months of deep study, I went to my advisor with the top technique they said in the book. He said: &ldquo;See Miguel, this technique works in theory because it assumes that the theoretical model of the robot is the same as the real robot, but in practice, it does not work. So your technique is not going to work in the real robot&rdquo;.</p>

  <p>I left that meeting thinking that I had wasted 6 months of my life. I was tired and frustrated, and I thought I was not going to be able to publish anything.</p>

  <p>Then I decided to change my approach. Instead of going through more books, I would start reading state-of-the-art papers. I started reading a technique called Learning from Demonstration, by a person called Sylvain Callinon. I read all his papers and in a month I had my first experiment with the robot using <a href="https://www.youtube.com/watch?v=ugQOH3PT9wA" target="_blank">Learning from Demonstration</a>. Soon after, my first paper was accepted. See what happened? I reversed the learning path. Instead of studying the history of robotics, I studied the robotic techniques that were used today.</p>

  <p>If you think about it deeply, don&rsquo;t you think it doesn&rsquo;t make sense to try to get into a high-performance team not knowing the AI that they use today? How do you expect to be hired? How do you expect to contribute to the team from day 1?</p>

  <p>Here is a mental exercise to help you understand why reverse learning makes so much sense. Let&rsquo;s say you want to lead an army to war today. Then instead of studying modern warfare techniques, like how people are using drones to hit the enemy, you decided to study everything about Julius Cesar and the Roman legions. Cesar was arguably the best general of all time, right?</p>

  <p>Wouldn&#39;t it be crazy to study the Roman legions if you want to lead an army today? Would you get a job in the Pentagon, or would you get a job in the History Department of Harvard? It is the same&nbsp;with AI.</p>

  <p>Now some questions that you might be thinking:</p>

  <p>Q: But if you don&rsquo;t learn the basic techniques of AI, you will know nothing.</p>

  <p>A: Notice that I&rsquo;m not saying that you shouldn&rsquo;t learn basic techniques like linear regression. Reverse learning is about reversing the learning path, starting from the techniques that are used today, and then going to the fundamentals. What I&rsquo;m proposing is to make smarter use of your time.</p>

  <p>Q: But I can&rsquo;t learn the techniques that are used in the industry today if I don&rsquo;t start from the basics.</p>

  <p>A: Yes you can. It will take another whole letter to explain to you the methodology to do it, but in <a href="https://www.linkedin.com/posts/miguelgfierro_machinelearning-datascience-ai-activity-6961202603774889984-x-pO/" target="_blank">this post</a>,&nbsp;you can find an explanation of one of the most effective algorithms that are used today: Gradient Boosting Decision Trees (GBDT). And notice that I didn&rsquo;t need to explain linear regression or logistic regression to explain GBDT.</p>

  <p>Q: But I&rsquo;m skeptical about this method, it seems to be a learn Data Science in 5 minutes thing, and mastering Data Science takes a long time</p>

  <p>A: Again, Reverse Learning is just reversing the order you learn, to understand the techniques that are most useful in the industry. It takes 10,000 hours to master a field. All I&rsquo;m saying is that it is a better use of your time if during the first 1,000 hours&nbsp;you learn the techniques that are used in the industry today, so you can join Google, and then spend the other 9,000h learning AI inside Google.</p>

  <p>I hope this new idea of Reverse Learning is useful to you. A lot of people have followed it and have been able to transition their careers to Data Science.</p>

  <p>This letter was part of&nbsp;my private machine learning community where I share ideas and strategies on how to switch your career to Data Science.&nbsp;If you think this content is valuable, <a href="https://bit.ly/emaillistmiguel" target="_blank">join the community</a>.</p>


