Speaker 1: Hey, everyone, welcome to the deep dive. So data driven
Speaker 1: decisions are pretty much everywhere.
Speaker 2: Now, right, absolutely, and machine learning is you know, a
Speaker 2: massive part of that picture.
Speaker 1: It really is. But let's be honored. Jumping into machine
Speaker 1: learning it can still feel like, well, like a whole
Speaker 1: new world to navigate for a lot of folks. Definitely,
Speaker 1: what if there was something powerful, really versatile and genuinely accessible,
Speaker 1: especially if you're already working.
Speaker 2: In the dot net world, And that's exactly what we're
Speaker 2: digging into today. We're doing a deep dive into mL
Speaker 2: dot net. That's Microsoft's machine learning framework, right.
Speaker 1: Our mission here is basically to pull out the most
Speaker 1: important stuff from the well the comprehensive guide on it.
Speaker 1: We want to give you a real shortcut.
Speaker 2: Yeah, it gets you well informed on building, optimizing, and
Speaker 2: deploying machine learning models using mL dot net exactly.
Speaker 1: You'll get the basics, see how it's actually used, understand
Speaker 1: the steps even if you're not you know, a machine
Speaker 1: learning guru.
Speaker 2: Right now, So where did it even come from? Let's
Speaker 2: start there, MLT and that it actually started life as
Speaker 2: an internal thing at Microsoft.
Speaker 1: Oh interesting, like an in house tool exactly.
Speaker 2: Which kind of shows you how central machine learning was
Speaker 2: becoming to them even back then.
Speaker 1: And what's really compelling is that then in twenty eighteen
Speaker 1: they did something pretty big. They released it as open source,
Speaker 1: which wasn't just like dropping code. It was really an invitation,
Speaker 1: wasn't it.
Speaker 2: It really was. It means mL dot net has grown massively,
Speaker 2: not just from Microsoft engineers, but the whole developer community
Speaker 2: chipping in.
Speaker 1: That open source approach that brings transparency, collective brain power. Yeah,
Speaker 1: makes it really solid totally.
Speaker 2: And the real power I think comes down to how
Speaker 2: accessible and versatile it is. It's not just built for
Speaker 2: one specific kind of problem. It handles a whole bunch
Speaker 2: of mL tasks, you know, classification, regression, clustering, even those
Speaker 2: complex recommendation systems. It's like a full AI toolkit.
Speaker 1: And its platform independent, runs on Windows.
Speaker 2: LFS, YEP, works wherever you work.
Speaker 1: But the real kicker, I think, especially for listeners who
Speaker 1: are dot net devs, is that deep integration.
Speaker 2: Oh absolutely.
Speaker 1: If you're already working in C shark, F sharp, maybe
Speaker 1: VB dot net, you use the tools, you know, the libraries,
Speaker 1: you know, that barrier to entry just drops way down.
Speaker 2: It does, and that adaptability it means you can stick
Speaker 2: machine learning smarts right into your desktop apps, web apps, mobile.
Speaker 1: Apps, So it's not just for dedicated mL engineers anymore, exactly.
Speaker 2: It kind of democratizes it within the dot net space.
Speaker 2: Any dot net dev can start using AI.
Speaker 1: Okay, So inevitably people will ask, how does it compare,
Speaker 1: you know, to other frameworks out there, Like psyche learn
Speaker 1: is huge in the Python world, right it is.
Speaker 2: And it's fantastic. Psychic learn offers a really comprehensive set
Speaker 2: of tools for Python developers.
Speaker 1: No doubt.
Speaker 2: But mL net well, mlnet really shines in its niche,
Speaker 2: which is accessibility and just ease of use, especially if
Speaker 2: you're already comfortable in dot net. The big advantage is
Speaker 2: working where you already live, basically, which leads to the question, right,
Speaker 2: why go learn a whole new language and tool chain.
Speaker 1: If you can bring mL right into your existing c
Speaker 1: sharp projects, say.
Speaker 2: Exactly, leverage the skills you've already got.
Speaker 1: That makes a ton of sense. Using what you know
Speaker 1: is always faster. Okay, But maybe stepping back for a
Speaker 1: second for someone who's heard machine learning but isn't totally clear, Yeah,
Speaker 1: can we break down the basics what is it? And
Speaker 1: then how does mL dot net help.
Speaker 2: Yeah, good idea. So, at its heart, machine learning is
Speaker 2: about letting computer systems learn from data without you having
Speaker 2: to explicitly program every single rule.
Speaker 1: Okay, So they learn how like recognizing patterns?
Speaker 2: Yeah, making predictions, precisely recognizing patterns, making predictions, even getting
Speaker 2: better over time based on the data they.
Speaker 1: See, which naturally brings up What are they learning from?
Speaker 1: That probably needs a few key terms.
Speaker 2: Right, it does. So first you've got data that's just
Speaker 2: the raw stuff, text, images, numbers, whatever.
Speaker 1: You're working like, Okay, data, got it.
Speaker 2: Then within that data you have features. These are the
Speaker 2: specific measurable characteristics or tributes the model looks at.
Speaker 1: Like if you're predicting house prices, the features might be
Speaker 1: square footage, number of.
Speaker 2: Deadrooms exactly, location, age of the house, those kinds of things,
Speaker 2: things that influence the outcome.
Speaker 1: Okay. And then labels.
Speaker 2: Labels are the answer. You're trying to predict the target variable.
Speaker 2: So in spam detection, the label is spam or not.
Speaker 1: Spam, simple enough, And the models.
Speaker 2: Are the models are the algorithms themselves. They're trained on
Speaker 2: the data to learn the relationship between the features and
Speaker 2: the labels. They're kind of the brain that makes the prediction.
Speaker 1: Gotcha. So data features, labels, models the building.
Speaker 2: Blocks, right, and these models learn in different ways. The
Speaker 2: main paradigm is probably supervised learning.
Speaker 1: Supervised meaning it has the answers already.
Speaker 2: Kind of it learns from labeled data data where you
Speaker 2: already know the correct output the label. This is super
Speaker 2: common for tasks like.
Speaker 1: Classification assigning categories.
Speaker 2: Right, or regression where you're predicting a continuous number like
Speaker 2: a price.
Speaker 1: Okay, what else?
Speaker 2: Then you have unsupervised learning. This deals with unlabeled data.
Speaker 2: The goal isn't to predict a known label, but to
Speaker 2: find hidden structures or patterns within the data itself.
Speaker 1: Ah, so like grouping similar customers together exactly.
Speaker 2: Clustering is a classic example, or simplifying data, which is
Speaker 2: dimensionality reduction. And just briefly, there's also reinforcement learning training
Speaker 2: agents through rewards and penalties, and semi supervised which mixes
Speaker 2: labeled and unlabeled data.
Speaker 1: That's quite a spectrum, and mL net has tools for these.
Speaker 2: It does for a really wide array. Take classification that's
Speaker 2: putting data into pre defined buckets. It could be binary classification.
Speaker 1: Just two options like spam not spam, fraud not fraud.
Speaker 2: Sentiment analysis positive and negative review all those yes, no
Speaker 2: type questions.
Speaker 1: Or it could be multi class classification more than two
Speaker 1: options yep.
Speaker 2: Think recognizing handwritten numbers zero through nine, or sorting news
Speaker 2: articles into topics like sports, politics, tech.
Speaker 1: Okay, what about predicting numbers.
Speaker 2: That's regression, predicting house prices, stock prices, sales figures, any
Speaker 2: numerical value. Mlnet is great at that too.
Speaker 1: And you mentioned clustering earlier, finding.
Speaker 2: Groups right Grouping similar data points without knowing the groups beforehand,
Speaker 2: really useful for customer segmentation, finding different user types, or
Speaker 2: even anomaly detection in some cases.
Speaker 1: Speaking of anomaly detection, finding the odd ones.
Speaker 2: Out exactly, spotting unusual patterns that don't fit the norm.
Speaker 2: Think network intrusion detection or monitoring machine health for predictive maintenance.
Speaker 1: It sounds incredibly useful, and.
Speaker 2: It goes further ranking search results, building recommendation engines, forecasting
Speaker 2: future trends, image classification, object detection in images. It comes
Speaker 2: a lot of ground.
Speaker 1: That's a seriously broad toolkit, and what gets really interesting
Speaker 1: is seeing how this translates into actual business value. Can
Speaker 1: we talk about some real world examples.
Speaker 2: Sure, look at e commerce recommendation systems are huge, suggesting
Speaker 2: products you might like based on what you've looked at
Speaker 2: or bought.
Speaker 1: Amazon saying customers who bought this also bought precisely.
Speaker 2: That's mL It also drives dynamic pricing, adjusting prices based
Speaker 2: on demand, and analyzing customer reviews automatically for sentiment.
Speaker 1: And in healthcare, I know there's a lot happening there, huge.
Speaker 2: Potential things like medical diagnoses support predicting disease risk. You
Speaker 2: mentioned the Cleveland Clinic heart disease data earlier. Mlnet can
Speaker 2: analyze data like that to help identify patterns linked to
Speaker 2: heart disease, potentially leading to earlier diagnoses.
Speaker 1: Wow, that's impactful definitely.
Speaker 2: Then in manufacturing, think predictive maintenance. Using sensor data to
Speaker 2: predict when a machining might fail before it happens. Saves
Speaker 2: a lot of downtime and money.
Speaker 1: Makes sense.
Speaker 2: Finance, big use cases in fraud detections, spotting suspicious transactions
Speaker 2: in real time, and complex risk assessment for loans or investments.
Speaker 1: And even just everyday customer service.
Speaker 2: Yeah, powering smarter chatbots that can actually understand and resolve issues,
Speaker 2: or automatically analyzing customer feedback, emails or calls for sentiment
Speaker 2: to see if people are generally happy or frustrated. It's
Speaker 2: really weaving into.
Speaker 1: Everything, it really is. Okay, So, if someone's convinced they
Speaker 1: want to try this, what's the actual process? The workflow
Speaker 1: for building a model with mL dot net right.
Speaker 2: mL dot net guides you through a pretty clear logical workflow.
Speaker 2: It breaks down into data preparation, then model training, followed
Speaker 2: by model evaluation and tuning, and finally model deployment and integration.
Speaker 1: A structured approach keeps things manageable exactly. Okay, let's start
Speaker 1: the beginning data preparation you said earlier, garbage in, garbage out.
Speaker 1: This sounds critical.
Speaker 2: It absolutely is. The quality and honestly the quantity of
Speaker 2: your data directly shaped how good your model can be.
Speaker 2: So getting the data right is job one.
Speaker 1: So how do you ensure good data? Step one is
Speaker 1: just getting it right?
Speaker 2: Data collection yep, gathering it from wherever it lives, text files, csvs, databases, APIs.
Speaker 2: mL dot net helps here with tools like textloader for
Speaker 2: files and databaseloader for databases, simplifying that import process.
Speaker 1: Okay, So You've got the raw data, but it's probably messy.
Speaker 2: Almost always, which brings us to data cleaning. This is
Speaker 2: about finding and fixing problems, errors, inconsistencies, missing values big
Speaker 2: part of the job.
Speaker 1: How do you fix like missing values?
Speaker 2: A couple of ways. You could just remove rows or
Speaker 2: columns that have missing data using something like drop missing values,
Speaker 2: but you have to be careful there. You don't want
Speaker 2: to throw away too much valuable info. Often a better
Speaker 2: approach is imputation. That's where you replace the missing value
Speaker 2: with best guess, maybe the average mean or the middle
Speaker 2: value median for that feature. mL net has tools like
Speaker 2: replace missing values for that.
Speaker 1: Okay, clean data, What next? I remember you mentioned things
Speaker 1: like text labels need.
Speaker 2: Handling, right, that's data encoding. Most algorithms need numbers, not text,
Speaker 2: like red or blue, so you have to convert categorical data.
Speaker 2: Common ways are one hot encoding, where each category red, green,
Speaker 2: blue gets its own column with a one or zero.
Speaker 1: Ah. Okay, so like arid is green is blue exactly?
Speaker 2: Or label encoding, where you just assign a unique number
Speaker 2: to each category like red one, green, two, blue three.
Speaker 2: The choice depends on the algorithm and the data.
Speaker 1: Got it anything else in data prep, Yes.
Speaker 2: Super important. Normalization and scaling. Think about your features. Age
Speaker 2: might be twenty to eighty, well, income might be thirty
Speaker 2: thousand to five hundred thousand. Huge difference in scale.
Speaker 1: And that difference can mess up the model.
Speaker 2: It can. Some algorithms give more weight to features with
Speaker 2: larger values just because they're bigger numbers, not because they're
Speaker 2: more important. So scaling puts everything on a level playing field.
Speaker 1: How do you do that?
Speaker 2: Techniques like standardization, which transforms data to have a mean
Speaker 2: of zero and a standard deviation of one or minmac scaling,
Speaker 2: which squishes everything into a specific range usually zero to one.
Speaker 1: Okay, that makes sense. Prevent one feature from dominating exactly.
Speaker 2: And the last piece, which can be really powerful is
Speaker 2: feature engineering.
Speaker 1: Engineering features like creating new ones precisely.
Speaker 2: It's about using your domain knowledge or sometimes just creativity
Speaker 2: to transform the raw data into features that are more
Speaker 2: meaningful for the model.
Speaker 1: Like the VMI example you gave earlier, calculating it from
Speaker 1: heightened weight.
Speaker 2: Perfect example, instead of giving the model raw height and weight,
Speaker 2: you give it BMI, which might be a much better
Speaker 2: predictor for certain health outcomes. Good feature engineering can seriously
Speaker 2: boost your model's accuracy, make it easier to understand, and
Speaker 2: even help prevent overfitting. It's where art meets science.
Speaker 1: A bit fascinating. So data prep is done. Now we
Speaker 1: get to the core right model training, evaluation, and tuning.
Speaker 2: Yep, the brains of the operation, and mlnet gives you
Speaker 2: a couple of great ways to approach this. The mL
Speaker 2: dot net Model Builder and the mL dot net Cli.
Speaker 1: Okay, tell me about the model builder. Sounds friendly, It
Speaker 1: really is.
Speaker 2: It's a graphical tool right inside visual studio. Makes things
Speaker 2: much simpler, especially if you're starting out. It helps with
Speaker 2: pre processing, picking models. It even does some automated feature
Speaker 2: engineering and hyper parameter tuning for you.
Speaker 1: So it hides some of the complexity it does.
Speaker 2: You can visually pick your scenario like sentiment analysis using
Speaker 2: Yelp reviews, point it at your data, choose where to
Speaker 2: run it like your local machine, and it guides you through.
Speaker 2: It even suggests algorithms.
Speaker 1: Nice. What about the cli command line interface.
Speaker 2: That's for folks who like the command line or need automation.
Speaker 2: It's very powerful for scripting for integrating mL model training
Speaker 2: into say a CICD pipeline or other automated workflows.
Speaker 1: How do you get these tools?
Speaker 2: Pretty standard dot net ways. Now get package manager for
Speaker 2: the libraries and for the cli. It's a dot net tool.
Speaker 2: So dot net tool installed, gml net straightforward.
Speaker 1: Okay, tools installed. How does the actual training and evaluation work?
Speaker 2: Well, First you pick a learning algorithm. mL net calls
Speaker 2: these trainers and they cleverly package an algorithm with a
Speaker 2: specific task type. Then, crucially, you split your prepared.
Speaker 1: Data into training and testing sets exactly.
Speaker 2: You train the model on the training set, let it
Speaker 2: learn the patterns. Then you use the testing set that
Speaker 2: data the model hasn't seen before to evaluate how well
Speaker 2: it performs.
Speaker 1: And how do you measure performance.
Speaker 2: Depends on the task. For classification, you might look at accuracy,
Speaker 2: how many did it get right? Maybe log loss. For regression,
Speaker 2: common metrics are mean absolute error MAE or root means
Speaker 2: squared error RMS, measuring how fall off the predictions were
Speaker 2: on average.
Speaker 1: Okay, and choosing the algorithm the trainer you said, it's
Speaker 1: part art, part science.
Speaker 2: It is factors like your data size and type, how
Speaker 2: fast you need training to be, how complex or interpretable
Speaker 2: you need the model to be. They all play a role.
Speaker 1: mL net has trainers for all the tasks.
Speaker 2: You mentioned pretty much. Yeah, like says care aggression trainer
Speaker 2: for regression, like to be a binary trainer for binary classification,
Speaker 2: K means trainer for clustering, and many others.
Speaker 1: And I remember you mentioned o NNX. Some trainers can
Speaker 1: export to that.
Speaker 2: That's right. O NNX is the Open Neural Network Exchange format.
Speaker 2: Being able to export means you can train in mL
Speaker 2: dot net, but maybe deploy or use the model in
Speaker 2: other environments or frameworks like TensorFlow or PyTorch huge for interoperability.
Speaker 1: What kinds of algorithms are we talking about? Broadly?
Speaker 2: A good mix. You've got linear models like average perceptron
Speaker 2: often good for text, powerful decision tree methods like light GBM,
Speaker 2: fast tree, fast force, great vertabular data gams which are
Speaker 2: good for explainability, SVMs K means for clustering, naive bays,
Speaker 2: even matrix factorization for building recommendation systems. Lots of choice.
Speaker 1: Okay, so you train a model, evaluate it, but maybe
Speaker 1: it's not good enough yet. What's hyper parameter tuning?
Speaker 2: Ah? Good question. Hyper Parameters are like the settings or
Speaker 2: knobs on your learning algorithm before you start training. They
Speaker 2: aren't learned from the data itself. You the developer, set them.
Speaker 1: What do they control?
Speaker 2: Things like how fast the model learns, learning rate, how
Speaker 2: complex it's allowed to get, how much it tries to
Speaker 2: avoid overfitting regularization, Maybe things like maximum number of iterations,
Speaker 2: how long it trains for.
Speaker 1: You need to find the best setting for these knobs exactly.
Speaker 2: That's tuning. Mlnet offers ways to help automate this, like
Speaker 2: grid search trying all combinations in a grid, or you
Speaker 2: can implement things like random search or Bayesian optimization to
Speaker 2: explore the possibilities more intelligently.
Speaker 1: Seems important for squeezing out performance. What about cross validation?
Speaker 2: Heard that term a lot crucial technique. It helps you
Speaker 2: get a more reliable estimate of how well your model
Speaker 2: will actually perform on unseen data, and it's a key
Speaker 2: way to fight overfitting.
Speaker 1: Does it work?
Speaker 2: The most common type is kfold cross validation. You split
Speaker 2: your data into say k five chunks or folds. You
Speaker 2: train the model five times. Each time, you train on
Speaker 2: four folds and test on the one left overfold, rotating
Speaker 2: which fold is the test set.
Speaker 1: Ah, So you test on all the data eventually, but
Speaker 1: never on data was trained on in that specific run.
Speaker 2: Exactly, then you average the performance across the five runs.
Speaker 2: Gives you a much more robust evaluation than a single
Speaker 2: train to split.
Speaker 1: Are there variations?
Speaker 2: Yep, Leave one out is where k equals your number
Speaker 2: of data points. Very thorough but slow for large data
Speaker 2: sets and stratified kfold is important for imbalanced data ensuring
Speaker 2: each fold has roughly the same proportion of different classes
Speaker 2: as the original data set.
Speaker 1: Okay, that whole training and tuning process makes sense. So
Speaker 1: you've got a model you're happy with. What's next? Model
Speaker 1: deployment and integration, getting.
Speaker 2: It out there right, bringing it to life. And a
Speaker 2: key part of this is being able to save and
Speaker 2: load your trained model. You want to preserve this hard
Speaker 2: earned knowledge.
Speaker 1: Right, Definitely don't want to retrain every.
Speaker 2: Time, no way. Reusing pre trained models saves a massive
Speaker 2: amount of time. So the process involves serialization serialization. It's
Speaker 2: basically converting the model's internal state, its structure, the learned weights,
Speaker 2: all metadata into a format that can be saved to
Speaker 2: a file, usually a compact byte stream.
Speaker 1: Okay, saving it and getting it back.
Speaker 2: That's de serialization, reading that saved file, that byte stream,
Speaker 2: and converting it back into a working model. Object in
Speaker 2: your application ready to make predictions.
Speaker 1: What format does it save in?
Speaker 2: Mlnet's default is its own native binary format. It's generally
Speaker 2: the fastest and supports all mL net features, so it's
Speaker 2: often recommended.
Speaker 1: But you also mentioned ONNX before, Yes.
Speaker 2: And that's a huge plus. You can also save models
Speaker 2: in the ONNX format because ONX is an open standard.
Speaker 2: This means you can potentially take your mL net trained
Speaker 2: model and use it in Python with TensorFlow, or deploy
Speaker 2: it on specialized hardware or in cloud services that understand ONX.
Speaker 2: Big for flexibility makes sense.
Speaker 1: Any tips for managing these saved models.
Speaker 2: Yeah, good practices help. Maybe save the model architecture and
Speaker 2: the learned weight separately if you update weights. Often use
Speaker 2: clear versioning in your file names like sentiment, dash model,
Speaker 2: dash v one by two, dot zip, have good naming conventions,
Speaker 2: and maybe add integrity checks like file hashing to make
Speaker 2: sure the model file hasn't been corrupted when you load it.
Speaker 1: Okay, model saved now, getting it used. Deploying to the
Speaker 1: cloud seems like a common goal.
Speaker 2: Very common, and for good reason. The cloud offers huge advantages.
Speaker 2: Scalability is a big one. Automatically handle more or less prediction, traffic, accessibility,
Speaker 2: use your model from anywhere, cost effectiveness, often pay as
Speaker 2: you go, and real time inference for applications needing instant
Speaker 2: predictions like fraud detection you mentioned exactly, or live recommendations.
Speaker 2: And mlnet is designed to be cloud agnostic. It works
Speaker 2: well with Azure of course, but also AWS, Google Cloud.
Speaker 1: How do you typically deploy it? Say, on Ashure?
Speaker 2: A very popular pattern is using Azure functions. That's serverless compute.
Speaker 2: You upload your code, including loading and running your model,
Speaker 2: and Azure handles the scaling automatically. You combine that with
Speaker 2: a WebAPI, often using something like ASP dot net core
Speaker 2: to expose your model's prediction function of the simple rest
Speaker 2: endpoint over HTTP.
Speaker 1: So other applications can just call that API to get
Speaker 1: predictions precisely.
Speaker 2: Web apps, mobile apps, IoT devices, other back end services.
Speaker 2: They just make a standard web request to your azufunction API,
Speaker 2: send the input data and get the prediction back.
Speaker 1: Well, that's pretty seamless. What's the process look like?
Speaker 2: Generally you'd prepare your mL dot net model, maybe converted
Speaker 2: to onnx for broad compatibility with an Azure services. Then
Speaker 2: you set up an Azure function usually with an HTTP trigger.
Speaker 2: You write the function code to load the model and
Speaker 2: implement the API endpoint logic to handle incoming requests and
Speaker 2: return predictions.
Speaker 1: Okay, deployed, but the real world is messy, right, How
Speaker 1: do you make sure your deployed model keeps working well?
Speaker 1: That sounds like a whole other challenge.
Speaker 2: It is, and that's where best practices for production environments
Speaker 2: come in. It's not enough just to deploy. You have
Speaker 2: to maintain and monitor like what kind of things? First,
Speaker 2: performance and optimization. Constantly monitor things like prediction, speed, latency,
Speaker 2: how many requests that can handle throughput, and critically its
Speaker 2: accuracy over time. You might use techniques like quantization to
Speaker 2: make the model smaller and faster, or batch processing if
Speaker 2: you can predict on multiple inputs at once, or passing
Speaker 2: results and scaling. Definitely leveraging the cloud's auto scaling features
Speaker 2: is key. Maybe setting up distributed inference. If one instance
Speaker 2: isn't enough.
Speaker 1: What about security? You're exposing an API.
Speaker 2: Crucial security and compliance. You need proper authentication and authorization.
Speaker 2: Who is allowed to call your API? Tools like Azure,
Speaker 2: Active directory or simple API keys can help. Always use
Speaker 2: SSLTLS encryption, validate the input data rigorously and be mindful
Speaker 2: of data privacy regulations.
Speaker 1: And if things go wrong, we're just keeping it on it.
Speaker 2: That's monitoring and logging. You need good telemetry, track requests, errors,
Speaker 2: performance metrics. Tools like Azure Monitor and Application Insights are
Speaker 2: essential for this set up. Health checks for your API.
Speaker 1: Endpoint makes sense? What about updates? Models probably need retraining
Speaker 1: or replacing eventually.
Speaker 2: Absolutely so, you need a strategy for versioning and rollbacks.
Speaker 2: How do you deploy a new version of the model
Speaker 2: without downtime? And just as important, how do you quickly
Speaker 2: roll back to a previous version if the new one
Speaker 2: has problems?
Speaker 1: Right?
Speaker 2: And finally, troubleshooting, have a plan, understand how to analyze
Speaker 2: error logs. Be prepared for model drift.
Speaker 1: Model drift, Yeah.
Speaker 2: That's when the real world data your model season production
Speaker 2: starts to look different from the data it was trained on.
Speaker 2: Maybe customer behavior changes or the environment shifts. This can
Speaker 2: make your model less accurate over time, so they need
Speaker 2: to detect it and likely retrain your model on newer data.
Speaker 2: And of course, know how to use the debugging tools
Speaker 2: your cloud platform offers.
Speaker 1: Wow, Okay, that's a lot more involved than just model
Speaker 1: to predict it is production.
Speaker 2: mL is a whole discipline, but mL net and cloud
Speaker 2: platforms provide the tools to manage it effectively.
Speaker 1: So to recap, then we've gone from the origins of
Speaker 1: mL dot net its core ideas, the different tasks that
Speaker 1: can handle right.
Speaker 2: Through the whole workflow prepping, data, training, tuning, evaluating.
Speaker 1: And finally saving, deploying and managing those models in production,
Speaker 1: especially using cloud tools like Azure Functions.
Speaker 2: Yeah, hopefully you listening now have a much clearer picture
Speaker 2: of how mL dot net empowers dot net developers, specifically
Speaker 2: how it brings AI capabilities into their world, making compast
Speaker 2: things more approachable, more impactful.
Speaker 1: What's really exciting to me is that it's not static,
Speaker 1: right Yeah. Mlnt is constantly evolving.
Speaker 2: Oh yeah, new algorithms get added, the auto mL features
Speaker 2: get smarter, the community keeps growing and contributing. It's a
Speaker 2: very active project.
Speaker 1: So as you, our listeners, think about your next project,
Speaker 1: your next data challenge, maybe consider the possibilities here. What
Speaker 1: opens up when you can bring machine learning into your
Speaker 1: existing dot net skill set. This knowledge is just theoretical,
Speaker 1: it's built to be shared and used. The fascinating question
Speaker 1: is How will you use a tool like mL dot
Speaker 1: net to transform your next big idea