Speaker 1: Welcome back to the deep dive, where we unpack complex
Speaker 1: topics and bring you the essential insights. Today we're navigating
Speaker 1: the exciting world of machine learning on Amazon Web Services.
Speaker 1: Our mission for this deep dive is really to distill
Speaker 1: the core concepts of machine learning, walk you through its
Speaker 1: end to end life cycle, the whole process, and also
Speaker 1: highlight how AWS services provide a well powerful toolkit for
Speaker 1: every single stage. We're drawing our insights from the AWS
Speaker 1: Certified Machine Learning Specialty MLSC zero one certification guide, trying
Speaker 1: to pull out those aha moments and strategic takeaways. The
Speaker 1: goal is to make you feel truly well informed, whether
Speaker 1: you're a strategizing for a meeting or just really curious
Speaker 1: about this field.
Speaker 2: Yeah, and what's truly valuable in this guide and what
Speaker 2: we'll focus on today is its ability to break down
Speaker 2: these complex mL ideas into actionable knowledge. We're going to
Speaker 2: try and give you a clear roadmap basically from foundational
Speaker 2: definitions right through to practical AWOS applications, showing you how
Speaker 2: you might build truly intelligent solutions.
Speaker 1: Okay, let's dig in to this.
Speaker 2: Then.
Speaker 1: The guide starts by clarifying the relationship between artificial intelligence
Speaker 1: machine learning and deep learning. I like the analogy they use.
Speaker 1: Think of it like a set of nested Russian dolls exactly.
Speaker 2: So at the outermost layer you've got artificial intelligence AI.
Speaker 2: That's the really broad field, aiming to create machines that
Speaker 2: can do tasks mimicking human intelligence. Then moving inward, machine
Speaker 2: learning mL is a key subset of AI. This is
Speaker 2: where systems learn from data. They identify patterns and make
Speaker 2: predictions without being explicitly programmed. It's about learning from experience,
Speaker 2: observing adapting.
Speaker 1: Okay, learning from data, not rules precisely.
Speaker 2: And then at the very core you have deep learning DL.
Speaker 2: That's an even more specialized subset of mL. Deep learning
Speaker 2: uses these multi layered structures you've probably heard of them,
Speaker 2: deep neural networks. They solve highly complex problems. They're powering
Speaker 2: a lot of the state of the art stuff we
Speaker 2: see today, like language translation or facial recognition.
Speaker 1: So what this hierarchy really means for us, for you listening,
Speaker 1: is that we're witnessing this incredible evolution. It's fueled by
Speaker 1: well more computing power and just vast amounts of data
Speaker 1: being available now, and AI applications are becoming more powerful,
Speaker 1: more accessible and really applicable across almost every industry.
Speaker 2: And when these systems learn, they generally fall into three
Speaker 2: main approaches, three ways of learning. The first is supervised learning.
Speaker 2: This relies on labeled data. So imagine you have a
Speaker 2: data set where every example has an answer already attached.
Speaker 2: That's your labeled.
Speaker 1: Data, right, like inputs and the correct outputs exactly.
Speaker 2: So. One common use for supervised learning is classification. Here
Speaker 2: the model predicts a category or class. For instance, the
Speaker 2: guy talks about classifying financial transactions. Is this fraudulent or legitimate?
Speaker 2: Based on features like amount, time of day, that sort.
Speaker 1: Of thing, okay, putting things into buckets?
Speaker 2: Yeah? And the other key type is regression. The goal
Speaker 2: here is to predict a continuous numerical value. This could
Speaker 2: be forecasting sales figures for the next quarter maybe, or
Speaker 2: predicting the obstable price for.
Speaker 1: A product, got it, predicting a number.
Speaker 2: Then there's unsupervised learning. This works with unlabeled data, so
Speaker 2: no answer is provided beforehand. Here, this system tries to
Speaker 2: find hidden patterns or structures on its own.
Speaker 1: Ah, okay, so finding patterns we didn't know.
Speaker 2: We're there exactly a great example of this is clustering
Speaker 2: you group similar data points together. Think about segmenting your
Speaker 2: customer base based on their purchasing behavior. You know, to
Speaker 2: understand different.
Speaker 1: Market segments, right, finding natural groupings.
Speaker 2: And finally, we have reinforcement learning. This is where a
Speaker 2: system learns by interacting with an environment. It gets rewards
Speaker 2: for good decisions and well penalties for poor ones. It's
Speaker 2: a bit like how we learn through trial and error.
Speaker 2: The guide mentions an example like an automated call center
Speaker 2: agent learning the best path to resolve customer queries by
Speaker 2: getting rewarded for good recommendations.
Speaker 1: Interesting learning by doing essentially So, this next point seems
Speaker 1: crucial because it's about how we actually use these The
Speaker 1: approach you choose, supervised, unsupervised, or reinforcement, it totally depends
Speaker 1: on your data and the problem you're trying to.
Speaker 2: Solve, right, absolutely, it's fundamental. Do you have clearly labeled examples?
Speaker 2: Supervised is likely your path? Are you looking for hidden
Speaker 2: groups in just raw data? Unsupervised? Is it about learning
Speaker 2: through interaction and feedback? Reinforcement The data and the goal
Speaker 2: dictate the method.
Speaker 1: Makes sense. Now, building effective mL models isn't just about
Speaker 1: picking one of those algorithms. It's a structured process. The
Speaker 1: guide highlights something called crisp DM, the cross industry's standard
Speaker 1: process for data mining, as a blueprint for this.
Speaker 2: Yeah, chris DM is really widely used. It provides a clear,
Speaker 2: iterative framework with six key phases. It starts with business understanding.
Speaker 2: This is all about clearly defining your project objectives, your
Speaker 2: success criteria, potential risks. It sounds obvious, but honestly, this
Speaker 2: is where many projects can go wrong if the problem
Speaker 2: isn't nailed down.
Speaker 1: Precisely right, knowing what you're actually trying to achieve.
Speaker 2: Then data understanding. This involves collecting, describing, exploring, checking the
Speaker 2: quality of your raw data. Data scientists need to be
Speaker 2: well super skeptical here, look for every nuance. Then comes
Speaker 2: data preparation and this is often the most time consuming phase. Really.
Speaker 2: It involves selecting, cleaning, transforming, formatting the data for your chosen.
Speaker 1: Algorithm, Okay, getting the data ready.
Speaker 2: Following that is modeling. Here you select the appropriate algorithm,
Speaker 2: design your tests, approach and train the model. You need
Speaker 2: to distinguish between parameters, which are learned from the data itself,
Speaker 2: and hyper parameters, which are like knobs you turn to
Speaker 2: control the learning process. The fifth phase is evaluation. You
Speaker 2: review the model's performance against those initial business success criteria you.
Speaker 1: Defined and if it's not good enough.
Speaker 2: That's the key. mL is iterative. It's a scientific process.
Speaker 2: If your model isn't cutting it, you loop back, maybe
Speaker 2: you tune those hyper parameters, maybe you need more data,
Speaker 2: maybe you even need to rethink the business problem itself. Finally, deployment,
Speaker 2: getting your model into reduction. This involves creating pipelines for
Speaker 2: continuous training and inference and setting up monitoring to catch
Speaker 2: model drift. All drift, Yeah, that's what a model's performance
Speaker 2: degrades over time because the real world data or patterns change,
Speaker 2: So you need to monitor and potentially retrain. And you know,
Speaker 2: if we connect this back to the AWS certification, the
Speaker 2: four domains covered in the exam data engineering, exploratory, data analysis, modeling,
Speaker 2: and mL OPS, right, they really map quite directly to
Speaker 2: these CRISP DM stages. It's complete life cycle.
Speaker 1: Okay, that framework makes a lot of sense. Now, you
Speaker 1: mentioned data preparation is often the most time consuming part.
Speaker 1: The guide really stresses this too. It's the absolute foundation
Speaker 1: for any good model. Get the data wrong, and well
Speaker 1: nothing else.
Speaker 2: Matters much absolutely garbage in, garbage out. As they say,
Speaker 2: A critical first step is understanding your feature types, the
Speaker 2: kind of beta you have. So you've got numerical data.
Speaker 2: This could be discrete like countable items, number of clicks maybe,
Speaker 2: or continuous measurements with potentially infinite values like temperature or.
Speaker 1: Price, numbers, screen or continuous.
Speaker 2: Got it. Then you have categorical data. This describes qualities
Speaker 2: or labels. It can be nominal labels without any inherent order,
Speaker 2: like colors or types of products, or ordinal labels that
Speaker 2: do have a meaningful order like low, medium, high, or
Speaker 2: education levels.
Speaker 1: Okay, categories with or without an order.
Speaker 2: Right, and categorical data, especially nominal, usually can't be fed
Speaker 2: directly into most algorithms. It needs transforming into numbers. For example,
Speaker 2: for that nominal data without order, like countries, we often
Speaker 2: use one hot encoding. This creates a new binary column
Speaker 2: a zero or one for each category. It avoids accidentally
Speaker 2: implying that, say, country three is somehow greater than country two.
Speaker 1: Ah avoids creating a false order exactly, whereas for ordinal
Speaker 1: data like those education levels, ordinal encoding preserves that inherent sequence.
Speaker 2: Now the crucial rule here in this trips people up
Speaker 2: sometimes is that any encoder you create must be fitted
Speaker 2: only on your training data. Then you use that same
Speaker 2: fitted encoder to transform your teches data and any new
Speaker 2: production data. You never refit on test data that introduces bias.
Speaker 1: Okay, fit on train, transform on tests.
Speaker 2: Got it now. For numerical features, you often need to
Speaker 2: adjust their scale. Data normalization, for instance, might scale data
Speaker 2: to arrange between zero and one. This is really vital
Speaker 2: for algorithms that are sensitive to the magnitude of numbers,
Speaker 2: like neural networks or caneurous.
Speaker 1: Neighbors, so they don't overweight big numbers precisely.
Speaker 2: Alternatively, data standardization transforms data to have a mean of
Speaker 2: zero and a standard deviation of one. This is fantastic
Speaker 2: for identifying outliers, for example, and for features that are
Speaker 2: skewed think income distributions often bunched up at one end.
Speaker 2: Logarithmic and power transformations like the box Cox method can
Speaker 2: make them more symmetrical, more like a Bell curve, and
Speaker 2: that often significantly improves the performance of many algorithms like
Speaker 2: linear regression.
Speaker 1: Wow, lots of ways to wrangle the data. What about
Speaker 1: problems like missing values?
Speaker 2: Yeah, that's a common one. First, you have to try
Speaker 2: and understand why they're missing. Is it ran them or
Speaker 2: is there a pattern. Options range from just listwise deletion
Speaker 2: discarding rows or columns with missing data, but be careful
Speaker 2: you might lose valuable information, or imputation where you replace
Speaker 2: missing values. Simple imputation might use the mean or the median,
Speaker 2: which is less sensitive to outliers, or the mode for
Speaker 2: categorical data, but you can get more sophisticated even using
Speaker 2: other mL models to predict what the missing values should be.
Speaker 1: Okay, and outliers those weird data points.
Speaker 2: So another common hurdle. Outliers are data points significantly different
Speaker 2: from the rest. They can dramatically skew your model's understanding,
Speaker 2: like pulling a regression line way off course. Tools like
Speaker 2: z scores or visualizing with box plots help detect them.
Speaker 2: Once found, you might remove them or maybe just flag
Speaker 2: them so your model knows they're unusual.
Speaker 1: Makes sense, And what if the data is like really unbalanced.
Speaker 1: You mentioned fraud detection earlier.
Speaker 2: Right, Unbalanced data sets very common. Say only one percent
Speaker 2: of your transactions are actually fraudulent. Your model might just
Speaker 2: learn to always predict not fraud, because that's accurate ninety
Speaker 2: nine percent of the time, but it misses the important cases.
Speaker 2: So to address this, you can tune your algorithm, maybe
Speaker 2: tell to pay more attention to the rare class using
Speaker 2: something like a class weight hyperparameter. Or you can resample
Speaker 2: your data. Either undersample the majority class just use fewer
Speaker 2: examples of not fraud, or oversample the minority class. A
Speaker 2: popular technique for oversampling is SMO and a synthetic minority
Speaker 2: over sampling technique. It intelligently creates new synthetic examples of
Speaker 2: the rare class to help balance things.
Speaker 1: Out smot okay, creating fake but plausible examples kind.
Speaker 2: Of yeah, based on the characteristics of the existing minority examples,
Speaker 2: and finally preparing text data for mL or natural language
Speaker 2: processing NLP. This has evolved a lot. Older methods like
Speaker 2: bag of Words BOW just count how often words appear simple,
Speaker 2: but loses context. More advanced techniques like word embedding, used
Speaker 2: in models like word two, VEK or glove represent words
Speaker 2: as dense numerical vectors. What's fascinating here is these vectors
Speaker 2: capture semantic meaning. Words with similar meanings end up closer
Speaker 2: together in this multi dimensional space, so.
Speaker 1: The model understands relationships between words in.
Speaker 2: A mathematical sense. Yes, it captures context and meaning much
Speaker 2: better than just counting.
Speaker 1: That's a really thorough look at data prep. It's clear
Speaker 1: its critical and well often complex. But all this meticulously
Speaker 1: prepared information needs a robust place to live. You need
Speaker 1: to store it somewhere, and on AWS. That journey often
Speaker 1: begins with S three, Right, our digital warehouse, where do
Speaker 1: we store all this data? For mL?
Speaker 2: You're absolutely right. The storage choice is fundamental. Amazon S
Speaker 2: three Simple Storage Service is very often the starting point
Speaker 2: and the core its object storage, known for its incredible durability,
Speaker 2: designed for eleven nine's durability, which is just astronomical protection
Speaker 2: against data loss. It's highly scalable. You store objects your
Speaker 2: files basically within these things called buckets, which are specific
Speaker 2: to an AWS region, and S three offers different storage classes.
Speaker 2: This lets you optimize costs based on how frequently you
Speaker 2: need to access the data. Data you access rarely can
Speaker 2: go into cheaper, colder storage. Plus, it has robust access
Speaker 2: control and encryption options to keep everything secure. OK.
Speaker 1: S three for scalable, durable object storage, what about more
Speaker 1: structured data like traditional databases?
Speaker 2: For that, Amazon Relational Database Service RDS is the managed service.
Speaker 2: It supports popular engines like Mycycle, Postgress, Goal, Oracle, etc.
Speaker 2: A key feature for reliability is multi easy deployments. This
Speaker 2: automatically creates a synchronous standby copy of your database in
Speaker 2: a different availability zone, so if one AZ has an issue,
Speaker 2: it fails over automatically.
Speaker 1: Great for high availability, so it keeps running even if
Speaker 1: there's an outage in one place exactly.
Speaker 2: And for scaling read performance, especially for applications that do
Speaker 2: a lot of reading, you can use read replicas. These
Speaker 2: are asynchronously replicated copies of your main database. You can
Speaker 2: point your read heavy traffic to them. You can even
Speaker 2: place them in different regions for global reach. This directly
Speaker 2: impacts your RPO recovery point objective how much data you
Speaker 2: might lose an RTO recovery time objective how fast you recover.
Speaker 2: Multi asy and read replicas help you achieve low RPO
Speaker 2: and RTO.
Speaker 1: Makes sense availability and read scaling.
Speaker 2: HM and beyond S three and rds AWS has specialized
Speaker 2: stores too. Amazon Redshift is a data warehouse optimized for
Speaker 2: analyzing massive data sets using SQL and Amazon DynamoDB is
Speaker 2: a fully managed no SQL database the key value in
Speaker 2: document data where you need super fast, flexible access at
Speaker 2: really any scale.
Speaker 1: Okay, so a whole range of options. The key takeaway
Speaker 1: here seems to be it's not just about storing data,
Speaker 1: it's about choosing the right storage for the right kind
Speaker 1: of data, getting that optimal balance of availability, performance, security,
Speaker 1: and cost for your specific mL use.
Speaker 2: Case, precisely matching the tool to the job.
Speaker 1: So once our data is carefully stored and prepped, we
Speaker 1: often need to process it further, maybe transform it in
Speaker 1: bulk or analyze streams of it. The guide walks us
Speaker 1: through a WUS services for both batch processing and real
Speaker 1: time stuff.
Speaker 2: Yeah, large scale data transformation and movement like etlxtract transform
Speaker 2: load AWS. Glue is a really powerful, fully managed service.
Speaker 2: It's a secret Sauce is the data catalog. You can
Speaker 2: automatically crawl your data sources, figure out the schema, detect changes,
Speaker 2: and make it all queriable. Then glues ETL jobs, which
Speaker 2: usually run on a patchy spark, do the heavy lifting
Speaker 2: of the actual data transformation, maybe copying and cleaning data
Speaker 2: from S three into redshift for example.
Speaker 1: So Glue handles the whole ETL pipeline.
Speaker 2: Pretty much in a serverlest way. Now, if you just
Speaker 2: want a query data that's already sitting in S three
Speaker 2: without moving or transforming it first, Amazon Athena is amazing
Speaker 2: for this. It's serverless, interactive use standard SQL to query
Speaker 2: data directly in S three across various formats CSV, json, parquet, ORC,
Speaker 2: no infrastructure to manage. Is incredibly fast for ad hoc
Speaker 2: analysis or quick.
Speaker 1: Exploration schema onread right, you define the structure as.
Speaker 2: You query it exactly. Now, for processing real time streaming data,
Speaker 2: we turn to Amazon Kinesis Visais data streams can capture
Speaker 2: and store huge amounts of data per second from loads
Speaker 2: of sources website clicks, IoT sensors, financial transactions. You can
Speaker 2: then build applications to process this stream in real time.
Speaker 2: Then there's Kinesis Data fire Hose. This is a fully
Speaker 2: managed service that takes that streaming data and automatically loads
Speaker 2: it into destinations like S three, redshift or analytics services.
Speaker 2: It can even transform the data on the fly using
Speaker 2: AWS Lambda before delivering it.
Speaker 1: So fire Hose is more about getting the stream into
Speaker 1: storage or other services easily.
Speaker 2: Yeah, simplifies the delivery part. And what about getting data
Speaker 2: from your own data centers into AWS. AWS Storage Gateway
Speaker 2: connects your on premises software appliances to cloud storage using
Speaker 2: standard file or block protocols. For really massive data transfers
Speaker 2: where the Internet is too slow, you have the AWS
Speaker 2: snow family. These are physical devices like Snowball Edge which
Speaker 2: is like a ruggedized suitcase computer, or even Snowmobile, a
Speaker 2: whole shipping container. You load data onto them locally, ship
Speaker 2: them to AWS and they upload it securely, much faster
Speaker 2: for petabytes a truck.
Speaker 1: Full of data literally pretty much.
Speaker 2: And AWS Data Sinc. Is great for ongoing online data
Speaker 2: transfer between your on premises storage and AWS services like
Speaker 2: S three or EFS. Finally, for those really big computation
Speaker 2: heavy batch jobs, things that might take hours or days
Speaker 2: or need massive resources beyond what Lander offers, Aws Bachil
Speaker 2: lets you schedule and run these efficiently. It manages the
Speaker 2: job queues, provisions the right compute resources like EC two instances,
Speaker 2: and scales automatically.
Speaker 1: Okay. This really covers the spectrum, from analyzing static data
Speaker 1: with Athena and glue to handling real time streams with kinesis,
Speaker 1: and even moving massive data sets physically. AWS seems to
Speaker 1: have a tool for almost every data processing need.
Speaker 2: It's a very comprehensive set of services.
Speaker 1: Now, before we dive headfirst into coding raw algorithms, the
Speaker 1: guide makes a point of highlighting aws's out of the
Speaker 1: box AI services. These seem designed to make advanced mL
Speaker 1: accessible even if you're not a deep learning expert. Right,
Speaker 1: no model building recques exactly.
Speaker 2: These are pre trained managed services. You use them via
Speaker 2: simple API calls. They bring sophisticated AI capabilities directly into
Speaker 2: your applications with minimal fuss. For example, Amazon Recognition provides
Speaker 2: powerful visual analysis. It can detect objects, people, faces, texts
Speaker 2: and images, and videos, even sentiment analysis on faces. Amazon
Speaker 2: Polly converts text into remarkably lifelike speech, loads of voices languages,
Speaker 2: great for accessibility or creating voice interfaces.
Speaker 1: Polly Speaks and Recognition Ce's right.
Speaker 2: And Amazon transcribed as the opposite of poly. It converts
Speaker 2: speech into text, excellent for transcribing audio, video calls, generating captions.
Speaker 2: It supports custom vocabularies too, for better accuracy and specific domains.
Speaker 2: Amazon comprehend digs into unstructured text I think customer reviews, emails,
Speaker 2: social media feeds. It pulls out insights like sentiment positive, negative, neutral,
Speaker 2: key phrases, entities, even topics.
Speaker 1: So comprehend understands text.
Speaker 2: Amazon Translate provides high quality, real time language translation between languages.
Speaker 2: Amazon TExtract is really interesting. It goes beyond basic ocr
Speaker 2: optical character recognition. It understands the structure of documents, so
Speaker 2: it can extract data not just as raw text, but
Speaker 2: specifically from forms and tables, preserving their layout and relationships.
Speaker 2: Super useful for document.
Speaker 1: Processing while understanding forms and tables not just text.
Speaker 2: Yeah, and finally, Amazon Lex this is the engine that
Speaker 2: powers Amazon Alexa. It lets you build sophisticated conversational interfaces chatbots,
Speaker 2: voice spots using natural language understanding NLU and automatic speech recognition. ASR.
Speaker 2: You define the user's goals, intense the information needed, slots
Speaker 2: and sample phrases, utterances, and LEX handles the complex conversation flow.
Speaker 1: Okay, that's an incredible menu of ready to use AI
Speaker 1: really lowers the barrier to entry, But it begs the
Speaker 1: question for you listening, how do you decide when should
Speaker 1: you use these powerful pre built tools versus actually diving
Speaker 1: in and building a custom mL model from scratch.
Speaker 2: That's a really important strategic decision and the answer often
Speaker 2: comes down to specificity and control. For common, well defined
Speaker 2: tasks like general translation, sentiment analysis, standard object recognition, and images,
Speaker 2: these managed services are often the fastest, easiest, and most
Speaker 2: cost effective path. They're pre trained by AWS on massive
Speaker 2: data sets, so you benefit from that expertise with minimal
Speaker 2: development effort. You don't need deep mL knowledge to integrate
Speaker 2: them via APIs.
Speaker 1: So use them for the standard stuff.
Speaker 2: Generally, yes, However, if your problem is highly specialized, maybe
Speaker 2: involves unique data types not covered by the services, or
Speaker 2: if you need fine grained control over the model architecture
Speaker 2: or the training process or the specific performance trade offs.
Speaker 2: That's when building a custom model, probably using a platform
Speaker 2: like Amazon sage Maker becomes the better choice. It gives
Speaker 2: you full flexibility, but requires more mL expertise and effort.
Speaker 1: Got it. Use managed services for speed and common tasks,
Speaker 1: build custom for unique needs and control. Okay, now let's
Speaker 1: go deeper into the custom model, building into the heart
Speaker 1: of mL, the algorithms themselves. The guide outlines aws's built
Speaker 1: in algorithms available in sage Maker, which are often optimized
Speaker 1: for the AWS environment. But first, maybe a quick word
Speaker 1: on ensemble models. The guide mentions these are pretty powerful.
Speaker 2: Yeah. Ensemble methods are a really important concept. The idea
Speaker 2: is to combine multiple individual mL models to get better
Speaker 2: predictive performance than any single model could achieve on its own.
Speaker 2: Two main types are bagging, think bootstrap aggregating. Like in
Speaker 2: a random forest algorithm, You train many models, usually decision trees,
Speaker 2: independently on different random samples of your data, and then
Speaker 2: you average their predictions for regression or take a majority
Speaker 2: vote for classification. It helps reduce variants.
Speaker 1: So wisdom of the crowd applied to models.
Speaker 2: Kinda yeah. The other main type is boosting. Here models
Speaker 2: are trained sequentially. Each new model focuses on correcting the
Speaker 2: errors made by the previous ones. It builds a strong
Speaker 2: predictor Iteratively, algorithms like ATTA boost or the very popular
Speaker 2: XG boost uses approach. Boosting often leads to very high accuracy,
Speaker 2: but you need to be careful about overfitting.
Speaker 1: Okay, bagging is parallel boosting a sequential makes sense? So
Speaker 1: what are some of the key built in algorithms sage
Speaker 1: Maker offers, for say, supervised learning.
Speaker 2: Right for supervised tasks with labeled data, sage Maker has
Speaker 2: several optimized algorithms. The linear learner algorithm is a good
Speaker 2: starting point. It's versatile handling with regression, predicting numbers and
Speaker 2: classification predicted categories. It's great for understanding linear relationships and
Speaker 2: includes options like L one and L two regularization to
Speaker 2: prevent overfitting and even perform some automatic feature selection. Then
Speaker 2: there's XG boost. As we mentioned, it's a gradi at
Speaker 2: boosting algorithm, incredibly popular and often wins data science competitions,
Speaker 2: especially with structured tabular data. Sage Maker has a highly
Speaker 2: optimized version.
Speaker 1: XG boost seems like a go to for many problems.
Speaker 2: It often is for unsupervised learning finding patterns in unlabeled data.
Speaker 2: K means is a classic clustering algorithm. You tell how
Speaker 2: many clusters you want to find, and it groups your
Speaker 2: data points based on similarity typically distance. Great for customer
Speaker 2: segmentation or finding archetypes. Random cut Forest RCF is specifically
Speaker 2: designed for anomaly detection. It builds a collection of random
Speaker 2: trees and identifies data points that are easily isolated. These
Speaker 2: are likely anomalies, good for fraud or outlier.
Speaker 1: Detection, finding the odd ones out exactly.
Speaker 2: And principal component analysis PCA. This is a fundamental technique
Speaker 2: for dimensionality reduction. If you have lots and lots of features,
Speaker 2: PCA can transform them into a smaller set of uncorrelated
Speaker 2: principal components that capture most of the original information. This
Speaker 2: helps simplify models, reduce noise, sometimes improve performance, and even
Speaker 2: makes high dimensional data easier to visualize.
Speaker 1: Reducing complexity while keeping the important info.
Speaker 2: That's the goal. Sage Maker also has specialized algorithms like
Speaker 2: deeper for time series forecasting using sophisticated recurrent neural networks.
Speaker 2: For text analysis, there's blazing text, which is optimized for
Speaker 2: both text classification and generating work word embeddings like word
Speaker 2: twvec very quickly on large data sets, And of course
Speaker 2: a suite of algorithms for image processing image classification that's
Speaker 2: the main object object detection find multiple objects in drawboxes,
Speaker 2: and semantic segmentation classify every pixel in the image.
Speaker 1: Wow, so a really broad set of tools. What about
Speaker 1: data formats? Do these algorithms just take CSV files?
Speaker 2: Many can take text CSSV Yes. For supervised learning, the
Speaker 2: convention is usually the target variable in the first calumn
Speaker 2: no head or row. However, for peak performance and efficiency,
Speaker 2: especially with large data sets, many stage Maker built in
Speaker 2: algorithms prefer an optimized binary format called recordio protobuff. This
Speaker 2: format allows for something called pipe mode where data is
Speaker 2: streamed directly from S three to the training instance without
Speaker 2: needing to download it all first. It saves time and
Speaker 2: disk space. Uh.
Speaker 1: Recordio protobuff for speed and streaming. This is a really
Speaker 1: comprehensive toolkit. It's clear AWS provides these highly optimized tools
Speaker 1: for almost any mL task. It lets you, the user
Speaker 1: focus more on framing the problem and interpreting results, rather
Speaker 1: than getting totally bogged down in the low level infrastructure
Speaker 1: or algorithm implementation.
Speaker 2: That's definitely the aim of a managed service like sage Maker.
Speaker 1: Okay, so we've built these potentially incredible models using these algorithms,
Speaker 1: but how do we actually know if they're any good?
Speaker 1: How do we evaluate them? It's not just about hitting
Speaker 1: run and hoping for the best.
Speaker 2: Right, absolutely not. Evaluation is critical. It's not just about
Speaker 2: getting a single accuracy number. It's about understanding how your
Speaker 2: model performs, its strengths, its weaknesses, and whether it actually
Speaker 2: meets the business need. Evaluation metrics are crucial for documenting performance,
Speaker 2: comparing different models or different versions of the same model,
Speaker 2: tracking them over time and production, and importantly for detecting
Speaker 2: that model drift we talked about earlier, when performance degrades
Speaker 2: metrics tell you it's time to retrain or investigate.
Speaker 1: So it's about ongoing quality control too.
Speaker 2: Definitely for classification models, the ones predicting categories like fraud
Speaker 2: not fraud, or spam not spam. The confusion matrix is fundamental.
Speaker 2: It's a simple table that breaks down predictions versus actual outcomes.
Speaker 2: You get four key numbers. True positives TP correctly predicted positive,
Speaker 2: said fraud, was fraud. True negative PN correctly predicted negatives
Speaker 2: not fraud, wasn't fraud. False positives FP incorrectly predicted positive,
Speaker 2: said fraud, but wasn't. This is a Type I error
Speaker 2: of false alarm false negatives. FN incorrectly predicted negative, said
Speaker 2: not fraud, but was fraud. This is a type two
Speaker 2: error of missed detection.
Speaker 1: TP tn fp FN. Okay, the four outcomes.
Speaker 2: Right, and from this matrix we derive the most common
Speaker 2: classification metrics accuracy TP plus TN divided by the total
Speaker 2: overall correctness. But careful. It can be really misleading if
Speaker 2: your data set is unbalanced like that ninety nine percent
Speaker 2: not fraud. Example, recall or sensitivity tp TP plus fn.
Speaker 2: This measures how well the model finds all the positive cases.
Speaker 2: High recall is crucial when missing a positive is bad
Speaker 2: reg missing a disease diagnosis. Precision or positive predictive value
Speaker 2: tp TP plus fp this measures how often the model
Speaker 2: is correct when it does predict positive High precision is
Speaker 2: key when false alarms are costly, for example, marking important
Speaker 2: emails as spam.
Speaker 1: Recall finds them all. Precision avoids false alarms. A trade off.
Speaker 2: Often, yes, there's usually a trade off between precision and recall.
Speaker 2: The f one score is the harmonic mean of the two,
Speaker 2: providing a single score that balances both. Useful when both
Speaker 2: precision and recall are important.
Speaker 1: Okay, and what about those curves you see like ROC.
Speaker 2: Right evaluation curves help visualize that trade off across different
Speaker 2: decision thresholds. The Precision Recall PR curve plots precision versus recall.
Speaker 2: It's particularly useful for imbalanced data sets as it focuses
Speaker 2: directly on the performance on the minority class. The ROC
Speaker 2: curve receiver operating characteristic plots the true positive rate, which
Speaker 2: is just recall, against the false positive rate FP FP
Speaker 2: plus TN. It's commonly used for more balanced data sets.
Speaker 2: The area under the curve AUC summarizes the curve into
Speaker 2: a single.
Speaker 1: Number PR curve for imbalance, ROC for balance. Good tip.
Speaker 1: What about regression models? The ones predicting numbers.
Speaker 2: Different metrics there, since we're not dealing with classes. Common
Speaker 2: ones include MAE mean absolute error, the average of the
Speaker 2: absolute differences between predictions and actual values. Simple intuitive units
Speaker 2: MS mean squared error, the average of the squared differences.
Speaker 2: This penalizes larger errors much more heavily than smaller ones.
Speaker 2: RMS root means squared error the square root of MS.
Speaker 2: This brings the metric back into the same units as
Speaker 2: your target variable, making it easier to interpret while still
Speaker 2: penalizing large errors. RMSE is probably the most common regression metric,
Speaker 2: and MAP mean absolute percentage error calculates the error as
Speaker 2: an average percentage of the actual values. Very intuitive for
Speaker 2: things like sales forecasting.
Speaker 1: Okay, MA rmsc AMAPE for regression. That's a lot of metrics.
Speaker 1: If you know, if you're listening and looking at these,
Speaker 1: what's maybe one piece of advice you'd give about picking
Speaker 1: the right metric for your specific project.
Speaker 2: That's a great question. The single most important thing is
Speaker 2: to deeply understand your business goal and the cost of
Speaker 2: different types of errors. Don't just default to accuracy because
Speaker 2: it sounds good. Ask yourself what's worse a false positive
Speaker 2: or a false negative. In medical diagnosis, missing a disease,
Speaker 2: a false negative could be catastrophic, so you'd optimize for recall.
Speaker 2: In filtering spam, marking a crucial email as spam, a
Speaker 2: false positive is highly annoying, so you'd prioritize precision. The
Speaker 2: context dictates the metric, Always tie it back to the
Speaker 2: real world impact.
Speaker 1: Connect the metric to the business impact excellent advice.
Speaker 2: So once we can measure our models effectively using these metrics,
Speaker 2: the next logical step is optimization, specifically hyperparameter tuning. Remember
Speaker 2: those knobs that control the learning process. Finding the best
Speaker 2: combination of those settings for your specific data is crucial.
Speaker 2: The goal isn't just to get a model that performs
Speaker 2: well in the data it was trained on. It's to
Speaker 2: get a model that generalizes well to new unseen data.
Speaker 2: We want to minimize both bias oversimplification and variance overfitting.
Speaker 1: Finding that sweet spot between too simple and too complex.
Speaker 2: Exactly there's several techniques for this hyper parameter search. Grid
Speaker 2: search is the most basic. You define a grid of
Speaker 2: possible values for each hyper parameter, and it literally tests
Speaker 2: every single combination very thorough but can be incredibly slow
Speaker 2: and computationally expensive, especially if you have many hyper parameters
Speaker 2: or wide ranges. A more efficient approach is random search.
Speaker 2: Instead of trying every combination, it randomly samples combinations from
Speaker 2: your defined search space. Surprisingly, it often finds very good
Speaker 2: or even optimal hyper parameters, much faster than grid search.
Speaker 1: Randomly trying things can be faster.
Speaker 2: Often yes, because not all hyper parameters are equally important.
Speaker 2: Random search explores the space more broadly quicker. For even
Speaker 2: more intelligence, there's Bayesian optimization. This method learns from past evaluations.
Speaker 2: It builds a probability model of how hyper parameters relate
Speaker 2: to performance, and uses it to intelligently choose the next
Speaker 2: set of hyper parameters to try. Focusing on promising regions
Speaker 2: of the search space. It can converge on optimal settings
Speaker 2: much much faster, especially for complex models.
Speaker 1: Subaesian learns as it goes.
Speaker 2: Precisely, it's a smarter search.
Speaker 1: The core idea here, then, is that tuning isn't just
Speaker 1: a one shot deal. It's an empirical process. You combine
Speaker 1: these smart search strategies with solid evaluation techniques, often using
Speaker 1: things like cross validation to build robust models, Models that
Speaker 1: don't just memorize the training data, but actually perform well
Speaker 1: out there in the real world.
Speaker 2: That's the name of the game generalization.
Speaker 1: Okay, bringing this all together. Now we've talked about the
Speaker 1: life cycle, data algorithms, evaluation optimization. The guide clearly points
Speaker 1: to Amazon stage Maker as the central workbench, the main
Speaker 1: hub for doing all this on AWS.
Speaker 2: Yes, sage Maker is designed to be that integrated environment
Speaker 2: for the entire mL workflow. It's a fully managed service
Speaker 2: aiming to simplify each step. It provides notebook instances. These
Speaker 2: are basically managed Jupiter notebooks running on EC two instances.
Speaker 2: They're great for data exploration, cleaning, preprocessing, and generally orchestrating
Speaker 2: your mL pipeline. For the heavy listing of training, stage
Speaker 2: Maker provides dedicated, optimized training instances. You choose the instance
Speaker 2: type based on your needs, submit your training code, and
Speaker 2: Stagemaker handles provisioning, execution, and tearing down the resources. And
Speaker 2: once your model is trained, sage Maker offers endpoint instances
Speaker 2: for deploying it and getting real time predictions via a
Speaker 2: simple API call. It handles scaling and availability for you.
Speaker 1: Notebooks for exploring training, instances for building endpoints for predicting.
Speaker 2: That's a good summary, and sage Maker also has managed
Speaker 2: services specifically for hyper parameter tuning jobs. You de find
Speaker 2: your hyper parameters ranges, the metric you want to optimize,
Speaker 2: and SageMaker automatically runs the search using strategies like basing, optimization,
Speaker 2: random search, or grid search, keeping track of the best performing.
Speaker 1: Job automating that tuning process we just discussed.
Speaker 2: Exactly when you choose instance types for these sage Maker components,
Speaker 2: they're all based on EC two instances, often with mL prefixes.
Speaker 2: You need to consider your workload. There are general purpose
Speaker 2: M family, Compute optimized CE family, Memory optimized OUR family,
Speaker 2: and portantly GPU enabled instances like the P and G families.
Speaker 2: GPUs are essential for accelerating deep learning training, which involves
Speaker 2: massive matrix calculations. The choice really depends on your data size,
Speaker 2: algorithm complexity, budget, and how fast you need.
Speaker 1: Results matching the hardware to the mL task. What about security?
Speaker 1: Keeping notebooks and data private.
Speaker 2: Security is built in you can launch sage Maker components
Speaker 2: like notebook instances or training jobs within your own private
Speaker 2: VPC virtual private cloud. This gives you fine grain control
Speaker 2: over network access. You can restrict internet access, connect securely
Speaker 2: to your on premises data sources, use security groups and
Speaker 2: network acls. SageMaker also supports network isolation for training and
Speaker 2: inference containers, preventing them from making unauthorized outbound network calls.
Speaker 1: So you can lock it down pretty tightly.
Speaker 2: Absolutely and while sage Maker provides that integrated environment, you
Speaker 2: can also orchestrate mL workflows using other AWS services, sometimes
Speaker 2: in combination. AWS lamb to functions, ervalless Event driven compute
Speaker 2: functions are great for automating parts of the pipeline. For example,
Speaker 2: an S three upload event could trigger a lamb to
Speaker 2: function to do some initial data preprocessing or validation. For
Speaker 2: more complex multi step workflows, AWS step functions is fantastic.
Speaker 2: It lets you define your workflow as a visual state machine.
Speaker 2: You can sequence and coordinate calls to Lambda functions, glue jobs, SageMaker,
Speaker 2: training jobs, manual approval steps, pretty much any AWS service.
Speaker 2: It's great for managing long running distributed processes with built
Speaker 2: in error handling.
Speaker 1: And retries step functions for orchestrating the whole flow.
Speaker 2: Yeah, and if we connect this back to that bigger picture,
Speaker 2: sage Maker, often combined with services like Glue, Lambda and
Speaker 2: step functions, really aims to provide a managed, scalable, and
Speaker 2: secure environment for that entire Cristium life cycle. We started
Speaker 2: with from understanding the business need and preparing data in
Speaker 2: notebooks all the way through training, tuning, deploying, and monitoring
Speaker 2: models in production. It's designed to be end to end.
Speaker 1: And there you have it. Wow, what an insightful deep
Speaker 1: dive that was. We started with the Very Foundation's AIMLDL.
Speaker 1: We explored that critical CRISPM life cycle. We understood the
Speaker 1: frankly immense importance of data preparation and the variety of
Speaker 1: AWS storage options. Then we delved into the specific AWSAI
Speaker 1: application services, those ready made tools, and also the powerful
Speaker 1: built in algorithms within sage Maker. And finally we saw
Speaker 1: how crucial evaluation and optimization are and how sage Maker
Speaker 1: and other services help operationalize it. All You've just gained,
Speaker 1: I think, a really valuable shortcut to being well informed
Speaker 1: about the whole landscape of machine learning on AWS. Extracting
Speaker 1: hopefully incredible value from what's normally a pretty dense technical guide.
Speaker 2: And maybe this raises an important final question for you,
Speaker 2: the listener, to consider. Given the power you've just heard
Speaker 2: about in these integrated AWS services, from intelligent data processing
Speaker 2: and diverse storage to that comprehensive suite of AI and
Speaker 2: mL tools, how might you reimagine a current data analysis task,
Speaker 2: or maybe an automation challenge in your own work? How
Speaker 2: could you potentially transform it into an intelligent, scalable mL
Speaker 2: solution using some of these capabilities.
Speaker 1: That's a great thought to leave everyone with. How can
Speaker 1: you apply this? Thanks for diving deep with us today.
Speaker 1: Until next time, keep exploring, keep learning, and stay curious.