Speaker 1: You've seen the headlines, You've definitely heard the buzz about AI,
Speaker 1: and maybe you've wondered how those really cool prototypes actually
Speaker 1: become real world solutions.
Speaker 2: Right, the ones that deliver actual tangible value.
Speaker 1: Exactly what does it really take to move AI from
Speaker 1: just a concept, like a neat idea to something that
Speaker 1: actually drives business results. That's what we're getting into today.
Speaker 2: Yeah, our mission here is to take you on a
Speaker 2: deep dive into well productionizing AI. We're using insights from
Speaker 2: Barry Walsh's guide on delivering AI B to B solutions,
Speaker 2: specifically with Cloud and Python.
Speaker 1: And we want to cut through the jargon right.
Speaker 2: Absolutely, focus on the essential nuggets, you know, the core ecosystem,
Speaker 2: the practical steps, the best practices for building AI apps
Speaker 2: that actually work and succeed out there.
Speaker 1: Because this isn't just about the technology itself, is it.
Speaker 1: It's more about understanding that whole journey, how AI moves
Speaker 1: from you know, pure hype to actual concrete return on
Speaker 1: investment ROI.
Speaker 2: That's the key.
Speaker 1: We want to show you why employers are so focused
Speaker 1: on these high value AI solutions and how understanding this
Speaker 1: process can give you some real aha moments about the
Speaker 1: future of tech and business and maybe you're rolling it too.
Speaker 2: So okay, let's start maybe by demystifying the AI ecosystem
Speaker 2: a bit and what we even mean by productionizing.
Speaker 1: AI good idea.
Speaker 2: For years, AI felt like it was always just around
Speaker 2: the corner. But now, well, now it's actually delivering, and
Speaker 2: things like the pandemic definitely accelerated that.
Speaker 1: Shaw that with chatbots, right, suddenly they were everywhere for
Speaker 1: customer service.
Speaker 2: Exactly, or deep learning aiding, healthcare diagnostics, even computer vision
Speaker 2: for remember the social distancing stuff. Oh yeah, these weren't
Speaker 2: just lab experiments. They became pretty critical tools fast.
Speaker 1: And the really big shift, Walsh points this out, is
Speaker 1: moving beyond those cool but maybe isolated, standalone AI projects.
Speaker 1: Companies are now chasing this bigger enterprise AI vision and
Speaker 1: the market for that it's projected at what three hundred
Speaker 1: and forty one billion dollars huge.
Speaker 2: It's massive. Yeah, So it's not just one off solutions anymore.
Speaker 2: It's got to be a broader, integrated strategy.
Speaker 1: Which demands something different. Right, this industrialization of.
Speaker 2: AI exactly, that's the term. It means focusing on reusability, scalability, safety,
Speaker 2: and this is key building these things in right from.
Speaker 1: The design face, not just an afterthought.
Speaker 2: Definitely not an afterthought. That way, AI becomes a reliable
Speaker 2: business asset, not just you know, a science.
Speaker 1: Project that makes complete sense. But okay, if it's such
Speaker 1: a clear benefit, what holds companies back? What are the
Speaker 1: hurdles they face trying to get this enterprise AI thing going?
Speaker 3: Well?
Speaker 2: You know, even with all the potential, a lot of
Speaker 2: companies do struggle. Sometimes it's just a lack of awareness
Speaker 2: of what AI can really.
Speaker 1: Do, or they're stuck with old tools.
Speaker 2: That too, legacy tools, maybe a general resistance to innovation.
Speaker 2: And of course you always have ethical concerns or worries
Speaker 2: about jobs understandable, but ultimately, the best way to look
Speaker 2: at it, the forward thinking view is augmented intelligence AI
Speaker 2: designed to solve specific business problems, and often there's still
Speaker 2: a human in the.
Speaker 1: Loop right guiding it, checking it exactly.
Speaker 2: It's about augmenting what humans can do, not necessarily replacing
Speaker 2: them entirely.
Speaker 1: That's a really important distinction. Now, Okay, core concepts sometimes
Speaker 1: terms get jumbled. What we're mostly using in business today,
Speaker 1: that's narrow AI.
Speaker 2: That's right, machines designed for one single specific task like
Speaker 2: Google Translate. It's amazing at translation, but you can't ask
Speaker 2: it about the weather, right.
Speaker 1: It doesn't have general smarts, the sci fi version the
Speaker 1: thinking machine that's artificial general intelligence AGI still mostly theory, still.
Speaker 2: Very much theory. And within narrow AI, the key techniques
Speaker 2: we use are machine learning mL and deep learning DL.
Speaker 1: And deep learning is a type of machine learning.
Speaker 2: Yeah, basically a more advanced subset. It's specifically built for
Speaker 2: tackling those really complex big data problems, often using structures
Speaker 2: we call neural networks.
Speaker 1: Okay, so if mL and DL are the techniques, how
Speaker 1: does data science fit in? Where does that sit?
Speaker 2: Good question? Data science is kind of the big umbrella.
Speaker 2: Think of it, covering everything the modeling, statistics, programming, plus
Speaker 2: crucially knowing the business domain, all aimed at extracting real
Speaker 2: insights and value from data. It's the whole toolkit you need, really.
Speaker 1: And honestly, none of this would scale what it without.
Speaker 1: The cloud. Cloud computing is just fundamental for.
Speaker 2: AI today, absolutely essential. It's the primary enabler.
Speaker 1: And when we talk cloud, everyone knows the big three Aws, Microsoft, Azure,
Speaker 1: Google Cloud Platform GCP.
Speaker 2: Those are the main players for sure, though you also
Speaker 2: have others like IBM Cloud Heroku doing significant work too.
Speaker 1: The key things the cloud provides for AI are basically
Speaker 1: storage and compute power.
Speaker 2: Right, that's it, storage and compute, And it's worth noting
Speaker 2: deep learning projects they need a lot of both, huge overhead.
Speaker 1: Sometimes, but machine learning is maybe less demanding.
Speaker 2: Off and yeah, mL projects can frequently run with fewer resources.
Speaker 2: What's interesting though, a trend we're seeing is companies worried
Speaker 2: about vendor.
Speaker 1: Lock, being stuck with one provider.
Speaker 2: Exactly, so they're moving towards multi cloud or hybrid setups.
Speaker 2: Gives them more flexibility strategically, and.
Speaker 1: How do they package these AI applications up to run
Speaker 1: reliably everywhere? That's a containerization, isn't it like Docker spot
Speaker 1: on containerization.
Speaker 2: Docker's the big name there. Has pretty much become the
Speaker 2: standard way to productionize AI apps. Why is that, Well,
Speaker 2: think of containers like those standard shipping containers. They bundle
Speaker 2: everything the app needs, code, libraries, settings. It makes them lightweight, portable,
Speaker 2: and they run in isolated environments.
Speaker 1: So consistent deployment no matter the underlying.
Speaker 2: System precisely really important for getting things working reliably in production.
Speaker 1: Okay, so we have the concepts, the cloud foundation. Now
Speaker 1: let's dig into the operational side, the blueprint. Barry Walsh
Speaker 1: really hammers this home data strategy is paramount. He cites
Speaker 1: the numbers something like eighty nine percent of businesses struggle
Speaker 1: with data management.
Speaker 2: Yeah, it's a striking figure, isn't it. And his point
Speaker 2: is without a solid data strategy, your analytics, your AI initiatives,
Speaker 2: they're likely doomed from the start.
Speaker 1: That's a sobering thought, it is.
Speaker 2: And to tackle that exact challenge, we have this methodology
Speaker 2: called data ops.
Speaker 1: Okay, data ops, what's that about?
Speaker 2: Basically merges ideas from DevOps, agile methods and lean manufacturing.
Speaker 1: Principles and data obviously, right.
Speaker 2: The core goal is to streamline your data pipelines, really
Speaker 2: boost data quality and reliability, shorten that innovation cycle time,
Speaker 2: cut down.
Speaker 1: Production error, and improve collaboration.
Speaker 2: Yeah, often through things like self service tools. It fosters
Speaker 2: this culture of continuous improvement, encouraging experimentation, sort of lab
Speaker 2: based innovation with data.
Speaker 1: Okay, So data ops handles the data flow and quality.
Speaker 1: Then you mentioneds mlops.
Speaker 2: Yes. Now, a really critical insight here is that AI
Speaker 2: and machine learning aren't just about code code plus data.
Speaker 1: And the data part is tricky because it changes exactly
Speaker 1: code development.
Speaker 2: We kind of know how to control that, but data
Speaker 2: is dynamic, it evolves on its own, and that's a
Speaker 2: massive challenge. There's this frustrating statistic that less than half
Speaker 2: of mL models actually make it into production.
Speaker 1: Wow, less than half?
Speaker 2: Yeah, not great, it's not. And that's precisely where MLUPS
Speaker 2: comes in. It applies all those proven best practices from
Speaker 2: DevOps and data ops, but specifically to the entire machine
Speaker 2: learning life cycle.
Speaker 1: So from the very beginning.
Speaker 2: Yeah, data prep, model training, all the way through deployment, monitoring,
Speaker 2: and that vital continuous improvement. Looplops is really about bridging
Speaker 2: that gap, that painful gap between developing a model and
Speaker 2: actually using it effectively in the real world.
Speaker 1: And you mentioned agile methods are key in data ops,
Speaker 1: same for mlops.
Speaker 2: Absolutely central Agile fosters that collaboration and adaptability you need.
Speaker 2: AI project teams ideally are really diverse.
Speaker 1: Who's usually on them.
Speaker 2: You'll have business users, data arket, tech solution architects, data engineers,
Speaker 2: data scientists, mL engineers, IT operations folks, everyone.
Speaker 1: Involved, and they work in sprints typically.
Speaker 2: Yeah, development sprints or product sprints, usually two to four
Speaker 2: weeks long. This lets them prioritize and deliver features fixed
Speaker 2: bugs incrementally.
Speaker 1: The benefits seem clear.
Speaker 2: Then more flexibility, definitely faster delivery times too, and crucially,
Speaker 2: you reduce the risk of building something nobody actually wants
Speaker 2: or needs because you're constantly adapting to changing requirements as
Speaker 2: you learn more.
Speaker 1: Collaboration sounds key. You mentioned tools like get and GitHub.
Speaker 1: How do they fit in?
Speaker 2: They're huge version control, especially distributed version control systems or
Speaker 2: dvcs like Geit is just invaluable. It tracks and manages
Speaker 2: changes not just a source code, but also to your
Speaker 2: data sets as.
Speaker 1: They evolve, tracking data changes too. That's interesting for AI.
Speaker 2: It's a massive benefit. Get helps reduce development time, leads
Speaker 2: to much higher success rates for deployments, gives you transparent traceability.
Speaker 2: You know exactly who change what when, and critically the
Speaker 2: ability to roll back.
Speaker 1: To a previous version if something goes wrong.
Speaker 2: Exactly previous states of both code and data. The getub
Speaker 2: ecosystem ties it all together. You've got the command line
Speaker 2: tool get itself, the cloud hosting on GitHub, and a
Speaker 2: desktop app too. Makes team collaboration much.
Speaker 1: Smoother, and then automating the whole release pipeline. That's CICD right.
Speaker 1: Continuous integration continues delivery.
Speaker 2: That's the one. CICD automates the build, test, and deployment stages.
Speaker 2: It lets you push out software updates constantly, reliably with
Speaker 2: minimal manual fuss.
Speaker 1: How does that apply specifically in the AI data ops world.
Speaker 2: Well, the automation extends even further there. It includes orchestrating
Speaker 2: your data pipelines automatically. It can even integrate things like
Speaker 2: data drift detection.
Speaker 1: What's data drift?
Speaker 2: That's when the live data your model season production starts
Speaker 2: looking different from the data it was trained on, which
Speaker 2: can really mess up performance.
Speaker 1: Ah, So CICD can catch.
Speaker 2: That, it can trigger alerts or even automated MITL retraining
Speaker 2: tools like Jenkins makes setting up these CICD environments relatively straightforward.
Speaker 2: Using pipeline scripts. It just makes the whole D end
Speaker 2: process much more efficient and robust.
Speaker 1: Okay, let's switch gears slightly and talk about the data itself,
Speaker 1: because we hear about the data deluge all the time.
Speaker 1: What was that prediction? One hundred and seventy five zetabytes
Speaker 1: by twenty twenty five.
Speaker 2: Something staggering like that. Yeah, a zetabyte is a trilly gigabytes.
Speaker 2: It's almost impossible to comprehend.
Speaker 1: But the real challenge, the insight for businesses isn't just
Speaker 1: the volume, is it. It's the messiness.
Speaker 2: Absolutely, raw data almost never comes ready to use. Companies
Speaker 2: often seriously underestimate the sheer effort needed to turn that
Speaker 2: raw stuff into clean, valuable data.
Speaker 1: And that's a critical step.
Speaker 2: It can absolutely make or break an AI project right
Speaker 2: at the start. Many initiatives stumble right there because they
Speaker 2: underestimate the cleaning and prep work.
Speaker 1: So how do you manage that influx? IBM has this
Speaker 1: concept the AI Ladder.
Speaker 2: Yeah, it outlines a strategic sequence collect, organize, analyze, and
Speaker 2: then infuse AI. A key part of it is unifying
Speaker 2: your data, often across multiple clouds, maybe using a data lake.
Speaker 1: Unification is key for getting that complete picture totally.
Speaker 2: And this leads us to data pipelines. These are the
Speaker 2: automated workflows that move.
Speaker 1: Data around like plumbing for data kind.
Speaker 2: Of Yeah, automated series of actions extract or ingest data
Speaker 2: from sources, transform it so it's usable, and then load
Speaker 2: it into a data store for analysis. It's that classic
Speaker 2: extract transform load loop ETL.
Speaker 1: Let's break that ETL down.
Speaker 3: Extraction that's just pulling data from all sorts of places
Speaker 3: text files, databases, websites, APIs, and increasingly using efficient formats
Speaker 3: like Parquet or AVRO. Then transformation that's the prep work
Speaker 3: getting the data ready for whatever system comes next. Involves formatting,
Speaker 3: filtering out bad data, encoding things, numerically, scaling values, normalizing,
Speaker 3: maybe splitting data sets lots of steps.
Speaker 1: Potentially, and finally loading, just.
Speaker 2: Putting that cleaned transformed data into its final destination, the
Speaker 2: data store.
Speaker 1: Okay eto. But then there's another term, data wrangling. How
Speaker 1: is that different from transformation?
Speaker 2: Good question. Data wrangling is maybe more active, more iterative.
Speaker 2: It happens after you've acquired the data, but before you
Speaker 2: start building models. Unlike say, exploratory data analysis, where you're
Speaker 2: just looking, wrangling actively changes the data to make it
Speaker 2: suitable for mL or.
Speaker 1: DL, So it's really shaping the data for the model exactly.
Speaker 2: It includes things like deciding how to handle missing values,
Speaker 2: do you drop the rows, fill them with the average, interpolate.
Speaker 2: It also means dealing with outliers, encoding, categorical data, scaling,
Speaker 2: numerical features. It's all about optimizing the data for the algorithm.
Speaker 1: Right, makes sense. So once the data is wrangled, you
Speaker 1: need somewhere to put it. You mentioned data lakes.
Speaker 2: Yep, data lakes are popular. They're basically a single large
Speaker 2: repository for all kinds of data, raw, structured, unstructured, semi structured.
Speaker 2: Great for handling that variety of velocity, volume, veracity the
Speaker 2: vis of big data.
Speaker 1: But there's a catch.
Speaker 2: There is Without really good cataloging and governance, a data
Speaker 2: lake can easily turn into a data swamp, just a
Speaker 2: mess of unusable data.
Speaker 1: Okay, so governance is crucial. What about data warehouses?
Speaker 2: Data warehouses are traditionally more structured, clean organized data, mostly structured,
Speaker 2: often serving as the single source of truth for reporting
Speaker 2: and analytics, though modern ones are getting better at handling
Speaker 2: unstructured data too.
Speaker 1: And data marts.
Speaker 2: Those are usually smaller, focused subsets of a data warehouse,
Speaker 2: tailored for a specific department or analytical need.
Speaker 1: And there's a newer concept, lakehouse.
Speaker 2: Yeah. The lakehouse idea tries to blend the best of
Speaker 2: both the flexibility and cheap storage of a data lake,
Speaker 2: combined with the data management and structure features of a
Speaker 2: data warehouse still evolving but.
Speaker 1: Promising, and choosing between ETL and ELT. That's a strategic
Speaker 1: decision too, right Definitely.
Speaker 2: ETL extract transform load is the classic way transform the
Speaker 2: data before loading it. Often good for structured data or
Speaker 2: migrating to the cloud. ELT ELT xtract load transform flips it.
Speaker 2: You'd load the raw data into storage first, then transform it.
Speaker 2: This is really popular for data lakes and exploratory analysis.
Speaker 1: Why more flexibility exactly?
Speaker 2: Data scientists can access the raw data and decide on
Speaker 2: transformations later as they figure out what they need. Much
Speaker 2: more agile for exploration.
Speaker 1: Than the databases themselves. SQL versus no SQL quick rundown sure.
Speaker 2: SQL databases think Microsoft SQL server, my School postgress are relational.
Speaker 2: They use pre defined schemas prey structured. Great for complex
Speaker 2: analytical queries. What we call ol app using powerful joints
Speaker 2: and no SQL, no SEQL like Mungo, DBE, Cassandra AWS,
Speaker 2: DynamoDB are non relational, often schemeless or flexible schema designed
Speaker 2: for massive scale, high speed and handling frequent changes like
Speaker 2: in web apps. Often used for OLTP transactional.
Speaker 1: Data and what if you need insights right now from
Speaker 1: data that's constantly flowing.
Speaker 2: Ah, then you need stream processing and analytics. This is
Speaker 2: about querying data streams as they arrive in your real time.
Speaker 2: Crucial when the data's value drops.
Speaker 1: Quickly, like IoT sensor data or stock prices.
Speaker 2: Perfect examples. Tools like Apache storms, Spark, streaming, flink, COFKA, streams, aws, kinesis.
Speaker 2: They're all built for this kind of high speed, continuous
Speaker 2: querying and analysis, getting insights in milliseconds or seconds.
Speaker 1: Okay, we've got the data flowing stored prepped. Let's finally
Speaker 1: dive into the engine machine learning and deep learning. Starting
Speaker 1: with mL. Supervised learning, Yeah.
Speaker 2: Probably the most common type. You train the model on
Speaker 2: data where you already know the right answer the label.
Speaker 1: Like predicting customer churn you have pass data on who
Speaker 1: left exactly.
Speaker 2: That's a classification problem. Or forecasting customer revenue based on passbending.
Speaker 2: That's a regression known inputs, known outputs.
Speaker 1: Then unsupervised learning, this is where it gets interesting.
Speaker 2: You don't have labels. The goal is to find hidden
Speaker 2: patterns or structures in the data itself.
Speaker 1: Like grouping similar customers together.
Speaker 2: Right, that's clustering. Another key technique is dimensionality reduction, like
Speaker 2: PCA principle component analysis. It helps simplify massive data sets
Speaker 2: by finding the most important underlying features, making them easier
Speaker 2: to work with.
Speaker 1: And then there's reinforcement learning. That sounds different.
Speaker 2: Again it is. It's about real time learning. An agent
Speaker 2: learns by trial and error in an environment, getting rewards
Speaker 2: or penalties for its.
Speaker 1: Actions, like training a robot to walk, yeah.
Speaker 2: Or gameplaying AI. Google Search Engine uses IT. Autonomous vehicles
Speaker 2: rely heavily on IT. Robotics. It's powering some really advanced applications.
Speaker 1: Okay, And building on mL, we get to deep learning.
Speaker 1: This is where the really complex stuff happens. Right.
Speaker 2: Pretty much, DL extends mL using these things called artificial
Speaker 2: neural networks A and NS, often with many, many hidden layers.
Speaker 2: They're loosely inspired by the brain structure.
Speaker 1: Why is DL booming now? These ideas aren't brand.
Speaker 2: New, true, the concepts have been around, but it's the
Speaker 2: massive leap in computing power, especially from GPUs, and huge
Speaker 2: amounts of data that have made training these deep networks feasible.
Speaker 2: Think milestones like IBM's Deep Blue beating Caspar.
Speaker 1: Ofv Watson on Jeopardy.
Speaker 2: Image net breakthroughs, Google Deep minds alphag These were all
Speaker 2: powered by or pushed the limits of deep learning and
Speaker 2: the hardware behind it.
Speaker 1: And inside deal. There are different kinds of neural networks.
Speaker 2: Oh yeah, whole zoo of them.
Speaker 1: Yeah.
Speaker 2: For image recognition, the standard is convolutional neural networks CNNs.
Speaker 2: They're brilliant at picking out sparal hierarchies of features in images,
Speaker 2: treating them as multidimensional grids or tensors.
Speaker 1: Tensors right, like complex spreadsheets.
Speaker 2: Sort of, yeah, multidimensional arrays. Then for sequential data time
Speaker 2: series language, you have for current neural networks RNNs and
Speaker 2: a more powerful variant called LSTMs long short term memory models.
Speaker 1: They have memory of past inputs essentially.
Speaker 2: Yes, they maintain a state that captures information from previous
Speaker 2: steps in the sequence, and then you get into networks.
Speaker 2: They can even generate new data like auto encoders or
Speaker 2: variational auto.
Speaker 1: Encoders and the famous JANS generative adversarial networks.
Speaker 2: That's them, the tech behind deep fakes. They learn patterns
Speaker 2: from input data and can create incredibly realistic synthetic images, text,
Speaker 2: even music.
Speaker 1: Wild stuff. What tools do people use to build these?
Speaker 2: Python is king here. The dominant frameworks are TensorFlow, which
Speaker 2: is Google's open source library, and Keras, which is a
Speaker 2: high level API that makes TensorFlow much easier to use.
Speaker 1: And the other big one.
Speaker 2: PyTorch from Facebook. It's another very popular open source framework
Speaker 2: known for its flexibility and dynamic approach often preferred in research.
Speaker 1: Building the model is one thing, but getting it to
Speaker 1: perform well, that's tuning right, sounds complex.
Speaker 2: It's definitely an iterative process, almost an art form. Sometimes
Speaker 2: you need to choose the right activation functions, deciding how
Speaker 2: neurons fire, select appropriate loss functions to measure the model's air,
Speaker 2: and pick good optimization algorithms to adjust the model's internal
Speaker 2: weights to minimize that error.
Speaker 1: And then there are hyper parameters like dials you can
Speaker 1: turn exactly.
Speaker 2: Think of them as settings outside the model that control
Speaker 2: the learning process itself. Things like the learning rate, how
Speaker 2: many examples you process at once, the batch size, and
Speaker 2: really important techniques like regularization dropout.
Speaker 1: What does regularization do?
Speaker 2: It helps prevent the model from overfitting. That's where it
Speaker 2: learns the training data too well, including noise, and then
Speaker 2: fails to generalize to new unseen data. Dropout is a
Speaker 2: common way to fight that. Fine tuning these hyper parameters
Speaker 2: is critical for getting good results.
Speaker 1: You know, thinking back, we've seen so many cool AI
Speaker 1: prototypes over the years, but historically it didn't a lot
Speaker 1: of them just fail to make it into actual use.
Speaker 2: Sadly, yes, that was a common story, often due to
Speaker 2: those operational silos we talked about, or maybe relying too
Speaker 2: much on niche experts, models being too complex and code heavy,
Speaker 2: core integration, lots of reasons.
Speaker 1: But it feels like that's changing now, like there's an
Speaker 1: automation revolution happening.
Speaker 2: I think that's fair to say, and that's where auto
Speaker 2: mL comes in alongside noload no code low code platforms.
Speaker 1: Okay, AUTOMML automating machine learning pretty much.
Speaker 2: It automates big chunks of the mL workflow, data prep,
Speaker 2: picking the right algorithm, tuning those tricky hyper parameters we
Speaker 2: just discussed, even deploying the model takes a lot of
Speaker 2: the manual gruntwork and guesswork out of it. And the
Speaker 2: no LO platform these are crucial for well democratizing AI.
Speaker 2: They provide user interfaces that let people who aren't hardcore
Speaker 2: data scientists build and deploy AI.
Speaker 1: Solutions, so business analysts maybe exactly.
Speaker 2: It enables a shift from older rule based automation to
Speaker 2: more intelligent AI infused cognitive Robotic process automation or CRPA,
Speaker 2: making automation smarter.
Speaker 1: How does auto mL actually work underneath? How does it
Speaker 1: find the best model?
Speaker 2: It uses clever search algorithms. Simple ones exist like random
Speaker 2: search or grid search, but the really powerful ones are
Speaker 2: adaptive like Bayesian.
Speaker 1: Optimization Asian optimization.
Speaker 2: Yeah. It learns from each model it tries based on
Speaker 2: how well previous configurations performed. It intelligently decides which combination
Speaker 2: of algorithm and hyper parameters to try next. It's much
Speaker 2: more efficient than just trying things.
Speaker 1: Randomly, like a smarter trial and error exactly.
Speaker 2: And for coders who want automation, they are great Python
Speaker 2: libraries too, things like PI, Carrot for low code, mL, Autosklern, Autowaika,
Speaker 2: even tPOT which uses genetic programming to evolve entire bipelines.
Speaker 1: Beyond libraries, there are full platforms now right enterprise level stuck.
Speaker 2: Oh yeah, the major cloud providers and others have comprehensive offerings.
Speaker 2: IBM cloud Pack for data is when it aims to
Speaker 2: be a unified platform with a data fabric approach, and
Speaker 2: its AUTOAI feature automates the process and.
Speaker 1: Ranks models and Azure.
Speaker 2: Azure Machine Learning has really strong cloud integration and built
Speaker 2: in automated mL features. Google Cloud vertex AI again a
Speaker 2: unified platform tightly integrated with all the other Google Cloud
Speaker 2: servicesws awsage Maker Autopilot is their main offering for simplifying
Speaker 2: the model building, often paired with sage Maker data Wrangler
Speaker 2: for the data prep side.
Speaker 1: And TensorFlow has its own too.
Speaker 2: Yep, TensorFlow Extended or TFX. It's open source from Google,
Speaker 2: specifically designed for a building scalable production mL pipelines and
Speaker 2: end to end mL alps. These platforms are really making
Speaker 2: productionizing AI much more.
Speaker 1: Achievable, and the impact of the NOLO interfaces on these
Speaker 1: platforms seems massive.
Speaker 2: It really is. They're essential for getting more people involved,
Speaker 2: broader stakeholder engagement, moving AI out of just the data
Speaker 2: science lab and into the hands of people across the business.
Speaker 2: That's huge for adoption.
Speaker 1: Okay, so we've got data models, automation. How do we
Speaker 1: actually go from concept to a working solution? What's the process?
Speaker 2: Well, the advice is generally to follow an agile approach,
Speaker 2: start small, but keep that bigger enterprise AI picture in mind.
Speaker 1: Is there framework.
Speaker 2: Wells suggests a kind of seven step process beig a
Speaker 2: specific well defined problem, develop low fideli solutions quickly, prototypes, iterate,
Speaker 2: maybe try different related problems, collaborate like on Cagle competitions
Speaker 2: to learn and always always make sure the solution delivers
Speaker 2: real measurable value.
Speaker 1: And you need to write infrastructure underneath all this, absolutely
Speaker 1: critical things like APIs, application programming interfaces, and endpoints.
Speaker 2: These are the standard ways you expose your trained mL
Speaker 2: models so other applications or UIs can use it, usually
Speaker 2: as a web service like Arrest API.
Speaker 1: That's how the model gets called.
Speaker 2: To make predictions precisely and to handle big data volumes
Speaker 2: and lots of users hating those APIs, you need serious
Speaker 2: processing power distributed processing clusters.
Speaker 1: This is where GPUs come in.
Speaker 2: Yes, GPUs graphics profits units and now TPUs TensorFlow processing
Speaker 2: units are key because they excel at the parallel computations
Speaker 2: needed for deep learning. And newer things are emerging too,
Speaker 2: like DPUs data processing units and FPGA's Field Programmable.
Speaker 1: Data RAYSE and dealing with massive data sets.
Speaker 2: Techniques like sharding are used. It basically means splitting your
Speaker 2: data horizontally. Across multiple servers, so you can process it
Speaker 2: in parallel and handle huge volumes.
Speaker 1: So the models deployed via an API, how do users
Speaker 1: interact with it through user interfaces?
Speaker 2: UIs right, and Python being the main AI language, has
Speaker 2: great tools for building these UIs quickly like what, Well,
Speaker 2: there's dash which is excellent for building analytical web dashboards.
Speaker 2: Flask is a popular lightweight framework good for simpler web
Speaker 2: apps or API back ends. Django is more of a
Speaker 2: full featured, batteries included framework for bigger projects.
Speaker 1: And I've heard a lot about streamlet recently.
Speaker 2: Yeah, streamlint has become incredibly popular because it makes it
Speaker 2: super easy to turn data scripts into sharable web apps
Speaker 2: with sliders, buttons, charts very quickly, great for prototyping and
Speaker 2: sharing results.
Speaker 1: Okay, let's see AI and action. How is all this
Speaker 1: impacting different industries?
Speaker 2: The impact is becoming really widespread.
Speaker 1: Take telecommunications, what are they doing?
Speaker 2: A big focus is predictive analytics for customer churn, trying
Speaker 2: to figure out who might leave based on factors like price,
Speaker 2: customer service interactions, network coverage issues. They also use real
Speaker 2: time dashboards and sentiment analysis.
Speaker 1: Using NLP on social media.
Speaker 2: Exactly like analyzing Twitter data using the API to gauge
Speaker 2: customer feelings about the b or service quality. What about
Speaker 2: retail again, churn and retention are huge, mining customer data,
Speaker 2: purchase history, website behavior demographics to build models predicting who
Speaker 2: might leave and why. Also lots of predictive analytics for
Speaker 2: online sales trends and customer behavior patterns.
Speaker 1: And banking and finance fraud detection must be a big one.
Speaker 2: Absolutely, that's kind of the flagship AI use case there,
Speaker 2: combining sophisticated machine learning with complex rule engines to spot
Speaker 2: anomalies flag suspicious transactions in real time. It's constantly evolving
Speaker 2: to catch new fraud patterns.
Speaker 1: Supply chain management seems ripe for optimization.
Speaker 2: Definitely, optimization and prescriptive analytics are key using AI to
Speaker 2: better match supply with demand, optimize delivery routes, improve planning
Speaker 2: and scheduling, ultimately aiming to reduce costs and increase efficiency.
Speaker 2: Healthcare and pharma, we're seeing a lot more chatbots and
Speaker 2: intelligent virtual assistance ivas being used for patient support, appointment scheduling,
Speaker 2: even initial mental health screening or support leveraging NLP and
Speaker 2: NLG Natural language generation for human.
Speaker 1: Like interaction and human resources. Can AI help there?
Speaker 2: Yeah. HR analytics is a growing field using AI for
Speaker 2: better recruitment, screening resumes, identifying promising candidates, talent management, improving
Speaker 2: employee experience and employee attrition. Modeling is a big one,
Speaker 2: figuring out why people leave and how to keep your
Speaker 2: best talent.
Speaker 1: It seems like it's touching almost everywhere.
Speaker 2: It really is. Manufacturing with industry four point zero predictive
Speaker 2: maintenance on machines, cybersecurity using mL and DL to detect
Speaker 2: sophisticated attacks, insurance especially telematics, data from cars for risk
Speaker 2: assessment and personalized premiums. Even the legal sector for automating
Speaker 2: research and contract review.
Speaker 1: Wow. And a key trend speeding this all up is
Speaker 1: using pre trained models, right, not building from scratch every time.
Speaker 2: That's a really important point. It's becoming much more common.
Speaker 2: Instead of spending months training your own massive model, you
Speaker 2: can leverage powerful pre trained models offered by cloud providers
Speaker 2: or others through simple.
Speaker 1: APIs like for analyzing text.
Speaker 2: Yeah, things like the Azure Text Analytics API or Google's
Speaker 2: teachable machines, which lets you train models easily. Walsh's guide
Speaker 2: even mentions using models like day L for generating creative images.
Speaker 2: It massively accelerates development.
Speaker 1: So quite a journey we've taken today, a real deep
Speaker 1: dive through this well sometimes complex world of productionizing AI.
Speaker 2: We covered a lot of ground, Yeah, from.
Speaker 1: The basic ecosystem, the data strategies, the crucial roles of
Speaker 1: data ops and emil ups, then into the mechanics of
Speaker 1: machine learning, deep learning, the automation with AutoML and NOLO.
Speaker 2: And finally see how it all comes together in real
Speaker 2: world applications across so many industries.
Speaker 1: And I think what's really clear, what stands out is
Speaker 1: that getting AI solutions delivered successfully it's not just about
Speaker 1: having a clever model, not at all.
Speaker 2: It's the entire end to end process, robust data pipelines,
Speaker 2: smart storage, careful orchestration, testing, continuous monitoring once it's live.
Speaker 2: It's the whole life cycle.
Speaker 1: It really is a journey, isn't it.
Speaker 2: Definitely, And as we've touched on, that journey is full
Speaker 2: of amazing technical opportunities, but also, let's be honest, some
Speaker 2: very practical challenges.
Speaker 1: Yeah. So maybe the deep dive for you the listener
Speaker 1: now is to think beyond just what AI can do
Speaker 1: think about how it's actually brought to life.
Speaker 2: Consider those hidden costs. Maybe cloud services for development aren't free.
Speaker 2: Managing data that constantly changes, that's complex.
Speaker 1: And needing the right mix of skills in your workforce
Speaker 1: to actually pull it all off exactly.
Speaker 2: The real AHA moment might come from understanding those underlying infrastructures,
Speaker 2: the economic realities, the operational hurdles that make AI really happen,
Speaker 2: or sometimes surprisingly make it harder than you'd think.