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data pipeline best practices

A pipeline that can be easily operated and updated is maintainable. Within the scope of the HCA, to ensure that others will be able to use your pipeline, avoid building in assumptions about environments and infrastructures in which it will run. So that's streaming right? Don't miss a single episode of The Banana Data Podcast! Will Nowak: But it's rapidly being developed to get better. How do we operationalize that? That's also a flow of data, but maybe not data science perhaps. I don't want to just predict if someone's going to get cancer, I need to predict it within certain parameters of statistical measures. I think just to clarify why I think maybe Kafka is overrated or streaming use cases are overrated, here if you want it to consume one cookie at a time, there are benefits to having a stream of cookies as opposed to all the cookies done at once. It's called, We are Living In "The Era of Python." See you next time. It takes time.Will Nowak: I would agree. Introduction to GCP and Apache Beam. And even like you reference my objects, like my machine learning models. This needs to be robust over time and therefore how I make it robust? Fair enough. So before we get into all that nitty gritty, I think we should talk about what even is a data science pipeline. Because I think the analogy falls apart at the idea of like, "I shipped out the pipeline to the factory and now the pipes working." And I could see that having some value here, right? So you have SQL database, or you using cloud object store. So maybe with that we can dig into an article I think you want to talk about. But all you really need is a model that you've made in batch before or trained in batch, and then a sort of API end point or something to be able to realtime score new entries as they come in. Will Nowak: Yeah. We recommend using standard file formats and interfaces. You were able to win the deal or it was lost. Will Nowak: Now it's time for, in English please. These systems can be developed in small pieces, and integrated with data, logic, and algorithms to perform complex transformations. And so now we're making everyone's life easier. And then once I have all the input for a million people, I have all the ground truth output for a million people, I can do a batch process. But it's again where my hater hat, I mean I see a lot of Excel being used still for various means and ends. Is the model still working correctly? It provides an operational perspective on how to enhance the sales process. Triveni Gandhi: I mean it's parallel and circular, right? And if you think about the way we procure data for Machine Learning mile training, so often those labels like that source of ground truth, comes in much later. According to Wikipedia "A software license is a legal instrument (usually by way of contract law, with or without printed material) governing the use or redistribution of software.” (see this Wikipedia article for details). And so I want to talk about that, but maybe even stepping up a bit, a little bit more out of the weeds and less about the nitty gritty of how Kafka really works, but just why it works or why we need it. How about this, as like a middle ground? Maintainability. A testable pipeline is one in which isolated sections or the full pipeline can checked for specified characteristics without modifying the pipeline’s code. And so I would argue that that flow is more linear, like a pipeline, like a water pipeline or whatever. This is generally true in many areas of software engineering. The Dataset API allows you to build an asynchronous, highly optimized data pipeline to prevent your GPU from data starvation. And so that's where you see... and I know Airbnb is huge on our R. They have a whole R shop. Note: this section is opinion and is NOT legal advice. Both, which are very much like backend kinds of languages. But one point, and this was not in the article that I'm linking or referencing today, but I've also seen this noted when people are talking about the importance of streaming, it's for decision making. A Data Pipeline, on the other hand, doesn't always end with the loading. That's the concept of taking a pipe that you think is good enough and then putting it into production. The data science pipeline is a collection of connected tasks that aims at delivering an insightful data science product or service to the end-users. Yeah. So it's another interesting distinction I think is being a little bit muddied in this conversation of streaming. You have one, you only need to learn Python if you're trying to become a data scientist. It seems to me for the data science pipeline, you're having one single language to access data, manipulate data, model data and you're saying, kind of deploy data or deploy data science work. Here we describe them and give insight as to why these goals are important. This pipe is stronger, it's more performance. In computational biology, GA4GH is a great source of these standards. Will Nowak: I think we have to agree to disagree on this one, Triveni. Will Nowak: What's wrong with that? Developed in the Data Sciences Platform at the Broad Institute, the toolkit offers a wide variety of tools with a primary focus on variant discovery and genotyping. Data analysis is hard enough without having to worry about the correctness of your underlying data or its future ability to be productionizable. So the concept is, get Triveni's information, wait six months, wait a year, see if Triveni defaulted on her loan, repeat this process for a hundred, thousand, a million people. And then does that change your pipeline or do you spin off a new pipeline? Google Cloud Platform provides a bunch of really useful tools for big data processing. Is this pipeline not only good right now, but can it hold up against the test of time or new data or whatever it might be?" Right? It's really taken off, over the past few years. Where you have data engineers and sort of ETL experts, ETL being extract, transform, load, who are taking data from the very raw, collection part and making sure it gets into a place where data scientists and analysts can pick it up and actually work with it. This is often described with Big O notation when describing algorithms. Thus it is important to engineer software so that the maintenance phase is manageable and does not burden new software development or operations. Do you have different questions to answer? Use it as a "do this" generally and not as an incredibly detailed "how-to". I write tests and I write tests on both my code and my data." Will Nowak: That's all we've got for today in the world of Banana Data. Will Nowak: Yeah, that's a good point. I think everyone's talking about streaming like it's going to save the world, but I think it's missing a key point that data science and AI to this point, it's very much batch oriented still.Triveni Gandhi: Well, yeah and I think that critical difference here is that, streaming with things like Kafka or other tools, is again like you're saying about real-time updates towards a process, which is different real-time scoring of a model, right? I can monitor again for model drift or whatever it might be. So Triveni can you explain Kafka in English please? Will Nowak: Yes. See this doc for more about modularity and its implementation in the Optimus 10X v2 pipeline, currently in development. Because no one pulls out a piece of data or a dataset and magically in one shot creates perfect analytics, right? Code should not change to enable a pipeline to run on a different technical architecture; this change in execution environment should be configurable outside of the pipeline code. There's iteration, you take it back, you find new questions, all of that. One of the benefits of working in data science is the ability to apply the existing tools from software engineering. Again, the use cases there are not going to be the most common things that you're doing in an average or very like standard data science, AI world, right? I agree. The best pipelines should be easy to maintain. Bad data wins every time. Will Nowak: Just to be clear too, we're talking about data science pipelines, going back to what I said previously, we're talking about picking up data that's living at rest. Most big data solutions consist of repeated data processing operations, encapsulated in workflows. So related to that, we wanted to dig in today a little bit to some of the tools that practitioners in the wild are using, kind of to do some of these things. When edges are directed from one node to another node the graph is called directed graph. I just hear so few people talk about the importance of labeled training data. So it's sort of the new version of ETL that's based on streaming. So just like sometimes I like streaming cookies. Best Practices for Building a Machine Learning Pipeline. General. Maybe you're full after six and you don't want anymore. The following broad goals motivate our best practices. I know. I think it's important. It's never done and it's definitely never perfect the first time through. But if you're trying to use automated decision making, through Machine Learning models and deployed APIs, then in this case again, the streaming is less relevant because that model is going to be trained again in a batch basis, not so often. And then that's where you get this entirely different kind of development cycle. So I think that similar example here except for not. And again, I think this is an underrated point, they require some reward function to train a model in real-time. Python used to be, a not very common language, but recently, the data showing that it's the third most used language, right? Everything you need to know about Dataiku. Will Nowak: Yeah. Between streaming versus batch. Pipelines will have greatest impact when they can be leveraged in multiple environments. That's the dream, right? That I know, but whether or not you default on the loan, I don't have that data at the same time I have the inputs to the model. And at the core of data science, one of the tenants is AI and Machine Learning. Triveni Gandhi: Right? Manual steps will bottleneck your entire system and can require unmanageable operations. Right? Right? We should probably put this out into production." The best pipelines should be easily testable. No problem, we get it - read the entire transcript of the episode below. One would want to avoid algorithms or tools that scale poorly, or improve this relationship to be linear (or better). To ensure the reproducibility of your data analysis, there are three dependencies that need to be locked down: analysis code, data sources, and algorithmic randomness. But what I can do, throw sort of like unseen data. The best pipelines should be easy to maintain. What that means is that you have lots of computers running the service, so that even if one server goes down or something happens, you don't lose everything else. The best pipelines should scale to their data. An organization's data changes, but we want to some extent, to glean the benefits from these analysis again and again over time. So all bury one-offs. Will Nowak: Yeah, I think that's a great clarification to make. The responsibilities include collecting, cleaning, exploring, modeling, interpreting the data, and other processes of the launching of the product. Will Nowak: Today's episode is all about tooling and best practices in data science pipelines. We've got links for all the articles we discussed today in the show notes. Maybe at the end of the day you make it a giant batch of cookies. So then Amazon sees that I added in these three items and so that gets added in, to batch data to then rerun over that repeatable pipeline like we talked about. Triveni Gandhi: Kafka is actually an open source technology that was made at LinkedIn originally. Its powerful processing engine and high-performance computing features make it capable of taking on projects of any size. Will Nowak: Yeah. And it's not the author, right? That's where the concept of a data science pipelines comes in: data might change, but the transformations, the analysis, the machine learning model training sessions, and any other processes that are a part of the pipeline remain the same. Now that's something that's happening real-time but Amazon I think, is not training new data on me, at the same time as giving me that recommendation. And I think we should talk a little bit less about streaming. Good clarification. So it's sort of a disservice to, a really excellent tool and frankly a decent language to just say like, "Python is the only thing you're ever going to need." And I guess a really nice example is if, let's say you're making cookies, right? ... cloud native data pipeline with examples from … So when we think about how we store and manage data, a lot of it's happening all at the same time. So we'll talk about some of the tools that people use for that today. Best Practices for Data Science Pipelines February 6, 2020 Scaling AI Lynn Heidmann An organization's data changes over time, but part of scaling data efforts is having the ability to glean the benefits of analysis and models over and over and over, despite changes in data. And so, so often that's not the case, right? Setting up data analytics pipeline: the best practices. That seems good. Right? Science that cannot be reproduced by an external third party is just not science — and this does apply to data science. All right, well, it's been a pleasure Triveni. So, I mean, you may be familiar and I think you are, with the XKCD comic, which is, "There are 10 competing standards, and we must develop one single glorified standard to unite them all. Pipeline has an easy mechanism for timing out any given step of your pipeline. I learned R first too. Okay. And that's sort of what I mean by this chicken or the egg question, right? The best pipelines should scale to their data. Make sure data collection is scalable. An organization's data changes over time, but part of scaling data efforts is having the ability to glean the benefits of analysis and models over and over and over, despite changes in data. So you're talking about, we've got this data that was loaded into a warehouse somehow and then somehow an analysis gets created and deployed into a production system, and that's our pipeline, right? Software is a living document that should be easily read and understood, regardless of who is the reader or author of the code. CRM best practices: analyzing won/lost data. So the first problem when building a data pipeline is that you ... process to follow or on best practices. And I think the testing isn't necessarily different, right? And so the pipeline is both, circular or you're reiterating upon itself. Yeah. People are buying and selling stocks, and it's happening in fractions of seconds. So we haven't actually talked that much about reinforcement learning techniques. Triveni Gandhi: I am an R fan right? That is one way. It's very fault tolerant in that way. Another thing that's great about Kafka, is that it scales horizontally. When the pipe breaks you're like, "Oh my God, we've got to fix this." Triveni Gandhi: But it's rapidly being developed. So you would stir all your dough together, you'd add in your chocolate chips and then you'd bake all the cookies at once. Right. So I get a big CSB file from so-and-so, and it gets uploaded and then we're off to the races. So, and again, issues aren't just going to be from changes in the data. Science is not science if results are not reproducible; the scientific method cannot occur without a repeatable experiment that can be modified. It came from stats. Maybe changing the conversation from just, "Oh, who has the best ROC AUC tool? And being able to update as you go along. Loading... Unsubscribe from Alooma? Enter the data pipeline, software that eliminates many manual steps from the process and enables a smooth, automated flow of data from one station to the next. With any emerging, rapidly changing technology I’m always hesitant about the answer. So that's a great example. Either way, your CRM gives valuable insights into why a certain sale went in a positive or negative direction. Scaling characteristics describe the performance of the pipeline given a certain amount of data. The more technical requirements for installing and running of a pipeline, the longer it will take for a researcher to have a usable running pipeline. Doing a sales postmortem is another. An Observability Pipeline is the connective tissue between all of the data and tools you need to view and analyze data across your infrastructure. But to me they're not immediately evident right away. You ready, Will? Licenses sometimes legally bind you as to how you use tools, and sometimes the terms of the license transfer to the software and data that is produced. Today I want to share it with you all that, a single Lego can support up to 375,000 other Legos before bobbling. Kind of this horizontal scalability or it's distributed in nature. Will Nowak: I would disagree with the circular analogy. Look out for changes in your source data. Portability is discussed in more detail in the Guides section; contact us to use the service. Because R is basically a statistical programming language. It used to be that, "Oh, makes sure you before you go get that data science job, you also know R." That's a huge burden to bear. Essentially Kafka is taking real-time data and writing, tracking and storing it all at once, right? If you're thinking about getting a job or doing a real software engineering work in the wild, it's very much a given that you write a function and you write a class or you write a snippet of code and you simultaneously, if you're doing test driven development, you write tests right then and there to understand, "Okay, if this function does what I think it does, then it will pass this test and it will perform in this way.". I get that. Getting this right can be harder than the implementation. Because frankly, if you're going to do time series, you're going to do it in R. I'm not going to do it in Python. Featured, Scaling AI, It's a more accessible language to start off with. Workplace. Unless you're doing reinforcement learning where you're going to add in a single record and retrain the model or update the parameters, whatever it is. Data pipelines are a generalized form of transferring data from a source system A to a source system B. Is you're seeing it, is that oftentimes I'm a developer, a data science developer who's using the Python programming language to, write some scripts, to access data, manipulate data, build models. And in data science you don't know that your pipeline's broken unless you're actually monitoring it. Dataiku DSS Choose Your Own Adventure Demo. Clarify your concept. Scaling AI, Best Practices in the Pipeline Examples; Best Practices in the; Articles and Presentations. This guide is not meant to be an exhaustive list of all possible Pipeline best practices but instead to provide a number of specific examples useful in tracking down common practices. Data processing pipelines are an essential part of some scientific inquiry and where they are leveraged they should be repeatable to validate and extend scientific discovery. I'm not a software engineer, but I have some friends who are, writing them. It automates the processes involved in extracting, transforming, combining, validating, and loading data for further analysis and visualization. Triveni Gandhi: Sure. I will, however, focus on the streaming version since this is what you might commonly come across in practice. 02/12/2018; 2 minutes to read +3; In this article . 8. And so I actually think that part of the pipeline is monitoring it to say, "Hey, is this still doing what we expect it to do? I know Julia, some Julia fans out there might claim that Julia is rising and I know Scholar's getting a lot of love because Scholar is kind of the default language for Spark use. Then maybe you're collecting back the ground truth and then reupdating your model. I disagree. Because data pipelines can deliver mission-critical data I can see how that breaks the pipeline. And where did machine learning come from? So that testing and monitoring, has to be a part of, it has to be a part of the pipeline and that's why I don't like the idea of, "Oh it's done." I think, and that's a very good point that I think I tried to talk on this podcast as much as possible, about concepts that I think are underrated, in the data science space and I definitely think that's one of them. So do you want to explain streaming versus batch? Featured, GxP in the Pharmaceutical Industry: What It Means for Dataiku and Merck, Chief Architect Personality Types (and How These Personalities Impact the AI Stack), How Pharmaceutical Companies Can Continuously Generate Market Impact With AI. This concept is I agree with you that you do need to iterate data sciences. Triveni Gandhi: Right? This strategy will guarantee that pipelines consuming data from stream layers consumes all messages as they should. Triveni Gandhi: And so I think streaming is overrated because in some ways it's misunderstood, like its actual purpose is misunderstood. The information in the series covers best practices relating to a range of universal considerations, such as pipeline reliability and maintainability, pipeline performance optimization, and developer productivity. Again, disagree. This can restrict the potential for leveraging the pipeline and may require additional work. Former data pipelines made the GPU wait for the CPU to load the data, leading to performance issues. Best Practices for Building a Cloud Data Pipeline Alooma. The majority of the life of code involves maintenance and updates. © 2013 - 2020 Dataiku. This is bad. Sorry, Hadley Wickham. Especially for AI Machine Learning, now you have all these different libraries, packages, the like. People assume that we're doing supervised learning, but so often I don't think people understand where and how that labeled training data is being acquired. The reason I wanted you to explain Kafka to me, Triveni is actually read a brief article on Do: Wrap Your Inputs in a Timeout. Triveni Gandhi: Yeah, so I wanted to talk about this article. And so this author is arguing that it's Python. A directed acyclic graph contains no cycles. Triveni Gandhi: Oh well I think it depends on your use case in your industry, because I see a lot more R being used in places where time series, and healthcare and more advanced statistical needs are, then just pure prediction. This guide is arranged by area, guideline, then listing specific examples. Choosing a data pipeline orchestration technology in Azure. I could see this... Last season we talked about something called federated learning. So yeah, I mean when we think about batch ETL or batch data production, you're really thinking about doing everything all at once. And people are using Python code in production, right? And so reinforcement learning, which may be, we'll say for another in English please soon. What does that even mean?" It's a somewhat laborious process, it's a really important process. I know you're Triveni, I know this is where you're trying to get a loan, this is your credit history. A bit dated, but always good. And so you need to be able to record those transactions equally as fast. Starting from ingestion to visualization, there are courses covering all the major and minor steps, tools and technologies. Where you're saying, "Okay, go out and train the model on the servers of the other places where the data's stored and then send back to me the updated parameters real-time." Is it the only data science tool that you ever need? And so when we think about having an effective pipeline, we also want to think about, "Okay, what are the best tools to have the right pipeline?" You need to develop those labels and at this moment in time, I think for the foreseeable future, it's a very human process. All rights reserved. Modularity is very useful because, as science or technology changes, sections of a tool can be updated, benchmarked, and exchanged as small units, enabling more rapid updates and better adaptation to innovation. I was like, I was raised in the house of R. Triveni Gandhi: I mean, what army. Python is good at doing Machine Learning and maybe data science that's focused on predictions and classifications, but R is best used in cases where you need to be able to understand the statistical underpinnings. And maybe that's the part that's sort of linear. So what do we do? Triveni Gandhi: Right. And so it's an easy way to manage the flow of data in a world where data of movement is really fast, and sometimes getting even faster. But then they get confused with, "Well I need to stream data in and so then I have to have the system." So software developers are always very cognizant and aware of testing. And then soon there are 11 competing standards." Right? Best Practices for Scalable Pipeline Code published on February 1st 2017 by Sam Van Oort Pipeline portability refers to the ability of a pipeline to execute successfully on multiple technical architectures. We then explore best practices and examples to give you a sense of how to apply these goals. That's where Kafka comes in. 10/21/2020; 9 minutes to read; In this article. Ensure that your data input is consistent. It's you only know how much better to make your next pipe or your next pipeline, because you have been paying attention to what the one in production is doing. An orchestrator can schedule jobs, execute workflows, and coordinate dependencies among tasks. Discover the Documentary: Data Science Pioneers. Formulation of a testing checklist allows the developer to clearly define the capabilities of the pipeline and the parameters of its use. Go for it. The delivered end product could be: Triveni Gandhi: There are multiple pipelines in a data science practice, right? The best way to avoid this issue is to create a different Group (HERE Account Group) for every pipeline, thus ensuring that each pipeline uses a unique application ID. And so I think Kafka, again, nothing against Kafka, but sort of the concept of streaming right? Triveni Gandhi: Right? I wanted to talk with you because I too maybe think that Kafka is somewhat overrated. But every so often you strike a part of the pipeline where you say, "Okay, actually this is good. Just this distinction between batch versus streaming, and then when it comes to scoring, real-time scoring versus real-time training. You've reached the ultimate moment of the sale funnel. So when you look back at the history of Python, right? How Machine Learning Helps Levi’s Leverage Its Data to Enhance E-Commerce Experiences. Sometimes I like streaming data, but I think for me, I'm really focused, and in this podcast we talk a lot about data science. Apply over 80 job openings worldwide. That was not a default. We have developed a benchmarking platform, called Unity, to facilitate efforts to develop and test pipelines and pipeline modules. Data Science Engineer. The availability of test data enables validation that the pipeline can produce the desired outcome. And I think sticking with the idea of linear pipes. This article provides guidance for BI creators who are managing their content throughout its lifecycle. 5. Disrupting Pipeline Reviews: 6 Data-Driven Best Practices to Drive Revenue And Boost Sales The sales teams that experience the greatest success in the future will capitalize on advancements in technology, and adopt a data-driven approach that reduces reliance on human judgment. This will eventually require unreasonable amounts of time (and money if running in the cloud) and generally reduce the applicability of the pipeline. Triveni Gandhi: Last season, at the end of each episode, I gave you a fact about bananas. Best Practices for Data Science Pipelines, Dataiku Product, But data scientists, I think because they're so often doing single analysis, kind of in silos aren't thinking about, "Wait, this needs to be robust, to different inputs. As a best practice, you should always plan for timeouts around your inputs. Do you first build out a pipeline? Triveni Gandhi: Right, right. So it's parallel okay or do you want to stick with circular? And honestly I don't even know. Where we explain complex data science topics in plain English. That's why we're talking about the tools to create a clean, efficient, and accurate ELT (extract, load, transform) pipeline so you can focus on making your "good analytics" great—and stop wondering about the validity of your analysis based on poorly modeled, infrequently updated, or just plain missing data. So think about the finance world. So putting it into your organizations development applications, that would be like productionalizing a single pipeline. Portability avoids being tied to specific infrastructure and enables ease of deployment to development environments. Is it breaking on certain use cases that we forgot about?". As mentioned before, a data pipeline or workflow can be best described as a directed acyclic graph (DAG). It's this concept of a linear workflow in your data science practice. Yeah. Yes. It's also going to be as you get more data in and you start analyzing it, you're going to uncover new things. That's fine. In a Data Pipeline, the loading can instead activate new processes and flows by triggering webhooks in other systems. The blog “Best Practices for B2B Sales - Sales Pipeline Data & Process Improvement, focused on using analytics as a basis to identify bottlenecks in the sales process and create a process for continual improvement. Learn Python.". View this pre-recorded webinar to learn more about best practices for creating and implementing an Observability Pipeline. I have clients who are using it in production, but is it the best tool? Yeah, because I'm an analyst who wants that, business analytics, wants that business data to then make a decision for Amazon. Needs to be very deeply clarified and people shouldn't be trying to just do something because everyone else is doing it. This answers the question: As the size of the data for the pipeline increases, how many additional computes are needed to process that data? With Kafka, you're able to use things that are happening as they're actually being produced. Design and initial implementation require vastly shorter amounts of time compared to the typical time period over which the code is operated and updated. Over the long term, it is easier to maintain pipelines that can be run in multiple environments. The best pipelines should be portable. But what we're doing in data science with data science pipelines is more circular, right? And then once they think that pipe is good enough, they swap it back in. These tools let you isolate all the de… Good analytics is no match for bad data. Will Nowak: Thanks for explaining that in English. And it's like, "I can't write a unit test for a machine learning model. Testability requires the existence of appropriate data with which to run the test and a testing checklist that reflects a clear understanding of how the data will be used to evaluate the pipeline. I became an analyst and a data scientist because I first learned R. Will Nowak: It's true. Find below list of references which contains a compilation of best practices. And so when we're thinking about AI and Machine Learning, I do think streaming use cases or streaming cookies are overrated. But batch is where it's all happening. We'll be back with another podcast in two weeks, but in the meantime, subscribe to the Banana Data newsletter, to read these articles and more like them. Amsterdam Articles. Definitely don't think we're at the point where we're ready to think real rigorously about real-time training. However, after 5 years of working with ADF I think its time to start suggesting what I’d expect to see in any good Data Factory, one that is running in production as part of a wider data platform solution. Join the Team! I mean people talk about testing of code. Data Analytics DevOps Machine Learning. The pipeline consolidates the collection of data, transforms it to the right format, and routes it to the right tool. Cool fact. 5 Articles; More In a data science analogy with the automotive industry, the data plays the role of the raw-oil which is not yet ready for combustion. What is the business process that we have in place, that at the end of the day is saying, "Yes, this was a default. But it is also the original sort of statistical programming language. But you don't know that it breaks until it springs a leak. If you have poor scaling characteristics, it may take an exponential amount of time to process more data. That's kind of the gist, I'm in the right space. Triveni Gandhi: Yeah, sure. And I wouldn't recommend that many organizations are relying on Excel and development in Excel, for the use of data science work. This person was low risk.". So yeah, there are alternatives, but to me in general, I think you can have a great open source development community that's trying to build all these diverse features, and it's all housed within one single language. So what do I mean by that? That you want to have real-time updated data, to power your human based decisions. And I think people just kind of assume that the training labels will oftentimes appear magically and so often they won't. And so again, you could think about water flowing through a pipe, we have data flowing through this pipeline. I think lots of times individuals who think about data science or AI or analytics, are viewing it as a single author, developer or data scientist, working on a single dataset, doing a single analysis a single time. I can throw crazy data at it. So that's a very good point, Triveni. And now it's like off into production and we don't have to worry about it. But you can't really build out a pipeline until you know what you're looking for. So therefore I can't train a reinforcement learning model and in general I think I need to resort to batch training in batch scoring. And so I think ours is dying a little bit. I can bake all the cookies and I can score or train all the records. So the idea here being that if you make a purchase on Amazon, and I'm an analyst at Amazon, why should I wait until tomorrow to know that Triveni Gandhi just purchased this item? Which is kind of dramatic sounding, but that's okay. Automation refers to the ability of a pipeline to run, end-to-end, without human intervention. And especially then having to engage the data pipeline people. But with streaming, what you're doing is, instead of stirring all the dough for the entire batch together, you're literally using, one-twelfth of an egg and one-twelfth of the amount of flour and putting it together, to make one cookie and then repeating that process for all times. Will Nowak: Yeah, that's fair. But in sort of the hardware science of it, right? This education can ensure that projects move in the right direction from the start, so teams can avoid expensive rework. In cases where new formats are needed, we recommend working with a standards group like GA4GH if possible. So by reward function, it's simply when a model makes a prediction very much in real-time, we know whether it was right or whether it was wrong. Deployment pipelines best practices. They also cannot be part of an automated system if they in fact are not automated. So I'm a human who's using data to power my decisions. Pipelines cannot scale to large amounts of data, or many runs, if manual steps must be performed within the pipeline. An important update for the HCA community: Major changes are coming soon to the HCA DCP. Triveni Gandhi: I'm sure it's good to have a single sort of point of entry, but I think what happens is that you get this obsession with, "This is the only language that you'll ever need. But there's also a data pipeline that comes before that, right? I don't know, maybe someone much smarter than I can come up with all the benefits are to be had with real-time training. Banks don't need to be real-time streaming and updating their loan prediction analysis. And then the way this is working right? You can make the argument that it has lots of issues or whatever. Will Nowak: That's example is realtime score. After Java script and Java. Right? The Python stats package is not the best. And then in parallel you have someone else who's building on, over here on the side an even better pipe. This person was high risk. Triveni Gandhi: Yeah. Triveni Gandhi: It's been great, Will. Maybe like pipes in parallel would be an analogy I would use. It starts by defining what, where, and how data is collected. It's a real-time scoring and that's what I think a lot of people want. Moreover, manual steps performed by humans will vary, and will promote the production of data that can not be appropriately harmonized. Will Nowak: One of the biggest, baddest, best tools around, right? And maybe you have 12 cooks all making exactly one cookie. And we do it with this concept of a data pipeline where data comes in, that data might change, but the transformations, the analysis, the machine learning model training sessions, these sorts of processes that are a part of the pipeline, they remain the same. So, that's a lot of words. Some of them has already mentioned above. Will Nowak: See. And so people are talking about AI all the time and I think oftentimes when people are talking about Machine Learning and Artificial Intelligence, they are assuming supervised learning or thinking about instances where we have labels on our training data. Triveni Gandhi: All right. So I guess, in conclusion for me about Kafka being overrated, not as a technology, but I think we need to change our discourse a little bit away from streaming, and think about more things like training labels. Triveni Gandhi: The article argues that Python is the best language for AI and data science, right? But this idea of picking up data at rest, building an analysis, essentially building one pipe that you feel good about and then shipping that pipe to a factory where it's put into use. Unexpected inputs can break or confuse your model. A graph consists of a set of vertices or nodes connected by edges. Impact. Science. 1) Data Pipeline Is an Umbrella Term of Which ETL Pipelines Are a Subset An ETL Pipeline ends with loading the data into a database or data warehouse. And what I mean by that is, the spoken language or rather the used language amongst data scientists for this data science pipelining process, it's really trending toward and homing in on Python. Will Nowak: Yeah. And so not as a tool, I think it's good for what it does, but more broadly, as you noted, I think this streaming use case, and this idea that everything's moving to streaming and that streaming will cure all, I think is somewhat overrated. Read the announcement. And it is a real-time distributed, fault tolerant, messaging service, right? Exactly. Are we getting model drift? It focuses on leveraging deployment pipelines as a BI content lifecycle management tool. Where you're doing it all individually. And so I think again, it's again, similar to that sort of AI winter thing too, is if you over over-hyped something, you then oversell it and it becomes less relevant. I would say kind of a novel technique in Machine Learning where we're updating a Machine Learning model in real-time, but crucially reinforcement learning techniques. That's fine. Okay. So the discussion really centered a lot around the scalability of Kafka, which you just touched upon. So a developer forum recently about whether Apache Kafka is overrated. What are the best practices from using Azure Data Factory (ADF)? Triveni Gandhi: And so like, okay I go to a website and I throw something into my Amazon cart and then Amazon pops up like, "Hey you might like these things too." Now in the spirit of a new season, I'm going to be changing it up a little bit and be giving you facts that are bananas. Triveni Gandhi: Okay. Data-integration pipeline platforms move data from a source system to a downstream destination system. So basically just a fancy database in the cloud. Will Nowak: Yeah. We provide a portability service to test whether your pipeline can run in a variety of execution environments, including those used by the HCA and others. Other general software development best practices are also applicable to data pipelines: Environment variables and other parameters should be set in configuration files and other tools that easily allow configuring jobs for run-time needs. The underlying code should be versioned, ideally in a standard version control repository. But once you start looking, you realize I actually need something else. An API can be a good way to do that. I mean there's a difference right? By employing these engineering best practices of making your data analysis reproducible, consistent, and productionizable, data scientists can focus on science, instead of worrying about data management. Will Nowak: So if you think about loan defaults, I could tell you right now all the characteristics of your loan application. My husband is a software engineer, so he'll be like, "Oh, did you write a unit test for whatever?" But I was wondering, first of all, am I even right on my definition of a data science pipeline? A pipeline orchestrator is a tool that helps to automate these workflows. Training teaches the best practices for implementing Big Data pipelines in an optimal manner. Modularity enables small units of code to be independently benchmarked, validated, and exchanged. So in other words, you could build a Lego tower 2.17 miles high, before the bottom Lego breaks. Majid Bahrepour. It loads data from the disk (images or text), applies optimized transformations, creates batches and sends it to the GPU.

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