Data accessibility and availability is a persistent sticking point within organizations that want to generate value from their data. Step 1. Get data. Step 2. ??? Step 3. Fancy models and profit! Right? **crickets** Yeah, there are some holes in this scheme, so let’s talk about that Step 2 bit for a moment.

Just a few years ago, companies of all sorts were trying to figure out how to collect data about their business. In many industries, this problem has been solved only too well! We now have data coming out of our ears! Petabytes here, terabytes there. But what is in this data, and how do we turn it into big bags of cash?

Ideally, all organizations should be making their data useful in some fashion. However, we frequently spend a bunch of time making sure we have stored our data securely, but don’t give sufficient thought to generating value from it. After all, why are you storing the data if not to use it later? Surely you don’t just like paying your data storage bill for fun!

This is among the problems that I’m suggesting you can solve with a good robust scheduling tool, and some scalable data storage. In my ODSC Europe talk, I’ll be demonstrating this idea with Apache Airflow and AWS data storage, specifically Redshift. However, the core principles are really not specific to the tools you pick; rather, the key is to conceptualize your data and your goals appropriately.

For example, let’s talk about the stakeholders in this problem for a company that wants to apply sophisticated machine learning to predicting a business outcome. Data management is one function, but data science and machine learning is another. Most organizations will have different people, in different roles/departments, handling these two elements.

Using a scheduled pipeline of data transfer allows you to link these two functions, and move your data where it needs to be when it is required. The two departments need only agree on some expectations regarding the way data will be formatted upon transfer, e.g. which fields and types to expect, and then your pipeline can make sure data gets moved without any human intervention. Internalizing the fact that safe, careful data mobility is just as important as the start and endpoints of this process is the real linchpin.

This is an alternative to some bad strategies, such as both teams having to use the exact same data storage solution. By doing this, you eliminate the need to move your data, sure, but you can count on no one getting the best tool for their actual needs. If one team’s primary concern is cheap and high volume storage, with minimal or no data manipulation, and the other team is worried about quick, user-friendly data querying and extraction, with flexibility for writing and augmentation, you’re going to be in a bind choosing one singular storage option. Maybe you can do it! But you don’t have to.

Another example is in machine learning itself. Much of what we do in modeling involves extracting data from someplace, engineering features tailored to the business outcome, and either training a model or making predictions using an existing model. Combining the scheduler + robust data store toolkit I’m proposing makes this really convenient, and as my talk title states, will enable you to make your modeling team much happier.

While your modelers can probably build bespoke python pipelines for feature engineering, and run them with cron, this is a brittle and low-visibility method for managing your business-critical infrastructure. Especially given the fact that model explainability and interpretability is crucial to really getting business value (if you can’t explain it, how are you going to sell the leadership on using it?), the visibility piece is key. A solid scheduler, like Airflow, can watch your back and make sure things are running as expected even when no one is watching. Set up whatever tests and checks are relevant to you, and make use of the readable logs, automatic alerts sent to your on-call system, and retries/error handling a scheduling tool can offer.

Further, it is likely very valuable for debugging and testing for you to maintain the engineered features and be able to revisit them later – this requires writing data back into your data store as part of the job, another point where you need your scheduler and data storage to be working in sync. When you have a scalable data store that can swiftly consume the data that your scheduled jobs are creating, the task of maintaining those features is vastly simplified. Then your data store is accessible to data scientists for future testing, iteration, and model improvements.

These are only a handful of the ways that a scheduler + database infrastructure can be a huge boost for data science team architecture. While much of the glamor and exceptional business value in data science may be found in model building, getting the data where it needs to be at the right time, in a way your team can use, is table stakes for achieving that. If you’re interested in seeing the nuts and bolts of how my team at Journera applies Apache Airflow and AWS Redshift to carrying out this plan, please join me at ODSC Europe in September in my talk, “Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow.”

About the author/speaker: Stephanie Kirmer

Stephanie Kirmer is a Data Science Technical Lead at Journera, an early-stage startup that helps companies in the travel industry use data efficiently and securely to create better travel experiences for customers. Previously, she worked as a Senior Data Scientist at Uptake, where she developed tools for analyzing diesel engine fuel efficiency, and constructed predictive models for diagnosing and preventing mechanical failure. Before joining Uptake, she worked on data science for social policy research at the University of Chicago and taught sociology and health policy at DePaul University.

Everyone practicing in the field of data science is faced with the same dilemma: how to stay on top of projects and stay current in a field that seems to change rapidly every six months. Getting out of the office can help you focus on building new skills, building your network, and offer a welcome respite from the office.

Need help convincing your manager that it’s in their best interest to send you to ODSC? Here are 10 reasons why the ODSC Europe 2020 Virtual Conference is a compelling investment of your time.

[Related article: Why Attend ODSC Europe as a Group]

Reason # 10 – Content

No other conference offers both the breadth and depth of an ODSC event. The proof is in the numbers. We are the number-one leading data science conference in terms of sessions with over 250 hours of content by 140 presenters, 24 half-day or full-day training sessions, 30 workshops, and dozens of general sessions.

Reason # 9 – Hands-on Applied Training

As an applied data science conference, we focus on practical data science that can be utilized immediately. Our expert speakers and instructors are drawn from across industries and know what it takes to make machine learning and deep learning work. Over 50% of our content is hands-on training in either full-day, half-day, or 2-hour sessions. Thus, you can return to the job ready to put your newfound skills to work.

Reason # 8 – Best of the Best

Our instructors are some of the leading experts in the world. Not only are they the best and brightest, but they also work at some of the top companies at the forefront of the AI revolution. You’ll learn from speakers like Diego Galar, PhD, Professor of Condition Monitoring at the Luleå University of Technology, Dr. Colin Gillespie, Senior Lecturer at Newcastle University, Ian Horrocks, PhD, Co-founder of Oxford Semantic Technologies, and many more to come.

Reason # 7 – Demo Hall

Companies need to constantly stay abreast of developments in the market. Much of data science is moving to the cloud and advanced machine learning workflow is a must-have.  Teams and companies need to make purchasing and build vs buy decisions. The AI Expo hall at the ODSC Europe 2020 is where you can learn about the latest products and features from leading providers like Microsoft, DataRobot, and many more. Hear directly from the providers and attend our demo theater sessions for more in-depth presentations. Return to work armed with insights to make build vs buy and purchasing decisions.

Reason # 6 – The Latest Trends and Topics

NLP pre-trained models like BERT, ERNIE, and ELMo and APIs like Hugging Face Transformer were some of the breakout topics of 2019 and going into 2020. We have over 20 sessions on NLP and numerous sessions on other topics, including machine learning workflow, federated models, explainable AI, recommendation systems, graph networks, computer vision, self-supervised and semi-supervised learning, synthetic data, DeepRacer, and reinforcement learning. You’ll return to work well-versed in the latest data science topics to help keep your team ahead of the rest.

Reason # 5 – The Connections

Like many fields, data science is a community. The collaborative effort that goes into building open source platforms and the sharing of ideas is a large part of that. Exchanging insights and connecting with people who have a shared passion is always rewarding. Companies are also embracing this collaborative trend. At ODSC Europe, you’ll make connections through our virtual networking opportunities, such as with our chat options, virtual networking lounge, and more, all in an effort to return to work with a stronger network of potential partners and collaborators that can help you and your company grow.

Reason # 4 – Return on Investment

Attending conferences and training can be a big budget item for any business. ODSC combines the best of both the technical and the community building. You’ll be inspired by our top data science leader keynotes but also get hand-on training you can readily apply at work. The best part is that we offer some of the most competitive pricing and widest range of content of any AI or data science conference. Businesses care about ROI and this is an event that is an excellent investment for you and your company.

Reason # 3 – Value your Time

You and your company know your time is valuable. However, the field is changing so rapidly it’s hard to know what skills to upgrade and what to focus on. Between the demands on your time at work, project deadlines, and a busy outside life, it’s a constant battle to carve out time to upskill. ODSC is an accelerated learning where your time is maximized. You can get hands-on training where you can get current quickly and set up the foundation to continue learning throughout the year. After 2 to 4 intense days of sessions, you’ll return to the office ready to put new skills and knowledge to work immediately.

Reason #2 – It’s On-Demand

There’s a lot of content to learn during an ODSC event in only a few days. With ODSC Europe being virtual, you can access all of the talks on-demand following the event. When you go and tell your manager that there are 250  hours of content to consume, then that’s entirely accurate, and you’ll be able to watch all of it whenever you want.

[Related article: The Second Round of ODSC Europe 2020 Speakers]

Reason # 1 – You Deserve it

Perhaps the most important reason of all. Let’s face it. Being a data scientist, software engineer, or technologist of any stripe is generally a well-paying and very professionally rewarding gig. But it’s also a demanding one. There is no better way to reward yourself, get inspired, and get re-energized.

Hopefully, these reasons are enough to convince your manager to help you get to the ODSC Europe 2020 Virtual Conference this September 17-19! Fill out this form to get a handy downloadable guide with everything you need to seal the deal. Be sure to tell them quickly, as our 50% discount ends on July 3rd! Register here.

This September, the ODSC Europe 2020 Virtual Conference will be focusing on the topics that are trending in data science in 2020 and into 2021. Although you might have heard about some of these focus areas a lot over the last year or so, they are constantly evolving and pushing the boundaries of the data science field.

[Related article: How to Convince Your Boss to Attend ODSC Europe 2020]

ML/DL

Experts in the Machine Learning and Deep Learning Track will discuss the latest advances, trends, and models in these rapidly expanding fields. Creators and top practitioners will share their knowledge and expertise in talks, tutorials, and workshops on the latest models and trends.

Confirmed Speakers Include

• Daria Stepanova, PhD | Research Scientist│Bosch Center for AI
• Dat Tran│Head of AI│Axel Springer SE | Training a Machine to See What’s Beautiful (esp. for Hotel Photos)

NLP

Explore the rapidly advancing field of NLP with the core practitioners and open-source contributors. Learn about the latest advancements and trends, including pre-trained models, with use-cases focusing on deep learning, speech-to-text, and semantic search.

Confirmed Speakers Include

• Joanneke Meijer│Data Science Manager│Amsterdam Data Collective│Sustainable Retail Through Open Source, Scraping and NLP
• Debanjana Banerjee│Data Scientist │Walmart Labs | CRESST: Complete Rare Event Specification using Stochastic Treatment

Research Frontiers

Data science is advancing at a tremendous pace, with new research and models announced every month. To help data scientists keep abreast of the latest advances, this track brings the most relevant information to you. World-class academics, researchers, and professionals summarize the latest research across focus areas and help identify what’s important.

Confirmed Speakers Include

• Dr. Zeynep Akata│Professor Of Computer Science│University of Tübingen| Learning and Explaining with Limited Labels
• Dr. Lora Aroyo│Research Scientist│Google

Kickstarter

The Data Science Kick-start track offers many introductory-level talks, tutorials, and workshops to get you started building in-demand skills. Our expert speakers and presenters offer world-class instruction, and give you invaluable insights to help you learn what matters most. We cover the most important tools, topics, and techniques in use to ensure you hit the ground running.

Confirmed Speakers Include

• Matt Brems│Global Lead Data Science Instructor│General Assembly | Natural Language Processing Fundamentals in Python
• Gabriel Bianconi│Founder│Scalar Research | Introduction To Face Processing With Computer Vision

ML for Programmers

With the rapid growth of AI comes an increasing demand for machine learning engineers and programmers. An ML engineer works at the intersection of software engineering and data science. Learn how to build on your programming skills to engineer the next generation of artificial intelligence-enabled software. On the ML for Programmers track, you’ll explore everything from data wrangling, modeling, and workflow, to essential deep learning and machine learning frameworks.

Confirmed Speakers Include

• Dr. Kirk Borne│Principal Data Scientist│Booz Allen Hamilton | Atypical Applications of Typical Machine Learning Algorithms
• Jared Lander│Chief Data Scientist│Lander Analytics | Machine Learning in R Part I: Penalized Regression and Boosted Trees

Data Visualization

The data visualization track brings together the world’s most creative minds that are changing the way we visualize, understand, and interact with data. Join a community of designers, data scientists, and developers to learn the art of storytelling, information communication, and data visualization using the latest open source tools and techniques.

Confirmed Speakers Include

• Max Humber│Lead Instructor│General Assembly-Animating Data: From matplotlib plots to GIFs
• Dr. Katharina Glass│Manager Data Scientist│Aurubis AG-Dare to Start Simple

R for Data Science

Learn the models, packages, and tools for R, one of the most popular languages for statistical modeling and a data science powerhouse platform with dozens of packages and interfaces for machine learning, deep learning, and data visualization.

MLOps & Data Engineering

As data science extends its reach across an enterprise, the need for better management, workflow, production and deployment practices to meet the challenges of deploying and monitoring models in production, managing data science workflows and teams, and understanding ROI increases. Learn how to effectively incorporate data science practice into the wider business process with use cases, best practices, and stories from the field.

Confirmed Speakers Include

• Stephanie Kirmer│Data Science Technical Lead│Journera | Making Happy Modelers: Build and Maintain Your Data Warehouse with AWS Redshift and Airflow
• Ian Horrocks, PhD│Professor│University of Oxford | Which is the tallest building in Europe? — Representing and Reasoning About Knowledge

Mini-Bootcamp

Grow your skillset with a unique learning path that will start in July and take you through September. With many opportunities to learn from some of the best and brightest minds in data science and AI, you’ll leave the Mini-Bootcamp armed with new tools and knowledge that can be immediately applied to your work. The Mini-Bootcamp learning path will include

• Pre-Bootcamp On-Demand Training covering the fundamentals of programming with Python, statistics, and SQL
• Pre-Bootcamp Live Training with the AI+ instructor of your choice
• A Virtual Hackathon that gives you an opportunity to put your new skills to the test
• 3 or 4-day Bootcamp at the conference where you’ll have access to
  • 6 sessions covering data science fundamentals
  • 50+ training sessions, workshops, and talks
  • The Virtual AI Expo and Demo Talks
  • The Virtual Career Lab and Expo

[Related article: The Second Round of ODSC Europe 2020 Speakers]

Don’t forget to join us this September to learn how advancements in these focus areas are pushing the boundaries of what data scientists can do. Plus you’ll save 50% on your pass when you register before Friday, July 3rd.