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Incubator/Accelerator - III | Alive

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About Clarify

Clarify offers a conversation intelligence platform that synthesizes all 3 forms of dialogue (audio, written and video) so that data can be used to improve compliance, productivity, and intelligence applications.

Headquarters Location

501 Pedernales Street Building 1B

Austin, Texas, 78702,

United States


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Latest Clarify News

Scaling AI: The 4 challenges you’ll face

Feb 14, 2021

Organizations of all sizes are embracing AI as a transformative technology to power their digital transformation journeys. Still the challenges around operationalizing AI at scale can still seem insurmountable, with a large number of projects failing. I’ve worked in big data and AI with several organizations and have seen some clear trends on why AI efforts are floundering after an enthusiastic start. These are large established organizations that have done an amazing job of garnering support from their board, C-suite, business stakeholders, and even customers to embark on AI-powered transformation journeys. They have most likely set up some form of a Center of Excellence (CoE) for AI, with key hires both in leadership and technical roles, and have demonstrated the promise of AI, using a few machine learning projects in a limited scale. Then they move to scale a project into production, and they get stuck. The reasons why scaling AI is so challenging seem to fall under four themes: customization, data, talent, and trust. Customization. Solving problems with machine learning (ML) to drive business outcomes requires customization. Most of the models for solving AI problems — ML, deep learning (DL), and natural language processing (NLP), for example — are open sourced or freely available. And these models themselves aren’t the critical factor in solving production-grade problems. Your team will need to customize and train each model to fit your specific problem, data, and domain. Then you need to optimize the model parameters so that they align to your business’s target outcomes/key performance indicators (KPIs). Then, to deploy your models, you need to integrate them into your existing IT architecture. Building AI systems from scratch for every problem and domain thus requires a ton of customization work. Or, if you opt instead to buy off-shelf solutions that are not optimized for your specific needs, you compromise on performance and outcomes. Both paths have their advantages and disadvantages, but it’s important to recognize that AI requires customizations for every project, and every business problem, and that a key part of operationalizing AI is making the customization process as efficient as possible. Data. I’ve seen a number of organizations fail at AI because they underestimated the effort needed to harness, prepare, and access the data to drive these projects at a production scale, and it becomes a rabbit hole. In most such cases, they realize they don’t have standardized data definitions or proper data management, or they struggle with distributed data sources. This kicks off a multi-year transformation journey. While a ton of big data projects exist to handle accessing, organizing, and curating these disparate datasets, these are not sufficient in providing a scalable solution for this problem. Advanced machine learning techniques to work with smaller data sets and noisier data in production are also needed to eliminate this blockage to getting AI pilots to production. Talent. Most organizations where I’ve seen AI projects fail to scale hired ML engineers and data scientists and realized that it was impossible to find someone who has a combination of statistical (ML) skills, domain expertise (both in the business domain and the process domain), and software development experience. So, using classic organizational design, they try to work around it. While you will eventually form a formidable in-house capability if you can retain and develop this highly coveted talent, the need to ramp up a team delays your value realization with AI. This affects your ability to innovate fast enough. I call this the “AI throughput,” the number of AI projects that can be put into production. It takes years for these teams to start producing real results. More successful organizations have brought a holistic ecosystem approach to scaling talent by augmenting internal AI teams with external partners to design a faster pilot-to-production path and improve AI throughput. Trust. People across the world have mixed feelings towards AI and fear it may make their jobs obsolete or irrelevant. So designing AI systems that emphasise the human-machine collaboration is foundational to scaling AI in these organizations. Although full automation through AI may be the solution for many business challenges, the most impactful and high-alpha processes are still the ones humans run. For large-scale adoption of AI across an organization, you need buy-in, support, and integration across multiple business processes, IT systems, and stakeholder workflows. AI implementation into business processes also introduces a variety of risks. One risk is to business performance in cases where the business impact of the AI system is unclear, costing organizations time, resources, and opportunity cost. Another risk is maintaining compliance with internal audit and regulatory requirements, an area that is largely fast evolving. A third type of risk is reputational, with concerns that biased decisions or decisions made by black box algorithms can negatively impact stakeholder experiences. This is a critical obstacle that even the most advanced teams will run into when trying to scale AI across their organizations. Overcoming the challenges I’ve outlined here requires more than just technology and toolsets. It involves a combination of organizational processes, being able to bring different teams along, and collaborating actively with a curated ecosystem of internal and external partners. The $15.7 trillion opportunity with AI is in front of us, but it requires us to come together as an industry to solve these key challenges. I will be exploring these areas in future posts with a focus on sharing some best practices. Ganesh Padmanabhan is VP, Global Business Development & Strategic Partnerships at BeyondMinds . He is also a member of the Cognitive World Think Tank on enterprise AI. VentureBeat VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access: up-to-date information on the subjects of interest to you our newsletters Clarify , which claims to “detect bias in ML models” and to aid in model interpretability SageMaker Pipelines , which help automate and organize the flow of ML pipelines Feature Store , a tool for storing, retrieving, editing, and sharing purpose-built features for ML workflows. Clarify: debiasing AI needs a human element At the AWS re:Invent event in December, Swami Sivasubramanian introduced Clarify as the tool for “bias detection across the end-to-end machine learning workflow” to rapturous applause and whistles. He introduced Nashlie Sephus, Applied Science Manager at AWS ML, who works in bias and fairness. As Sephus makes clear, bias can show up at any stage in the ML workflow: in data collection, data labeling and selection, and when deployed (model drift, for example). The scope for Clarify is vast; it claims to be able to: perform bias analysis during exploratory data analysis conduct bias and explainability analysis after training explain individual inferences for models in production (once the model is deployed) integrate with Model Monitor to provide real-time alerts with respect to bias creeping into your model(s). Clarify does provide a set of useful diagnostics for each of the above in a relatively user-friendly interface and with a convenient API, but the claims above are entirely overblown. The challenge is that algorithmic bias is rarely, if ever, reducible to metrics such as class imbalance and positive predictive value. It is valuable to have a product that provides insights into such metrics, but the truth is that they’re below table stakes. At best, SageMaker claiming that Clarify detects bias across the entire ML workflow is a reflection of the gap between marketing and actual value creation. To be clear, algorithmic bias is one of the great challenges of our age: Stories of at-scale computational bias are so commonplace now that it’s not surprising when Amazon itself “ scraps a secret recruiting tool that showed bias against women. ” To experience first-hand ways in which algorithmic bias can enter ML pipelines, check out the instructional game Survival of the Best Fit . Reducing algorithmic bias and fairness to a set of metrics is not only reductive but dangerous. It doesn’t incorporate the required domain expertise and inclusion of key stakeholders (whether domain experts or members of traditionally marginalized communities) in the deployment of models. It also doesn’t engage in key conversations around what bias and fairness actually are; and, for the most part, they’re not easily reducible to summary statistics. There is also the seminal work of Timnit Gebru , Joy Buolamwini, and many others (such as Gender Shades ), which gives voice to the fact that algorithmic bias is not merely a question of training data and metrics. In Dr. Gebru’s words : “Fairness is not just about data sets, and it’s not just about math. Fairness is about society as well, and as engineers, as scientists, we can’t really shy away from that fact.” To be fair, Clarify’s documentation makes clear that consensus building and collaboration across stakeholders—including end users and communities—is part of building fair models. It also states that customers “should consider fairness and explainability during each stage of the ML lifecycle: problem formation, dataset construction, algorithm selection, model training process, testing process, deployment, and monitoring/feedback. It is important to have the right tools to do this analysis.” Unfortunately, statements like “Clarify provides bias detection across the machine learning workflow” make the solution sound push-button: as if you just pay AWS for Clarify and your models will be unbiased. While Amazon’s Sephus clearly understands and articulates that debiasing will require much more in her presentation, such nuance will be lost on most business executives. The key takeaway is that Clarify provides some useful diagnostics in a convenient interface, but buyer beware! This is by no means a solution to algorithmic bias. Pipelines: right problem but a complex approach SageMaker Pipelines ( video tutorial , press release ). This tool claims to be the “first CI/CD service for machine learning.” It promises to automatically run ML workflows and helps organize training. Machine learning pipelines often require multiple steps (e.g. data extraction, transform, load, cleaning, deduping, training, validation, model upload, etc. ), and Pipelines is an attempt to glue these together and help data scientists run these workloads on AWS. So how well does it do? First, it is code-based and greatly improves on AWS CodePipelines , which were point-and-click based . This is clearly a move in the right direction. Configuration was traditionally a matter of toggling dozens of console configurations on an ever-changing web console, which was slow, frustrating, and highly non-reproducible. Point-and-click is the antithesis of reproducibility. Having your pipelines in code makes it easier to share and edit your pipelines. SageMaker Pipelines is following in a strong tradition of configuring computational resources as code (the best-known examples being Kubernetes or Chef ). Specifying configurations in source-controlled code via a stable API has been where the industry is moving. Second, SageMaker Pipelines are written in Python and have the full power of a dynamic programming language. Most existing general-purpose CI/CD solutions like Github Actions , Circle CI , or Azure Pipelines use static YAML files. This means Pipelines is more powerful. And the choice of Python (instead of another programming language) was smart. It’s the predominant programming language for data science and probably has the most traction (R, the second most popular language, is probably not well suited for systems work and is unfamiliar to most non-data developers). However, the tool’s adoption will not be smooth. The official tutorial requires correctly setting IAM permissions by toggling console configurations and requires users to read two other tutorials on IAM permissions to accomplish this. The terminology appears inconsistent with the actual console (“add inline policy” vs. “attach policy” or “trust policy” vs. “trust relationship”). Such small variations can be very off-putting for those who are not experts in cloud server administration — for example, the target audience for SageMaker Pipelines. Outdated and inconsistent documentation is a tough problem for AWS, given the large number of services AWS offers. The tool also has a pretty steep learning curve. The official tutorial has users download a dataset, split it into training and validation sets, and upload the results to the AWS model registry . Unfortunately, it takes 10 steps and 300 lines of dev-ops code (yes, we counted). That’s not including the actual code for ML training and data prep. The steep learning curve may be a challenge to adoption, especially compared to radically simpler (general purpose) CI/CD solutions like Github Actions. This is not a strictly fair comparison and (as mentioned previously) SageMaker Pipelines is more powerful: It uses a full programming language and can do much more. However, in practice, CI/CD is often used solely to define when a pipeline is run (e.g., on code push or at a regular interval). It then calls a task runner (e.g., gulp or pyinvoke are both much easier to learn; pyinvoke’s tutorial is 19 lines), which brings the full power of a programming language. We could connect to the AWS service through their respective language SDKs, like the widely used boto3. Indeed, one of us used (abused?) Github Actions CI/CD to collect weekly vote-by-mail signup data across dozens of states in the run-up to the 2020 election and build monthly simple language models from the latest Wikipedia dumps . So the question is whether an all-in-one tool like SageMaker Pipelines is worth learning if it can be replicated by stitching together commonly used tools. This is compounded by SageMaker Pipelines being weak on the natural strength of an integrated solution (not having to fight with security permissions amongst different tools). AWS is working on the right problem. But given the steep learning curve, it’s unclear whether SageMaker Pipelines will be enough to convince folks to switch from the simpler existing tools they’re used to using. This tradeoff points to a broader debate: Should companies embrace an all-in-one stack or use best-of-breed products? More on that question shortly. Feature Store: a much-needed feature for the enterprise As Sivasubramanian mentioned in his re:Invent keynote, “features are the foundation of high-quality models.” SageMaker Feature Store provides a repository for creating, sharing, and retrieving machine learning features for training and inference with low latency. This is exciting as it’s one of many key aspects of the ML workflow that has been siloed across a variety of enterprises and verticals for too long, such as in Uber’s ML platform Michelangelo (its feature store is called Michelangelo Palette ). A huge part of the democratization of data science and data tooling will require that such tools be standardized and made more accessible to data professionals. This movement is ongoing: For some compelling examples, see Airbnb’s open-sourcing of Airflow , the data workflow management tool, along with the emergence of ML tracking platforms, such as Weights and Biases , Neptune AI , and Comet ML . Bigger platforms, such as Databricks’ MLFlow, are attempting to capture all aspects of the ML lifecycle. Most large tech companies have their internal feature stores; and organizations that don’t keep feature stores end up with a lot of duplicated work. As Harish Doddi, co-founder and CEO of Datatron said several years ago now on the O’Reilly Data Show Podcast : “When I talk to companies these days, everybody knows that their data scientists are duplicating work because they don’t have a centralized feature store. Everybody I talk to really wants to build or even buy a feature store, depending on what is easiest for them.” To get a sense of the problem space, look no further than the growing set of solutions, several of which are encapsulated in a competitive landscape table on : The SageMaker Feature Store is promising. You have the ability to create feature groups using a relatively Pythonic API and access to your favorite PyData packages (such as Pandas and NumPy), all from the comfort of a Jupyter notebook. After feature creation, it is straightforward to store results in the feature group, and there’s even a max_workers keyword argument that allows you to parallelize the ingestion process easily. You can store your features both offline and in an online store. The latter enables low-latency access to the latest values for a feature. The Feature Store looks good for basic use cases. We could not determine whether it is ready for production use with industrial applications, but anyone in need of these capabilities should check it out if you already use SageMaker or are considering incorporating it into your workflow. Final thoughts Finally, we come to the question of whether or not all-in-one platforms, such as SageMaker, can fulfill all the needs of modern data scientists, who need access to the latest, cutting edge tools. There’s a trade-off between all-in-one platforms and best-of-breed tooling. All-in-one platforms are attractive as they can co-locate solutions to speed up performance. They can also seamlessly integrate otherwise disparate tools (although, as we’ve seen above, they do not always deliver on that promise). Imagine a world where permissions, security, and compatibility are all handled seamlessly by the system without user intervention. Best-of-breed tooling can better solve individual steps of the workflow but will require some work to stitch together. One of us has previously argued that best-of-breed tools are better for data scientists . The jury is still out. The data science arena is exploding with support tools, and figuring out which service (or combination thereof) makes for the most effective data environment will keep the technical community occupied for a long time. Tianhui Michael Li is president at Pragmatic Institute and the founder and president of The Data Incubator , a data science training and placement firm. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw. Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled . Previously, he was a data scientist at DataCamp , and has taught data science topics at Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC, and with organizations such as Data Carpentry. [Full Disclosure: As part of its services, Coiled provisions and manages cloud resources to scale Python code for data scientists, and so does offer something that SageMaker also does as part of its services. But it’s also true that all-one-platforms such as SageMaker and products such as Coiled can be seen as complementary: Coiled has several customers who use SageMaker Studio alongside Coiled. ]  If you’re an experienced data or AI practitioner, consider sharing your expertise with the community via a guest post for VentureBeat. VentureBeat VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access: up-to-date information on the subjects of interest to you our newsletters networking features, and more The data tooling and infrastructure space is growing rapidly, and this trend is showing no signs of slowing down. Behemoth data storage firm Snowflake IPOed late last year and became more valuable than IBM , and Databricks recently raised a $1 billion Series G with a $28 billion post-money valuation, to name two examples. The long tail of the data tools space is becoming increasingly crowded, as evidenced by Matt Turck’s 2020 Data & AI Landscape (just look at the image below). AWS is one of the most prominent players in the space, and SageMaker is its flagship solution for the machine learning development workflow. When AWS announces new SageMaker features, the industry pays attention. Having written two reviews since Sagemaker Studio’s inception, we were interested to see a swathe of new features come across the wire last December and at Swami Sivasubramanian’s Machine Learning Keynote at re:Invent . After spending some time with the new features, we’ve put together a two-part piece on our impressions. This first part covers: Better integration with Formation Stacks , which allows for easier provisioning of resources General ability to use Sagemaker and the platform’s usability Data Wrangler , a GUI-based tool for data preparation and feature engineering Clarify , which claims to “detect bias in ML models” and to aid in model interpretability Sagemaker Pipelines , which help automate and organize the flow of ML pipelines Let’s get started! One-click provisioning makes it easier to get started Overall, we found the experience with SageMaker much smoother than last year . The Sagemaker AutoPilot environment would actually start and provision (it embarrassingly refused to launch last year during re:Invent). There overall experience felt much improved, and the tutorials and documentation are better integrated with the platform. One of the environment’s best features is Cloud Formation Stacks , which have been around since 2011 but seem to have been better integrated into SageMaker. It’s a significant pain point in computing to get hardware and infrastructure provisioned safely — getting S3 buckets, databases, EC2 instances all up and talking to each other securely. This often meant hours of tinkering with IAM permissions just to get a “Hello World” server going. Cloud Formation simplifies that by pre-defining infrastructure configuration “stacks” into YAML files (think Kubernetes Object YAML but for AWS infrastructure), which can be fired up with one click. An AWS spokesperson told us the integration was part of a move “to make SageMaker widely accessible for the most sophisticated ML engineers and data scientists as well as those who are just getting started.”  Even better, many of the AWS tutorials now feature buttons to launch stacks with just one click: (The buttons are reminiscent of a late ’90s One-Click Shopping button and that resemblance may be subliminal marketing. Both distill immensely complex infrastructure, whether e-commerce or cloud, into a single consumer-friendly button that drives sales.) Sagemaker has improved but usability is still lacking, hindering adoption Given the interest in deep learning, we wanted to try out deep learning on AWS. These models are on the leading edge of machine learning but are notoriously computationally expensive to train, requiring GPUs , which can be quite spendy. We decided to test out these newfound capabilities by running examples from FastAI ’s popular deep learning book to see how easy it is to get started. Fortunately, the Deep Learning models come with convenient launch buttons , so you can get up and running pretty smoothly. The AWS instances were very powerful (for a fairly computationally intensive NLP example their ml.p3.2xlarge ran about 20X faster than the free tier Quadro P5000 available on Gradient ), and for only $3.825 an hour . Nonetheless, the tools were not without their hiccups. On AWS, most of the GPU instances are not automatically available; instead, users must request a quota limit increase. Requesting a limit increase appears to require human approval and usually takes a day, killing momentum. Also, the launch stacks sometimes don’t line up with the tutorial types: e.g., the entity resolution tutorial launches with a CPU instance type , which required 24 hours to approve. When the notebook ran, it required a GPU instance . Users are not given any resource quotas for this by default and must request an increase manually, adding another 24-hour delay. This assumes they are eligible for such increases at all (one of us was not, and only found a workaround after contacting an AWS representative). Some of this may have been due to the fact that we were using a relatively new AWS account. But great software has to work for new users as well as veterans if it hopes to grow and this is what we set out to test. Great software should also work for users who do not have the luxury of a contact at AWS. Our experience is well-summarized by Jesse Anderson, author of Data Teams . He told us that “AWS’s intent is to offload data engineer tasks to make them more doable for the data scientists. It lowers the bar somewhat but it isn’t a huge shift. There’s still a sizable amount of data engineering needed just to get something ready for SageMaker.” To be fair to AWS, service quotas are useful in helping control cloud costs, particularly in a large enterprise setting where a CIO might want to enable the rank-and-file to request the services they need without incurring an enormous bill. Yet, one could easily imagine a better world. At a minimum, AWS-related error messages (e.g. resource limit constraints) should come with links to details on how to fix them rather than making users spend time hunting through console pages. For example, GCloud Firebase , which has a similar service quotas, does this well. Even better, it would be nice if there were single-click buttons that immediately granted account owners a single instance for 24 hours so users don’t have to wait for human approval. In the end, we expected a more straightforward interface. We’ve seen some tremendous improvements over last year, but AWS is still leaving a lot on the table. Data Wrangler: right problem, wrong approach There’s a now-old trope ( immortalized by Big Data Borat ) that data scientists spend 80% of their time cleaning and preparing data: Industry leaders recognize the importance of tackling this problem well. As Ozan Unlu , Founder and CEO of automated observability startup Edge Delta explained to us, “allowing data scientists to more efficiently surpass the early stages of the project allows them to spend a much larger proportion of their time on significantly higher value additive tasks.” Indeed, one of us previously wrote an article called The Unreasonable Importance of Data Preparation , clarifying the need to automate parts of the data preparation process. SageMaker Studio’s Data Wrangler claims to “provide the fastest and easiest way for developers to prepare data for machine learning” and comes packed with exciting features, including: 300+ data transformation features (including one-hot encoders, which are table stakes for machine learning), the ability to hand-code your own transformations, and upcoming integrations with Snowflake, MongoDB, and Databricks. Users are also able to output their results and workflows to a variety of formats like SageMaker pipelines (more on this in Part 2), Jupyter notebooks, or a Feature Store (we’ll get to this in Part 2 as well). However, we’re not convinced that most developers or data scientists would find it very useful yet. First off, it’s GUI-based, and the vast majority of data scientists will avoid GUIs like the plague. There are several reasons for this, perhaps the most important being that GUIs are antithetical to reproducible data work. Hadley Wickham, Chief Scientist at RStudio and author of the principles of tidy data , has even given a talk entitled “ You can’t do data science in a GUI .” To be fair to SageMaker, you can export your workflow as Python code, which will help alleviate reproducibility to a certain extent. This approach follows in the footsteps of products such as Looker (acquired last year by Google for $2.6 billion! ), which generates SQL code based upon user interactions with a drag and drop interface. But it will probably not appeal to developers or data scientists (if you can already express your ideas in code, why learn to use someone else’s GUI?). There may be some value in enabling non-technical domain experts (who are presumably less expensive talent resources) to transform data and export the process to code. However, the code generated from recording an iterative exploratory GUI session may not be very clean and could require significant engineering or data scientist intervention. Much of the future of data work will occur in GUIs and drag-and-drop interfaces, but this will be the long tail of data work and not that of developers and data scientists. Data Wrangler’s abstraction away from code and the abstraction over many other parts of the data preparation workflow are also concerning. Take the “quick model” feature which, according to AWS Evangelist Julien Simon , “immediately trains a model on the pre-processed data,” shows “the impact of your data preparation steps,” and provides insight into feature importance. When building this quick model, it isn’t clear what kind of model is actually trained, so it’s not obvious how any insight could be developed here or whether the “important features” are important at all. Most troubling is Data Wrangler’s claim to be providing insight into your data and your model without using any form of domain expertise at all. This is in stark contrast to tools such as Snorkel , a project that aims to “inject domain information [or heuristics] into machine learning models in higher-level, higher-bandwidth ways.” This lack of input is particularly worrisome in an era rife with AI bias issues . One key aspect of the future of data tooling is forming the connective tissue between data science workflows and domain experts, but the abstractions Data Wrangler presents seem to be moving us in the opposite direction. We’ll get to this in more detail when discussing Clarify, the SageMaker Studio tool that “detects bias in ML models.” So far, we’ve seen some wins and some misses for AWS. The apparent better integration with Cloud Formation Stacks is a real win for usability I hope we see more of this from AWS. On the other hand, the steep learning curve and the UX shortcomings are still obstacles to data scientists looking to use the environment. This is born out in usage numbers: A 2020 Kaggle survey puts SageMaker usage among data scientists at 16.5%, even though overall AWS usage is 48.2% (mostly through direct access to EC2). For reference, JupyterLab usage is at 74.1%, and Scikit-learn at 82.8%. Surprisingly, this may be an area of strength for GCloud . While Google’s cloud service holds an embarrassing third-place ranking overall (behind Microsoft Azure and AWS), it holds a strong second place for data scientists according to the Kaggle Survey. Products like Google Colab , which only offer a fraction of the functionality of AWS SageMaker, are very good at what they do and have attracted some devoted fans in the data science community. Perhaps Google’s notorious engineering-first culture has translated into a more user-friendly experience in the cloud than its Seattle-based rival. We have certainly noticed that the documentation is kept a little better in sync and that the developer experience is a little sharper. As we mentioned last year , user-centric design will be key in winning the cloud race, and while Sagemaker has made significant strides in that direction, it still has a ways to go. Join us in part 2 , where we talk about Pipelines, Feature Store, Clarify, and the ML industry’s darker parts. Tianhui Michael Li is president at Pragmatic Institute and the founder and president of The Data Incubator , a data science training and placement firm. Previously, he headed monetization data science at Foursquare and has worked at Google, Andreessen Horowitz, J.P. Morgan, and D.E. Shaw. Hugo Bowne-Anderson is Head of Data Science Evangelism and VP of Marketing at Coiled . Previously, he was a data scientist at DataCamp , and has taught data science topics at Yale University and Cold Spring Harbor Laboratory, conferences such as SciPy, PyCon, and ODSC, and with organizations such as Data Carpentry. [Full Disclosure: As part of its services, Coiled provisions and manages cloud resources to scale Python code for data scientists, and so does offer something that SageMaker also does as part of its services. But it’s also true that all-one-platforms such as SageMaker and products such as Coiled can be seen as complementary: Coiled has several customers who use SageMaker Studio alongside Coiled. ]  VentureBeat VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact. Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access: up-to-date information on the subjects of interest to you our newsletters

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Clarify Frequently Asked Questions (FAQ)

  • When was Clarify founded?

    Clarify was founded in 2014.

  • Where is Clarify's headquarters?

    Clarify's headquarters is located at 501 Pedernales Street, Austin.

  • What is Clarify's latest funding round?

    Clarify's latest funding round is Incubator/Accelerator - III.

  • How much did Clarify raise?

    Clarify raised a total of $1.41M.

  • Who are the investors of Clarify?

    Investors of Clarify include Plug and Play Accelerator, Microsoft ScaleUp, Brett Hurt, Sam Decker, Playfair Capital and 10 more.

  • Who are Clarify's competitors?

    Competitors of Clarify include Deepgram.

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