neptune.ai @neptune_ai Twitter profile

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neptune.ai

2 months

A 2-minute demo showcasing how supports teams that train foundation models. Haven't heard about Neptune before? TL;DR: It's an experiment tracker built to support teams that train large-scale models. Neptune allows you to: → Monitor and

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neptune.ai

@neptune_ai

5 years

We have a treat for all @PyTorch users out there! "8 Creators and Core Contributors Talk About Their Model Training Libraries From PyTorch Ecosystem" First-hand info about: - Philosophy - Build-in features - Extension capabilities - and way more

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neptune.ai

@neptune_ai

5 years

Just updated the @fastdotai integration with @NeptuneML so that you can have your experiments tracked, hosted and ready to share with others. Check out the blog post about it

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neptune.ai

@neptune_ai

5 years

Track metrics/hyperparameters/code of @fastdotai experiments All it takes is one callback. This @Medium post explains how #DataScience #MachineLearning

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neptune.ai

@neptune_ai

3 years

Want to understand with code how to build #BERT ? Check this article! @nielspace07 uses @pytorch and breaks the process into 4 sections: - Preprocessing - Building model - Loss and Optimization - Training

How to Code BERT Using PyTorch - Tutorial With Examples

Guide on BERT coding in PyTorch, focusing on understanding BERT, its significance, and pre-trained model utilization.

neptune.ai

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neptune.ai

@neptune_ai

3 years

If you want to know what are the components of #BERT , and dive into the code - check this article.

How to Code BERT Using PyTorch - Tutorial With Examples

Guide on BERT coding in PyTorch, focusing on understanding BERT, its significance, and pre-trained model utilization.

neptune.ai

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neptune.ai

@neptune_ai

2 years

There are 2 types of #ML engineers: Task ML engineer and Platform ML engineer @sh_reya explains in “Thoughts on #MLEngineering After a Year of my PhD”

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neptune.ai

@neptune_ai

4 years

Good news! We integrated Neptune with another awesome library - Keras Tuner. You can now: -see charts of logged metrics -see the parameters tried -log hyperparameter search space And more! Docs 👉 Thanks @fchollet and the team for this great library 🙏

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neptune.ai

@neptune_ai

5 years

#ToolAlert dabl: an awesome library by @amuellerml that reduces boilerplate when creating baseline ML solutions. With (dabl.) clean, plot, AnyClassifier, and explain you can do pretty much everything you need with a one-liner. Check it out:

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neptune.ai

@neptune_ai

4 years

If you're training deep learning models, @fastdotai should definitely be your tool. And if you want to additionally monitor your training (believe us, you should want that), try Neptune-fastai integration. It's just one additional callback. Docs:

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neptune.ai

@neptune_ai

3 years

Before creating a model, it’s a good idea to ask ourselves: “Can this problem be solved without #ML ?” Yes? Then head on over to this article 👇

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neptune.ai

@neptune_ai

2 years

The secret key is the interoperability of the components. That is the difference between success and frustration when building an #MLOps platform. In the classic debate between build, in-house vs buy best-of-breed, the answer (when you cut the fluff out) is almost always both.

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neptune.ai

@neptune_ai

2 years

#MLOps stack at companies doing #ML at a reasonable scale. How do they choose their tools? Veeeeery pragmatically. First thing is to understand what you actually need. Which part of the stack you need to do well and which part not so much.

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neptune.ai

@neptune_ai

2 years

Dear practical, pragmatic #ML folks out there. If there is one conference you should attend this year, it is probably @normconf 2022. Here is why:

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neptune.ai

@neptune_ai

4 years

Want to present your lib for #AI research at the @iclr_conf ? DM @kamil_k7k , co-host of the social event focused on tools and practices in #DeepLearning and join the ideas exchange forum!

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neptune.ai

@neptune_ai

2 years

A list of the best open-source resources to learn #MLOps by @jacopotagliabue : 1/ Made with ML by @GokuMohandas : 2/ Machine Learning Systems Design by @chipro : 3/ Part of NYU course by @jacopotagliabue :

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neptune.ai

@neptune_ai

3 years

Here's another amazing article by @cathalhoran on our blog. This time he wrote about: - how Transformer models learn context - how they are similar and different - if Masked Language Models perform better - BERT and its masking process implementation 👇

Unmasking BERT: The Key to Transformer Model Performance

If you’re reading this article, you probably know about Deep Learning Transformer models like BERT. They’re revolutionizing the way we do Natural Language Processing (NLP). In case you don’t know,...

neptune.ai

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neptune.ai

@neptune_ai

2 years

We figured that sometimes the lack of @huggingface integration was a bit of a blocker from using @neptune_ai . Well, it wasn’t difficult to figure out - a few people just told us that.

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neptune.ai

@neptune_ai

3 years

Our #BERT section on the blog is growing! We already published some theoretical articles, now it's time for a hands-on tutorial. @nielspace07 shows how to code BERT using @PyTorch

How to Code BERT Using PyTorch - Tutorial With Examples

Guide on BERT coding in PyTorch, focusing on understanding BERT, its significance, and pre-trained model utilization.

neptune.ai

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neptune.ai

@neptune_ai

2 years

Worth checking out ➡️ Build a News Article Summarizer App with Hugging Face and Gradio by @benthecoder1

Build a News Article Summarizer App with Hugging Face 🤗 and Gradio

Building a text summarizer with Newspaper3k, Hugging Face, and Gradio

medium.com

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neptune.ai

@neptune_ai

2 years

Worth checking 👉 Introduction to gradient boosting on decision trees with Catboost by @kidrulit

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neptune.ai

@neptune_ai

2 years

“Inevitably, the resident CTO arrives at a fruit bowl of cherries picked from open-source projects, vendor-supplied proprietary tools, and services from a cloud provider.” - @ciphr

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neptune.ai

@neptune_ai

2 years

Task ML engineer -decent understanding of infrastructure & really good of ML -responsible for sustaining a specific ML pipeline -concerned with specific models for business-critical tasks -paged when top-line metrics are falling -tasked with “fixing” something model-related

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neptune.ai

@neptune_ai

4 years

In this post, we explore how to build #ML apps with @streamlit , and give you a few examples. It doesn’t take long to start with Streamlit, since you don’t need any front-end web development experience, you script everything with #Python , so give it a go!

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neptune.ai

@neptune_ai

2 years

If you were to build a new #MLPlatform now, is there anything that you would do differently? Question from the AMA with the Netflix ML infra team. Answer? “...We would also try and integrate/build experiment tracking as a first class concern upfront.”

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neptune.ai

@neptune_ai

5 years

Learn how to optimize hyperparameters of lightGBM from MSFTResearch with Scikit-Optimize Kudos to @betatim @mechcoder @iaroslav_ai and others for this beautiful library! MachineLearning DataScience

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neptune.ai

@neptune_ai

2 years

3/ “I’m a big fan of buying what you can buy and building what you really really need to build. That makes you different. That is essential to your business.”

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neptune.ai

@neptune_ai

2 years

The solution can be a mix of: -tools built in-house -open-source -third-party SaaS or on-prem tools So depending on their use case, they may have sth as basic as bash scripts for most of their ML operations & get sth more advanced for one area where they need it. Examples 👇

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neptune.ai

@neptune_ai

2 years

1/ “The first thing you need to know is that you have a problem that can be solved using machine learning techniques and it’s worth solving yourself.”

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neptune.ai

@neptune_ai

5 years

Scikit-Optimize is a great library for hyperparameters optimization if used correctly. Read this library evaluation blog post on TDataScience post #MachineLearning #DataScience

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neptune.ai

@neptune_ai

2 years

Best people to follow to learn about #ML / #MLOps at a reasonable scale (and beyond) - @aesroka - @xLaszlo - Simon Stiebellehner - @electricweegie - @Dtourimmersions - @jacopotagliabue - @htahir111 - @adamvpro - @eugeneyan - @GokuMohandas - @svpino Who did we miss?

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neptune.ai

@neptune_ai

5 years

#ToolAlert gather - an awesome tool from @microsoft that lets you clean, restructure and even recover lost code in ProjectJupyter Lab. Check it out here: #DataScience #MachineLearning

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neptune.ai

@neptune_ai

2 years

9 #MLOps best practices taken from the interview by @kpolich (host at @DataSkeptic ) with @piotrniedzwiedz ⬇️

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neptune.ai

@neptune_ai

5 years

Things we like about Hyperopt: - Simple API - Nested search space - Distributed computation Things we don't like about Hyperopt: - documentation - documentation - visualization Read this blog post for more #DataScience #MachineLearning

Optuna vs Hyperopt: Which Hyperparameter Optimization Library Should You Choose?

Comparision of Optuna vs Hyperopt, evaluating ease of use, hyperparameters, documentation, visualizations, speed, and experimental outcomes.

neptune.ai

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neptune.ai

@neptune_ai

5 years

Neptune integrates with Optuna from @PreferredNetJP ! It is simple. 1. Add a callback: study.optimize(objective, n_trials=100, callbacks=[opt_utils.NeptuneMonitor()]) 2. Monitor your search results. 3. Done. #MachineLearning #DataScience #DeepLearning

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neptune.ai

@neptune_ai

2 years

Next Tuesday on MLOps Live, we’re again doubling the number of our guests. Silas Bempong and @abhijitramesh2k will join us to answer your questions about doing MLOps for clinical research studies.

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neptune.ai

@neptune_ai

2 years

What are the main components for implementing #MLOps in #NeuralSearch applications?

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neptune.ai

@neptune_ai

5 years

The first part of the "Hyperparameter optimization in python" blog series was just published on @TDataScience ! Part 1 evaluates the Scikit-Optimize library written by @betatim @mechcoder @iaroslav_ai and others! Read here #MachineLearning #DataScience

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neptune.ai

@neptune_ai

4 years

Awesome article by Johannes Schmidt about training your own object detector. 🙌 For training and experiment management he used @PyTorchLightnin and @neptune_ai .

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neptune.ai

@neptune_ai

3 years

Orchestration tools make the ML process easier, more efficient and help data scientists and ML teams focus on what’s necessary, rather than waste resources trying to identify priority issues. We review 13 of them ( @flyteorg , MLRun, @PrefectIO , and more).

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neptune.ai

@neptune_ai

4 years

We prepared another article with tips and tricks from #Kaggle competitions! This time we focused on #TabularData binary classification. Make sure to check it out if you want to master the competition!

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neptune.ai

@neptune_ai

5 years

Neptune logging was added to @PyTorchLightnin (lightweight @PyTorch wrapper) and it's so easy to use: trainer = Trainer(logger=NeptuneLogger(...)) Check how to use it: #MachineLearning #DeepLearning

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neptune.ai

@neptune_ai

5 years

#ToolAlert PlotNeuralNet - a tool for creating beautiful diagrams of your neural network architectures. #MachineLearning #DataScience

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neptune.ai

@neptune_ai

2 years

But when you combine tools from the MLOps ecosystem (open-source or vendor-supplied), you have new problems. You need to design for interoperability of modules that solve particular problems.

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neptune.ai

@neptune_ai

5 years

#ToolAlert PlotNeuralNet - a tool for creating beautiful diagrams of your neural network architectures. #MachineLearning #DataScience

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neptune.ai

@neptune_ai

5 years

#ToolAlert fastai-serving - a new library from @developmentseed for deploying fastdotai models into production. #DataScience #MachineLearning #DeepLearning

GitHub - developmentseed/fastai-serving: A Docker image for serving fast.ai models, mimicking the...

A Docker image for serving fast.ai models, mimicking the API of Tensorflow Serving - developmentseed/fastai-serving

github.com

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neptune.ai

@neptune_ai

4 years

Some time ago, inspired by the blog post by @Polly_zk and his package TeleGrad, we decided to write the @telegram bot for Neptune! You can use it to access experiment information. Check the docs here:

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neptune.ai

@neptune_ai

2 years

To succeed at doing #ML at a reasonable scale you need a mindset shift. There are 4 core pillars. @jacopotagliabue , @GreCo_CiRo , and @AndreaPolonioli explain.

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neptune.ai

@neptune_ai

3 years

This Is Our #MLOps Tool Stack: Continuum Industries by @continuum_ind 👇 @DVCorg @numpy_team @pandas_dev @matplotlib @github @kubernetesio @awscloud @raydistributed @neptune_ai

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neptune.ai

@neptune_ai

2 years

Read this cool article: Demystifying loss functions: cross entropy by @heisguyy

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neptune.ai

@neptune_ai

5 years

#ToolAlert PySyft - a framework for Differential Privacy and Federated Learning from @OpenMinedOrg . Check out or listen to @iamtrask speak about it on twimlai podcast.

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neptune.ai

@neptune_ai

2 years

We heard it a few times from potential Neptune users: “Overall, the product looks good and almost provides all we need - but unfortunately, it seems it doesn't support FBprophet, which is one of our used models.” Finally, we can say it’s not the case anymore!

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neptune.ai

@neptune_ai

3 years

We already have a post about the best #MLOps tools in general, but we got a lot of questions about #OpenSource tool stack. So here it is - top tools for various tasks, including: @MetaflowOSS #kedro CML from @DVCorg @h2oai AutoML @ApacheSpark and more!

Open Source MLOps: Platforms, Frameworks and Tools

Overview of essential open-source MLOps tools, focusing on their functionality and integration within the machine learning landscape.

neptune.ai

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neptune.ai

@neptune_ai

3 years

What are the differences between @XGBoostProject and LightGBM? Check this post to find out:

XGBoost vs LightGBM: How Are They Different

Learn about the structural differences, feature methods, and trade-offs between XGBoost and LightGBM in machine learning.

neptune.ai

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neptune.ai

@neptune_ai

3 years

Worth reading: Object detection with 10 lines of code by @OlafenwaMoses

Object Detection with 10 lines of code

One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes…

towardsdatascience.com

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neptune.ai

@neptune_ai

4 years

Check out our new article, we compile tips and tricks from solutions of some of Kaggle’s top NLP competitions. We discuss dealing with larger datasets, small datasets and external data, text representations, modeling, evaluation, cross-validation and more!

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neptune.ai

@neptune_ai

5 years

#ToolAlert PlotNeuralNet - a tool for creating beautiful diagrams of your neural network architectures. MachineLearning DataScience

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neptune.ai

@neptune_ai

2 years

“Your app is great, checks most of the boxes for us, supports a ton of metadata types, solid API, but @huggingface is fundamental to our workflow. So, we really need this integration to be there.”

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neptune.ai

@neptune_ai

2 years

Top 2 books for learning #MLOps by @jacopotagliabue (father of reasonable scale MLOps): 1/ Effective Data Science Infrastructure by @vtuulos 2/ Designing Machine Learning Systems by @chipro What are your favorites?

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neptune.ai

@neptune_ai

5 years

Your #NeuralNetworks are not converging? Tried optimizing hyperparameters algorithmically? #MachineLearning #DataScience

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neptune.ai

@neptune_ai

2 years

Once an #MLteam gets to a certain number of experiments, it can be difficult to collaborate. Here’s how @hypefactors managed to solve this problem. Old vs new way of collaborating on #ModelDevelopment ↓

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neptune.ai

@neptune_ai

5 years

Scikit-Optimize is being actively maintained again! A new release is out and docs rebrand looks so cool :) Want to learn about Scikit-Optimize? Read our article that evaluates this lib based on API, speed, documentation, visualizations and more.

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neptune.ai

@neptune_ai

1 year

A team that understands what ML/AI Ops needs they ACTUALLY have. @continuum_ind They wanted:

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neptune.ai

@neptune_ai

2 years

What are the bottlenecks teams often encounter when setting up #MLOps & data engineering for neural search? Full MLOps Live episode with @jakubzavrel and Fernando Barrera: Youtube: Spotify: Apple Podcasts:

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neptune.ai

@neptune_ai

3 years

Today #DataScientists and developers run multiple parallel experiments that can get overwhelming even for large teams. How to compare them effectively? Prepared by @sam_techwriter 👏

How to Compare Machine Learning Models and Algorithms

Guide to comparing machine learning models and algorithms, focusing on the challenge of selection and parameters comparison.

neptune.ai

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neptune.ai

@neptune_ai

5 years

#ToolAlert PlotNeuralNet - a tool for creating beautiful diagrams of your neural network architectures. #MachineLearning #DataScience

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neptune.ai

@neptune_ai

4 years

Are there any #football fans here? If yes, here's an article for you. @Elishatofunmi shows how to build an automated #MachineLearning model that is able to track team players on the pitch, and help predict their moves.

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neptune.ai

@neptune_ai

5 years

Have you used Hyperopt to optimize parameters of your #MachineLearning Models? Read our opinionated blog post about the pros and cons of using it Do you agree with those points? #DataScience

Hyperparameter Optimization in Python. Part 2: Hyperopt.

In this blog series, I am comparing python HPO libraries. Before reading this post, I would highly advise that you read Part 0…

towardsdatascience.com

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neptune.ai

@neptune_ai

3 years

#MLOpsEngineers work closely with #DataScientists and #DataEngineers in the #DataScience Team from the start of the project so it’s always good to arm yourself with the best practices! 🔥

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neptune.ai

@neptune_ai

2 years

📣 New @neptune_ai feature alert: SERVICE ACCOUNTS We’ve got quite a few requests from #ML teams that: - integrate Neptune with their #cicdpipeline - want to avoid using personal tokens for automated processes So we introduced service accounts that should check these boxes.

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neptune.ai

@neptune_ai

2 years

What is the best way to keep track of your #ML experiment results (+ monitor #CICDpipelines )? Old way vs new way by @continuum_ind ➡️

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neptune.ai

@neptune_ai

3 months

We’ve just launched our interview series from the @aiDotEngineer ! Today’s spotlight: @itsSandraKublik , Developer Relations at @cohere , talks about the evolution of the #RAG based models and the biggest challenges in the RAG-based systems (and advanced RAG) that will remain

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neptune.ai

@neptune_ai

4 years

Learn how to apply #DataAugmentation to generate an image dataset, what the difference is between #ImageProcessing and #ComputerVision , what techniques are used in the #ML industry, and a lot more – all in this article by @aigehi .

What Image Processing Techniques Are Actually Used in the ML Industry?

Learn about practical image processing techniques in the ML industry and explore Keras-based tasks with code samples.

neptune.ai

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neptune.ai

@neptune_ai

2 years

Next Tuesday = Next MLOps Live Q&A session This time the episode will be focused on MLOps at a small scale. Our guest @duarteocarmo will answer your questions around how early-stage startups and small teams tackle MLOps.

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neptune.ai

@neptune_ai

2 years

1/ You port your models to native mobile apps. You probably don’t need model monitoring but may need advanced model packaging and deployment.

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neptune.ai

@neptune_ai

5 years

#ProTip Before you start doing any #MachineLearning setup a solid validation schema. Without reliable validation, you will never know if you are making any progress. Read this great example of setting up validation to a tricky problem

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neptune.ai

@neptune_ai

2 years

“1st thing you need to know is that you have a problem that can be solved using ML techniques & it’s worth solving yourself. I’m a big fan of buying what you can buy & building what you really really need to build. That makes you different. That is essential to your business.”

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neptune.ai

@neptune_ai

5 years

Get inspired about #machinelearning by reading about and following these top #ML influencers. This list will help you broaden your knowledge! Read about: @SchmidhuberAI @kaifulee @Ronald_vanLoon @lexfridman @KirkDBorne …and more in this article:

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neptune.ai

@neptune_ai

2 years

What are your expectations for the #ExperimentTracking tool? 4 criteria by @deepsense_ai : 1/ Easy onboarding 2/ Simplicity of the tool 3/ Convenient API 4/ Fast and accurate support

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neptune.ai

@neptune_ai

4 years

⚡️ @PyTorchLightnin is a great research framework that helps you organize your DL code and outsource development boilerplate. We like it a lot, so we had to integrate it with our tool. Anything that you can log to your Lightning module, you can have visualized in Neptune.

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neptune.ai

@neptune_ai

3 years

🥳Looking for a tool to store all your model metadata, including visualizations? Good news! We updated Neptune's integrations with viz libraries, to be in line with the new Neptune API! Check the docs 👉 Altair @vega_vis @bokeh @plotlygraphs @matplotlib

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neptune.ai

@neptune_ai

2 years

🔍 Get inspired: Self-Supervised Learning in Vision Transformers by @DavideCoccomini

Self-Supervised Learning in Vision Transformers

What is self-supervised learning and how has it been applied to Vision Transformers?

towardsdatascience.com

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neptune.ai

@neptune_ai

3 years

If you’re a #DataScientist or #MachineLearning engineer who knows #DevOps principles, read this article. Anton Morgunov shows how to apply them to ML workflows.

Why You Should Use Continuous Integration and Continuous Deployment in Your Machine Learning...

Distinguish DevOps from MLOps and navigate MLOps challenges for effective machine learning project management.

neptune.ai

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neptune.ai

@neptune_ai

3 years

With #MLOps still being a nascent field, it’s hard to find established best practices and #ModelDeployment examples to operationalize ML solutions. What are the most common challenges faced by ML engineers and their teams? Article by @nerdCyberArtist :

Model Deployment Challenges: 6 Lessons From 6 ML Engineers

Six industry experts share lessons on model deployment challenges, with a focus on MLOps, scalability, ooling, and more.

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neptune.ai

@neptune_ai

5 years

Neptune logging was added to @PyTorchLightnin (lightweight @PyTorch wrapper) and it's so easy to use: trainer = Trainer(logger=NeptuneLogger(...)) Check how to use it: #MachineLearning #DeepLearning

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@neptune_ai

5 years

Do you know when to use ROC AUC and when to go with Precision-Recall curve AUC for your classification models? Read our blog post: #MachineLearning #DataScience

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@neptune_ai

5 years

#ToolAlert HyperparameterHunter - a Python library for automated feature engineering and hyperparameter optimization. Check out this blog post: #MachineLearning #DataScience

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neptune.ai

@neptune_ai

5 years

#ToolAlert PySyft - a framework for Differential Privacy and Federated Learning from @OpenMinedOrg . Check out or listen to @iamtrask speak about it on twimlai podcast.

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neptune.ai

@neptune_ai

5 years

Do you know what beta in F-beta metric stands for and how you can use it to put more focus on recall or precision? Read our blog post: #MachineLearning #DataScience

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neptune.ai

@neptune_ai

4 years

The most effective way to understand data is to visualize it. And we really value all the tools that help with this task. #Altair is a great visualization library for #Python so we integrated it with Neptune to let you log interactive charts. More here:

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neptune.ai

@neptune_ai

4 years

Follow the greatest minds of #machinelearning and get inspired. We compiled a list of top #ML influencers to help you figure out who to learn from! Read about: @noamchomskyT @OriolVinyalsML @AndrewYNg @fabiomoioli @petitegeek …and others:

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neptune.ai

@neptune_ai

2 years

End-to-end ML workflow: 0 -> productionalized model (6 steps) 👇 Step 1 – POC Step 2 – Labelling Step 3 – Designing Step 4 – Optimizing Step 5 – Iterating Step 6 – Monitoring/improving 👇

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neptune.ai

@neptune_ai

3 years

Explore our #HyperparameterOptimization section on the blog: 👉 Hyperparameter Tuning in Python 👉 Best Tools for Model Tuning and Hyperparameter Optimization 👉 How to Track Hyperparameters of ML Models?

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neptune.ai

@neptune_ai

4 years

In this new article, @Elishatofunmi uses the #Pytennis environment to build a Model-Free and Model-Based #RL system + gives you some resources you can explore – check it out! 🎾🎾

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neptune.ai

@neptune_ai

2 years

Most companies are either not doing any production ML yet, or do it at a #Reasonablescale . Reasonable scale as in: - five ML people, - ten models, - millions of requests. Reasonable, demanding, but nothing crazy and hyperscale. Nothing like Google.

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neptune.ai

@neptune_ai

4 years

“I have been pleasantly surprised with how easy it was to set up Neptune in my PyTorch Lightning projects!” - user's feedback 😍 If you're a @PyTorchLightnin user and want to be positively surprised as well, check our integration docs 👉

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neptune.ai

@neptune_ai

5 years

Did you know that you can use Kolmogorov-Smirnov statistic to measure the performance of you binary classifier? Read our blog post: #MachineLearning #DataScience

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neptune.ai

@neptune_ai

4 years

Neptune + @bokeh = possibility to log interactive charts generated in bokeh (like confusion matrix or distribution) in Neptune. 😍 📈📉📊 Check the docs ➡️

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neptune.ai

@neptune_ai

2 years

Recommending ➡️ Evolution of data roles in 20 years and how it transformed me as ML Enginee by @kannanpoem

Evaluation of data roles in 20 years and how it transformed me as ML Engineer

I believe that there would be many Generation Y data people like me who sailing in the same boat and still keep exploring what to do next…

kannandreams.medium.com

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neptune.ai

@neptune_ai

5 years

#Resource Great tutorial on #MachineLearning model fairness: #DataScience

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neptune.ai

@neptune_ai

2 years

We’ve raised an $8M Series A to continue building for the ML community! @AlmazCapital led the round with participation from our existing investors: @btovPartners , #RheingauFounders , and #TDJPitangoVentures .

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