Remove outdated TensorBoard and Pachyderm references from README.md

Author: robmarkcole

README.md

@@ -26,7 +26,6 @@
 * [Leveraging Geolocation Data for Machine Learning: Essential Techniques](https://towardsdatascience.com/leveraging-geolocation-data-for-machine-learning-essential-techniques-192ce3a969bc) -> A Gentle Guide to Feature Engineering and Visualization with Geospatial data, in Plain English
 * [Image Classification Labeling: Single Class versus Multiple Class Projects](https://www.azavea.com/blog/2020/06/08/image-classification-labeling-single-class-versus-multiple-class-projects/)
 * [Image Augmentations for Aerial Datasets](https://blog.roboflow.com/image-augmentations-for-aerial-datasets/)
-* [Using TensorBoard While Training Land Cover Models with Satellite Imagery](https://up42.com/blog/tech/using-tensorboard-while-training-land-cover-models-with-satellite-imagery)
 * [Visualise Embeddings with Tensorboard](https://medium.com/gsi-technology/visualising-embeddings-using-t-sne-8fd4e31b56e2) -> also checkout the [Tensorflow Embedding Projector](https://projector.tensorflow.org/)
 * [Introduction to Satellite Image Augmentation with Generative Adversarial Networks - video](https://geoawesomeness.com/introduction-to-satellite-image-augmentation-with-generative-adversarial-networks/)
 * [Use Gradio and W&B together to monitor training and view predictions](https://wandb.ai/abidlabs/your-test-project/reports/How-Gradio-and-W-B-Work-Beautifully-Together---Vmlldzo4MTk0MzI)
@@ -153,7 +152,6 @@ Once your model is deployed you will want to monitor for data errors, broken pip
 * [geo-ml-model-catalog](https://github.com/radiantearth/geo-ml-model-catalog) -> provides a common metadata definition for ML models that operate on geospatial data
 * [hummingbird](https://github.com/microsoft/hummingbird) ->  a library for compiling trained traditional ML models into tensor computations, e.g. scikit learn model to pytorch for fast inference on a GPU
 * [deepchecks](https://github.com/deepchecks/deepchecks) -> Deepchecks is a Python package for comprehensively validating your machine learning models and data with minimal effort
-* [pachyderm](https://www.pachyderm.com/) -> Data Versioning and Pipelines for MLOps. Read [Pachyderm + Label Studio](https://medium.com/pachyderm-data/pachyderm-label-studio-ecc09f1f9329) which discusses versioning and lineage of data annotations
 
 # AWS
 * Host your data on [S3](https://aws.amazon.com/s3/) and metadata in a db such as [postgres](https://aws.amazon.com/rds/postgresql/)