The profile is currenly unclaimed by the seller. All information is provided by CB Insights.

Founded Year

1993

Stage

Acquired | Acquired

Total Raised

$25.05M

Valuation

$0000 

About Sagemaker

Sagemaker was a Software (non-internet/mobile) company based in Fairfield, Connecticut. Sagemaker was acquired in 2001.

Sagemaker Headquarter Location

883 Black Rock Tpke 200

Fairfield, Connecticut, 06430,

United States

203-368-4888

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Latest Sagemaker News

Run automatic model tuning with Amazon SageMaker JumpStart | Amazon Web Services

May 13, 2022

| Amazon Web Services 6 minutes read In December 2020, AWS announced the general availability of Amazon SageMaker JumpStart , a capability of Amazon SageMaker that helps you quickly and easily get started with machine learning (ML). In March 2022, we also announced the support for APIs in JumpStart. JumpStart provides one-click fine-tuning and deployment of a wide variety of pre-trained models across popular ML tasks, as well as a selection of end-to-end solutions that solve common business problems. These features remove the heavy lifting from each step of the ML process, making it simpler to develop high-quality models and reducing time to deployment. In this post, we demonstrate how to run automatic model tuning with JumpStart. SageMaker automatic model tuning Traditionally, ML engineers implement a trial and error method to find the right set of hyperparameters. Trial and error involves running multiple jobs sequentially or in parallel while provisioning the resources needed to run the experiment. With SageMaker automatic model tuning , ML engineers and data scientists can offload the time-consuming task of optimizing their model and let SageMaker run the experimentation. SageMaker takes advantage of the elasticity of the AWS platform to efficiently and concurrently run multiple training simulations on a dataset and find the best hyperparameters for a model. SageMaker automatic model tuning finds the best version of a model by running many training jobs on your dataset using the algorithm and ranges of hyperparameters that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you choose. Automatic model tuning uses either a Bayesian (default) or a random search strategy to find the best values for hyperparameters. Bayesian search treats hyperparameter tuning like a regression problem. When choosing the best hyperparameters for the next training job, it considers everything that it knows about the problem so far and allows the algorithm to exploit the best-known results. In this post, we use the default Bayesian search strategy to demonstrate the steps involved in running automatic model tuning with JumpStart using the LightGBM model. JumpStart currently supports 10 example notebooks with automatic model tuning. It also supports four popular algorithms for tabular data modeling. The tasks and links to their sample notebooks are summarized in the following table. Task Retrieve the JumpStart pre-trained model and images container In this section, we choose the LightGBM classification model for fine-tuning. We use the ml.m5.xlarge instance type on which the model is run. We then retrieve the training Docker container, the training algorithm source, and the pre-trained model. See the following code: training_instance_type = "ml.m5.xlarge"# Retrieve the docker imagetrain_image_uri = image_uris.retrieve(    region=None,    framework=None,    model_id=train_model_id,    model_version=train_model_version,    image_scope=train_scope,    instance_type=training_instance_type,)# Retrieve the training scripttrain_source_uri = script_uris.retrieve(    model_id=train_model_id, model_version=train_model_version, script_scope=train_scope)# Retrieve the pre-trained model tarball to further fine-tunetrain_model_uri = model_uris.retrieve(    model_id=train_model_id, model_version=train_model_version, model_scope=train_scope) Set static hyperparameters We now retrieve the default hyperparameters for this LightGBM model, as preconfigured by JumpStart. We also override the num_boost_round hyperparameter with a custom value. # Retrieve the default hyper-parameters for fine-tuning the modelhyperparameters = hyperparameters.retrieve_default(    model_id=train_model_id, model_version=train_model_version)# [Optional] Override default hyperparameters with custom values Define the tunable hyperparameter ranges Next we define the hyperparameter ranges to be optimized by automatic model tuning . We define the hyperparameter name as expected by the model and then the ranges of values to be tried for this hyperparameter. Automatic model tuning draws samples (equal to the max_jobs parameter) from the space of hyperparameters, using a technique called Bayesian search. For each drawn hyperparameter sample, the tuner creates a training job to evaluate the model with that configuration. See the following code: hyperparameter_ranges =    "learning_rate": ContinuousParameter(1e-4, 1, scaling_type="Logarithmic"),    "num_boost_round": IntegerParameter(2, 30),    "early_stopping_rounds": IntegerParameter(2, 30),    "num_leaves": IntegerParameter(10, 50),    "feature_fraction": ContinuousParameter(0, 1),    "bagging_fraction": ContinuousParameter(0, 1),    "bagging_freq": IntegerParameter(1, 10),    "max_depth": IntegerParameter(5, 30),    "min_data_in_leaf": IntegerParameter(5, 50), Initialize the automatic model tuning We start by creating an Estimator object with all the required assets that define the training job, such as the pre-trained model, the training image, and the training script. We then define a HyperparameterTuner object to interact with SageMaker hyperparameter tuning APIs. The HyperparameterTuner accepts as parameters the Estimator object, the target metric based on which the best set of hyperparameters is decided, the total number of training jobs (max_jobs) to start for the hyperparameter tuning job, and the maximum parallel training jobs to run (max_parallel_jobs). Training jobs are run with the LightGBM algorithm, and the hyperparameter values that has the minimal mlogloss metric is chosen. For more information about configuring automatic model tuning, see Best Practices for Hyperparameter Tuning . # Create SageMaker Estimator instancetabular_estimator = Estimator(    role=aws_role,    image_uri=train_image_uri,    source_dir=train_source_uri,    model_uri=train_model_uri,    entry_point="transfer_learning.py",    instance_count=1,    instance_type=training_instance_type,    max_run=360000,    hyperparameters=hyperparameters,    output_path=s3_output_location,)tuner = HyperparameterTuner(    estimator=tabular_estimator,    objective_metric_name="multi_logloss",    hyperparameter_ranges=hyperparameter_ranges,    metric_definitions=["Name": "multi_logloss", "Regex": "multi_logloss: ([0-9\. ]+)"],    strategy="Bayesian",    max_jobs=10,    max_parallel_jobs=2,    objective_type="Minimize",    base_tuning_job_name=training_job_name,) In the preceding code, we tell the tuner to run at most 10 experiments (max_jobs) and only two concurrent experiments at a time (max_parallel_jobs). Both of these parameters keep your cost and training time under control. Run the tuning job To launch the SageMaker tuning job, we call the fit method of the hyperparameter tuner object and pass the Amazon Simple Storage Service (Amazon S3) path of the training data: tuner.fit("training": training_dataset_s3_path, logs=True) While automatic model tuning searches for the best hyperparameters, you can monitor their progress either on the SageMaker console or on Amazon CloudWatch . When training is complete, the best model’s fine-tuned artifacts are uploaded to the Amazon S3 output location specified in the training configuration. Deploy the best model to an endpoint When the tuning job is complete, the best model has been selected and stored in Amazon S3. We now can deploy that model by calling the deploy method of the HyperparameterTuner object and passing the needed parameters, such as the number of instances to be used for the created endpoint, their type, the image to be deployed, and the script to run: tuner.deploy(    initial_instance_count=1,    instance_type=inference_instance_type,    entry_point="inference.py",    image_uri=deploy_image_uri,    source_dir=deploy_source_uri,    endpoint_name=endpoint_name,    enable_network_isolation=True) We can now test the created endpoint by making inference requests. You can follow the rest of the process in the accompanying notebook .

  • When was Sagemaker founded?

    Sagemaker was founded in 1993.

  • Where is Sagemaker's headquarters?

    Sagemaker's headquarters is located at 883 Black Rock Tpke 200, Fairfield.

  • What is Sagemaker's latest funding round?

    Sagemaker's latest funding round is Acquired.

  • How much did Sagemaker raise?

    Sagemaker raised a total of $25.05M.

  • Who are the investors of Sagemaker?

    Investors of Sagemaker include Enivid, Actua Corporation, EnerTech Capital, Woodbrook Capital, GE Capital and 3 more.

  • Who are Sagemaker's competitors?

    Competitors of Sagemaker include Labelbox and 1 more.

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